diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..c29583c6b01185dad97f39d3e18437ba63dfaa4d
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1,36 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+examples/duck.jpg filter=lfs diff=lfs merge=lfs -text
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..2c8f13cffd40e28b808b974e456d3d52af4b4a99
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,170 @@
+checkpoints/
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+src/
+# C extensions
+*.so
+temp
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+# For a library or package, you might want to ignore these files since the code is
+# intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+# However, in case of collaboration, if having platform-specific dependencies or dependencies
+# having no cross-platform support, pipenv may install dependencies that don't work, or not
+# install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+# This is especially recommended for binary packages to ensure reproducibility, and is more
+# commonly ignored for libraries.
+# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+# in version control.
+# https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+# and can be added to the global gitignore or merged into this file. For a more nuclear
+# option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+/tmp
+/logs
+/*.json
+/*.jpg
+/*.ipynb
+
+GenAI-Arena-hf-logs/vote_log/*
\ No newline at end of file
diff --git a/.gitmodules b/.gitmodules
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/.idea/.gitignore b/.idea/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..13566b81b018ad684f3a35fee301741b2734c8f4
--- /dev/null
+++ b/.idea/.gitignore
@@ -0,0 +1,8 @@
+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml
diff --git a/.idea/GenAI-Arena.iml b/.idea/GenAI-Arena.iml
new file mode 100644
index 0000000000000000000000000000000000000000..60c1c77e4977a632de9ba2e9b2463c4465fea56e
--- /dev/null
+++ b/.idea/GenAI-Arena.iml
@@ -0,0 +1,15 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml
new file mode 100644
index 0000000000000000000000000000000000000000..105ce2da2d6447d11dfe32bfb846c3d5b199fc99
--- /dev/null
+++ b/.idea/inspectionProfiles/profiles_settings.xml
@@ -0,0 +1,6 @@
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
new file mode 100644
index 0000000000000000000000000000000000000000..2b2a0663cae3524b1775e9eec58231359ad104ba
--- /dev/null
+++ b/.idea/modules.xml
@@ -0,0 +1,8 @@
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
new file mode 100644
index 0000000000000000000000000000000000000000..35eb1ddfbbc029bcab630581847471d7f238ec53
--- /dev/null
+++ b/.idea/vcs.xml
@@ -0,0 +1,6 @@
+
+
+
+
+
+
\ No newline at end of file
diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..a1c5ea04c828c08b35e54b1c9f39e12b152f4692
--- /dev/null
+++ b/README.md
@@ -0,0 +1,44 @@
+---
+title: K-Sort Arena
+emoji: 📈
+colorFrom: purple
+colorTo: pink
+sdk: gradio
+sdk_version: 4.21.0
+app_file: app.py
+pinned: false
+license: mit
+tags:
+- arena
+- leaderboard
+short_description: Efficient Image/Video K-Sort Arena
+---
+
+## Installation
+
+- for cuda 11.8
+```bash
+conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
+pip3 install -U xformers --index-url https://download.pytorch.org/whl/cu118
+pip install -r requirements.txt
+```
+- for cuda 12.1
+```bash
+conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia
+pip install -r requirements.txt
+```
+
+## Start Hugging Face UI
+```bash
+python app.py
+```
+
+## Start Log server
+```bash
+uvicorn serve.log_server:app --reload --port 22005 --host 0.0.0.0
+```
+
+## Update leaderboard
+```bash
+cd arena_elo && bash update_leaderboard.sh
+```
\ No newline at end of file
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..93a59fbe87181d1748d59cec2e82aa2a836f60eb
--- /dev/null
+++ b/app.py
@@ -0,0 +1,59 @@
+import gradio as gr
+import os
+from serve.gradio_web import *
+from serve.leaderboard import build_leaderboard_tab
+from model.model_manager import ModelManager
+from pathlib import Path
+from serve.constants import SERVER_PORT, ROOT_PATH, ELO_RESULTS_DIR
+
+def build_combine_demo(models, elo_results_file, leaderboard_table_file):
+
+ with gr.Blocks(
+ title="Play with Open Vision Models",
+ theme=gr.themes.Default(),
+ css=block_css,
+ ) as demo:
+ with gr.Tabs() as tabs_combine:
+ with gr.Tab("Image Generation", id=0):
+ with gr.Tabs() as tabs_ig:
+ with gr.Tab("Generation Leaderboard", id=0):
+ build_leaderboard_tab(elo_results_file['t2i_generation'], leaderboard_table_file['t2i_generation'])
+
+ with gr.Tab("Generation Arena (battle)", id=1):
+ build_side_by_side_ui_anony(models)
+
+ return demo
+
+
+def load_elo_results(elo_results_dir):
+ from collections import defaultdict
+ elo_results_file = defaultdict(lambda: None)
+ leaderboard_table_file = defaultdict(lambda: None)
+ if elo_results_dir is not None:
+ elo_results_dir = Path(elo_results_dir)
+ elo_results_file = {}
+ leaderboard_table_file = {}
+ for file in elo_results_dir.glob('elo_results_*.pkl'):
+ if 't2i_generation' in file.name:
+ elo_results_file['t2i_generation'] = file
+ # else:
+ # raise ValueError(f"Unknown file name: {file.name}")
+ for file in elo_results_dir.glob('*_leaderboard.csv'):
+ if 't2i_generation' in file.name:
+ leaderboard_table_file['t2i_generation'] = file
+ # else:
+ # raise ValueError(f"Unknown file name: {file.name}")
+
+ return elo_results_file, leaderboard_table_file
+
+if __name__ == "__main__":
+ server_port = int(SERVER_PORT)
+ root_path = ROOT_PATH
+ elo_results_dir = ELO_RESULTS_DIR
+ models = ModelManager()
+
+ elo_results_file, leaderboard_table_file = load_elo_results(elo_results_dir)
+ demo = build_combine_demo(models, elo_results_file, leaderboard_table_file)
+ demo.queue(max_size=20).launch(server_port=server_port, root_path=ROOT_PATH, share=True)
+
+ # demo.launch(server_name="0.0.0.0", server_port=7860, root_path=ROOT_PATH)
\ No newline at end of file
diff --git a/arena_elo/LICENSE b/arena_elo/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..b66e214125a484c217def34e6cd30914d086336c
--- /dev/null
+++ b/arena_elo/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 WildVision-Bench
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/arena_elo/README.md b/arena_elo/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..9056ec3417001832dfb118bd13a47a5783c24a59
--- /dev/null
+++ b/arena_elo/README.md
@@ -0,0 +1,46 @@
+## Computing the Elo Ratings
+
+
+```bash
+apt-get -y install pkg-config
+pip install -r requirements.txt
+```
+
+
+### to update the leaderboard
+
+```bash
+export LOGDIR="/path/to/your/logdir"
+bash update_elo_rating.sh
+```
+
+### to inspect the leaderboard status
+```bash
+python -m elo_rating.inspect_elo_rating_pkl
+```
+
+### to inspect the collected data status and cost
+```bash
+export LOGDIR="/path/to/your/logdir"
+python -m elo_rating.inspect_cost
+```
+
+### to upload the battle data to hugging face🤗
+```bash
+export HUGGINGFACE_TOKEN="your_huggingface_token"
+bash get_latest_data.sh
+python -m elo_rating.upload_battle_data --repo_id "WildVision/wildvision-bench" --log_dir "./vision-arena-logs/"
+```
+
+### to upload the chat data to hugging face🤗
+```bash
+export HUGGINGFACE_TOKEN="your_huggingface_token"
+bash get_latest_data.sh
+python -m elo_rating.upload_chat_data --repo_id "WildVision/wildvision-bench" --log_dir "./vision-arena-logs/"
+```
+
+
+### to get the collected data
+```bash
+python -m
+
diff --git a/arena_elo/edition_model_info.json b/arena_elo/edition_model_info.json
new file mode 100644
index 0000000000000000000000000000000000000000..bef917ac558b9c9966546ee4ed966b504af0e036
--- /dev/null
+++ b/arena_elo/edition_model_info.json
@@ -0,0 +1,47 @@
+{
+ "CycleDiffusion": {
+ "Link": "https://github.com/ChenWu98/cycle-diffusion",
+ "License": "X11",
+ "Organization": "Carnegie Mellon University"
+ },
+ "PNP": {
+ "Link": "https://github.com/MichalGeyer/plug-and-play",
+ "License": "-",
+ "Organization": "Weizmann Institute of Science"
+ },
+ "InstructPix2Pix": {
+ "Link": "https://www.timothybrooks.com/instruct-pix2pix",
+ "License": "Copyright 2023 Timothy Brooks, Aleksander Holynski, Alexei A. Efros",
+ "Organization": "University of California, Berkeley"
+ },
+ "Pix2PixZero": {
+ "Link": "https://pix2pixzero.github.io",
+ "License": "MIT License",
+ "Organization": "Carnegie Mellon University, Adobe Research"
+ },
+ "MagicBrush": {
+ "Link": "https://osu-nlp-group.github.io/MagicBrush",
+ "License": "CC-BY-4.0",
+ "Organization": "The Ohio State University, University of Waterloo"
+ },
+ "Prompt2prompt": {
+ "Link": "https://prompt-to-prompt.github.io",
+ "License": "Apache-2.0",
+ "Organization": "Google, Tel Aviv University"
+ },
+ "SDEdit": {
+ "Link": "https://sde-image-editing.github.io",
+ "License": "MIT License",
+ "Organization": "Stanford University"
+ },
+ "CosXLEdit": {
+ "Link": "https://huggingface.co/spaces/multimodalart/cosxl",
+ "License": "cosxl-nc-community",
+ "Organization": "Stability AI"
+ },
+ "InfEdit": {
+ "Link": "https://huggingface.co/spaces/sled-umich/InfEdit",
+ "License": "Apache-2.0",
+ "Organization": "University of Michigan, University of California, Berkeley"
+ }
+}
\ No newline at end of file
diff --git a/arena_elo/elo_rating/__init__.py b/arena_elo/elo_rating/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/arena_elo/elo_rating/basic_stats.py b/arena_elo/elo_rating/basic_stats.py
new file mode 100644
index 0000000000000000000000000000000000000000..41527492b747a4d20dbf594f4e418286ed1876d5
--- /dev/null
+++ b/arena_elo/elo_rating/basic_stats.py
@@ -0,0 +1,227 @@
+import argparse
+import code
+import datetime
+import json
+import os
+from pytz import timezone
+import time
+
+import pandas as pd # pandas>=2.0.3
+import plotly.express as px
+import plotly.graph_objects as go
+from tqdm import tqdm
+
+NUM_SERVERS = 1
+LOG_ROOT_DIR = os.getenv("LOGDIR", None)
+if LOG_ROOT_DIR is None:
+ raise ValueError("LOGDIR environment variable not set, please set it by `export LOGDIR=...`")
+
+def get_log_files(max_num_files=None):
+ log_root = os.path.expanduser(LOG_ROOT_DIR)
+ filenames = []
+ if NUM_SERVERS == 1:
+ for filename in os.listdir(log_root):
+ if filename.endswith("-conv.json"):
+ filepath = f"{log_root}/{filename}"
+ name_tstamp_tuple = (filepath, os.path.getmtime(filepath))
+ filenames.append(name_tstamp_tuple)
+ else:
+ for i in range(NUM_SERVERS):
+ for filename in os.listdir(f"{log_root}/server{i}"):
+ if filename.endswith("-conv.json"):
+ filepath = f"{log_root}/server{i}/{filename}"
+ name_tstamp_tuple = (filepath, os.path.getmtime(filepath))
+ filenames.append(name_tstamp_tuple)
+ # sort by tstamp
+ filenames = sorted(filenames, key=lambda x: x[1])
+ filenames = [x[0] for x in filenames]
+
+ max_num_files = max_num_files or len(filenames)
+ filenames = filenames[-max_num_files:]
+ return filenames
+
+
+def load_log_files(filename):
+ data = []
+ for retry in range(5):
+ try:
+ lines = open(filename).readlines()
+ break
+ except FileNotFoundError:
+ time.sleep(2)
+
+ for l in lines:
+ row = json.loads(l)
+ data.append(
+ dict(
+ type=row["type"],
+ tstamp=row["tstamp"],
+ model=row.get("model", ""),
+ models=row.get("models", ["", ""]),
+ )
+ )
+ return data
+
+
+def load_log_files_parallel(log_files, num_threads=16):
+ data_all = []
+ from multiprocessing import Pool
+
+ with Pool(num_threads) as p:
+ ret_all = list(tqdm(p.imap(load_log_files, log_files), total=len(log_files)))
+ for ret in ret_all:
+ data_all.extend(ret)
+ return data_all
+
+
+def get_anony_vote_df(df):
+ anony_vote_df = df[
+ df["type"].isin(["leftvote", "rightvote", "tievote", "bothbad_vote"])
+ ]
+ anony_vote_df = anony_vote_df[anony_vote_df["models"].apply(lambda x: x[0] == "")]
+ return anony_vote_df
+
+
+def merge_counts(series, on, names):
+ ret = pd.merge(series[0], series[1], on=on)
+ for i in range(2, len(series)):
+ ret = pd.merge(ret, series[i], on=on)
+ ret = ret.reset_index()
+ old_names = list(ret.columns)[-len(series) :]
+ rename = {old_name: new_name for old_name, new_name in zip(old_names, names)}
+ ret = ret.rename(columns=rename)
+ return ret
+
+
+def report_basic_stats(log_files):
+ df_all = load_log_files_parallel(log_files)
+ df_all = pd.DataFrame(df_all)
+ now_t = df_all["tstamp"].max()
+ df_1_hour = df_all[df_all["tstamp"] > (now_t - 3600)]
+ df_1_day = df_all[df_all["tstamp"] > (now_t - 3600 * 24)]
+ anony_vote_df_all = get_anony_vote_df(df_all)
+
+ # Chat trends
+ chat_dates = [
+ datetime.datetime.fromtimestamp(x, tz=timezone("US/Pacific")).strftime(
+ "%Y-%m-%d"
+ )
+ for x in df_all[df_all["type"] == "chat"]["tstamp"]
+ ]
+ chat_dates_counts = pd.value_counts(chat_dates)
+ vote_dates = [
+ datetime.datetime.fromtimestamp(x, tz=timezone("US/Pacific")).strftime(
+ "%Y-%m-%d"
+ )
+ for x in anony_vote_df_all["tstamp"]
+ ]
+ vote_dates_counts = pd.value_counts(vote_dates)
+ chat_dates_bar = go.Figure(
+ data=[
+ go.Bar(
+ name="Anony. Vote",
+ x=vote_dates_counts.index,
+ y=vote_dates_counts,
+ text=[f"{val:.0f}" for val in vote_dates_counts],
+ textposition="auto",
+ ),
+ go.Bar(
+ name="Chat",
+ x=chat_dates_counts.index,
+ y=chat_dates_counts,
+ text=[f"{val:.0f}" for val in chat_dates_counts],
+ textposition="auto",
+ ),
+ ]
+ )
+ chat_dates_bar.update_layout(
+ barmode="stack",
+ xaxis_title="Dates",
+ yaxis_title="Count",
+ height=300,
+ width=1200,
+ )
+
+ # Model call counts
+ model_hist_all = df_all[df_all["type"] == "chat"]["model"].value_counts()
+ model_hist_1_day = df_1_day[df_1_day["type"] == "chat"]["model"].value_counts()
+ model_hist_1_hour = df_1_hour[df_1_hour["type"] == "chat"]["model"].value_counts()
+ model_hist = merge_counts(
+ [model_hist_all, model_hist_1_day, model_hist_1_hour],
+ on="model",
+ names=["All", "Last Day", "Last Hour"],
+ )
+ model_hist_md = model_hist.to_markdown(index=False, tablefmt="github")
+
+ # Action counts
+ action_hist_all = df_all["type"].value_counts()
+ action_hist_1_day = df_1_day["type"].value_counts()
+ action_hist_1_hour = df_1_hour["type"].value_counts()
+ action_hist = merge_counts(
+ [action_hist_all, action_hist_1_day, action_hist_1_hour],
+ on="type",
+ names=["All", "Last Day", "Last Hour"],
+ )
+ action_hist_md = action_hist.to_markdown(index=False, tablefmt="github")
+
+ # Anony vote counts
+ anony_vote_hist_all = anony_vote_df_all["type"].value_counts()
+ anony_vote_df_1_day = get_anony_vote_df(df_1_day)
+ anony_vote_hist_1_day = anony_vote_df_1_day["type"].value_counts()
+ # anony_vote_df_1_hour = get_anony_vote_df(df_1_hour)
+ # anony_vote_hist_1_hour = anony_vote_df_1_hour["type"].value_counts()
+ anony_vote_hist = merge_counts(
+ [anony_vote_hist_all, anony_vote_hist_1_day],
+ on="type",
+ names=["All", "Last Day"],
+ )
+ anony_vote_hist_md = anony_vote_hist.to_markdown(index=False, tablefmt="github")
+
+ # Last 24 hours
+ chat_1_day = df_1_day[df_1_day["type"] == "chat"]
+ num_chats_last_24_hours = []
+ base = df_1_day["tstamp"].min()
+ for i in range(24, 0, -1):
+ left = base + (i - 1) * 3600
+ right = base + i * 3600
+ num = ((chat_1_day["tstamp"] >= left) & (chat_1_day["tstamp"] < right)).sum()
+ num_chats_last_24_hours.append(num)
+ times = [
+ datetime.datetime.fromtimestamp(
+ base + i * 3600, tz=timezone("US/Pacific")
+ ).strftime("%Y-%m-%d %H:%M:%S %Z")
+ for i in range(24, 0, -1)
+ ]
+ last_24_hours_df = pd.DataFrame({"time": times, "value": num_chats_last_24_hours})
+ last_24_hours_md = last_24_hours_df.to_markdown(index=False, tablefmt="github")
+
+ # Last update datetime
+ last_updated_tstamp = now_t
+ last_updated_datetime = datetime.datetime.fromtimestamp(
+ last_updated_tstamp, tz=timezone("US/Pacific")
+ ).strftime("%Y-%m-%d %H:%M:%S %Z")
+
+ # code.interact(local=locals())
+
+ return {
+ "chat_dates_bar": chat_dates_bar,
+ "model_hist_md": model_hist_md,
+ "action_hist_md": action_hist_md,
+ "anony_vote_hist_md": anony_vote_hist_md,
+ "num_chats_last_24_hours": last_24_hours_md,
+ "last_updated_datetime": last_updated_datetime,
+ }
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--max-num-files", type=int)
+ args = parser.parse_args()
+
+ log_files = get_log_files(args.max_num_files)
+ basic_stats = report_basic_stats(log_files)
+
+ print(basic_stats["action_hist_md"] + "\n")
+ print(basic_stats["model_hist_md"] + "\n")
+ print(basic_stats["anony_vote_hist_md"] + "\n")
+ print(basic_stats["num_chats_last_24_hours"] + "\n")
diff --git a/arena_elo/elo_rating/clean_battle_data.py b/arena_elo/elo_rating/clean_battle_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..b806eb159a3222df49bb0bc2d8475e04a679c8dd
--- /dev/null
+++ b/arena_elo/elo_rating/clean_battle_data.py
@@ -0,0 +1,432 @@
+"""
+Clean chatbot arena battle log.
+
+Usage:
+python3 clean_battle_data.py --mode conv_release
+"""
+import argparse
+import datetime
+import json
+import os
+import sys
+from pytz import timezone
+import time
+import PIL
+from PIL import ImageFile
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+
+from tqdm import tqdm
+
+from .basic_stats import get_log_files, NUM_SERVERS, LOG_ROOT_DIR
+from .utils import detect_language, get_time_stamp_from_date
+
+VOTES = ["tievote", "leftvote", "rightvote", "bothbad_vote"]
+IDENTITY_WORDS = [
+ "vicuna",
+ "lmsys",
+ "koala",
+ "uc berkeley",
+ "open assistant",
+ "laion",
+ "chatglm",
+ "chatgpt",
+ "gpt-4",
+ "openai",
+ "anthropic",
+ "claude",
+ "bard",
+ "palm",
+ "lamda",
+ "google",
+ "llama",
+ "qianwan",
+ "alibaba",
+ "mistral",
+ "zhipu",
+ "KEG lab",
+ "01.AI",
+ "AI2",
+ "Tülu",
+ "Tulu",
+ "NETWORK ERROR DUE TO HIGH TRAFFIC. PLEASE REGENERATE OR REFRESH THIS PAGE.",
+ "$MODERATION$ YOUR INPUT VIOLATES OUR CONTENT MODERATION GUIDELINES.",
+ "API REQUEST ERROR. Please increase the number of max tokens.",
+ "**API REQUEST ERROR** Reason: The response was blocked.",
+ "**API REQUEST ERROR**",
+]
+
+for i in range(len(IDENTITY_WORDS)):
+ IDENTITY_WORDS[i] = IDENTITY_WORDS[i].lower()
+
+
+def remove_html(raw):
+ if raw.startswith("
"):
+ return raw[raw.find(": ") + 2 : -len("
\n")]
+ if raw.startswith("### Model A: ") or raw.startswith("### Model B: "):
+ return raw[13:]
+ return raw
+
+
+def to_openai_format(messages):
+ roles = ["user", "assistant"]
+ ret = []
+ for i, x in enumerate(messages):
+ ret.append({"role": roles[i % 2], "content": x[1]})
+ return ret
+
+
+def replace_model_name(old_name, tstamp):
+ replace_dict = {
+ "bard": "palm-2",
+ "claude-v1": "claude-1",
+ "claude-instant-v1": "claude-instant-1",
+ "oasst-sft-1-pythia-12b": "oasst-pythia-12b",
+ "claude-2": "claude-2.0",
+ "PlayGroundV2": "PlayGround V2",
+ "PlayGroundV2.5": "PlayGround V2.5",
+ }
+ if old_name in ["gpt-4", "gpt-3.5-turbo"]:
+ if tstamp > 1687849200:
+ return old_name + "-0613"
+ else:
+ return old_name + "-0314"
+ if old_name in replace_dict:
+ return replace_dict[old_name]
+ return old_name
+
+
+def read_file(filename):
+ data = []
+ for retry in range(5):
+ try:
+ # lines = open(filename).readlines()
+ for l in open(filename):
+ row = json.loads(l)
+ if row["type"] in VOTES:
+ data.append(row)
+ break
+ except FileNotFoundError:
+ time.sleep(2)
+ except json.JSONDecodeError:
+ print(f"Error in reading {filename}")
+ print(row)
+ exit(0)
+ return data
+
+
+def read_file_parallel(log_files, num_threads=16):
+ data_all = []
+ from multiprocessing import Pool
+
+ with Pool(num_threads) as p:
+ ret_all = list(tqdm(p.imap(read_file, log_files), total=len(log_files)))
+ for ret in ret_all:
+ data_all.extend(ret)
+ return data_all
+
+def load_image(image_path):
+ try:
+ return PIL.Image.open(image_path)
+ except:
+ return None
+
+def clean_battle_data(
+ log_files, exclude_model_names, ban_ip_list=None, sanitize_ip=False, mode="simple", task_name="image_editing"
+):
+ data = read_file_parallel(log_files, num_threads=16)
+
+ convert_type = {
+ "leftvote": "model_a",
+ "rightvote": "model_b",
+ "tievote": "tie",
+ "bothbad_vote": "tie (bothbad)",
+ }
+
+ all_models = set()
+ all_ips = dict()
+ ct_anony = 0
+ ct_invalid = 0
+ ct_leaked_identity = 0
+ ct_banned = 0
+ battles = []
+ for row in tqdm(data, desc="Cleaning"):
+ if row["models"][0] is None or row["models"][1] is None:
+ continue
+
+ # Resolve model names
+ models_public = [remove_html(row["models"][0]), remove_html(row["models"][1])]
+ if "model_name" in row["states"][0]:
+ models_hidden = [
+ row["states"][0]["model_name"],
+ row["states"][1]["model_name"],
+ ]
+ if models_hidden[0] is None:
+ models_hidden = models_public
+ else:
+ models_hidden = models_public
+
+ if (models_public[0] == "" and models_public[1] != "") or (
+ models_public[1] == "" and models_public[0] != ""
+ ):
+ ct_invalid += 1
+ continue
+
+ if models_public[0] == "" or models_public[0] == "Model A":
+ anony = True
+ models = models_hidden
+ ct_anony += 1
+ else:
+ anony = False
+ models = models_public
+ if not models_public == models_hidden:
+ ct_invalid += 1
+ continue
+
+ # # Detect langauge
+ # state = row["states"][0]
+ # if state["offset"] >= len(state["messages"]):
+ # ct_invalid += 1
+ # continue
+ # lang_code = detect_language(state["messages"][state["offset"]][1])
+
+ # # Drop conversations if the model names are leaked
+ # leaked_identity = False
+ # messages = ""
+ # for i in range(2):
+ # state = row["states"][i]
+ # for turn_idx, (role, msg) in enumerate(
+ # state["messages"][state["offset"] :]
+ # ):
+ # if msg:
+ # messages += msg.lower()
+ # for word in IDENTITY_WORDS:
+ # if word in messages:
+ # leaked_identity = True
+ # break
+
+ # if leaked_identity:
+ # ct_leaked_identity += 1
+ # continue
+
+ def preprocess_model_name(m):
+ if m == "Playground v2":
+ return 'playground_PlayGroundV2_generation'
+ if m == "Playground v2.5":
+ return 'playground_PlayGroundV2.5_generation'
+ return m
+ models = [preprocess_model_name(m) for m in models]
+
+ # Replace bard with palm
+ if task_name == "image_editing":
+ valid = True
+ for _model in models:
+ try:
+ platform, model_name, task = _model.split("_")
+ except ValueError:
+ print(f"Invalid model names: {_model}")
+ valid = False
+ break
+ if not (platform in ["playground", "imagenhub"] and task == "edition"):
+ valid = False
+ break
+ if not valid:
+ ct_invalid += 1
+ continue
+ for i, _model in enumerate(models):
+ platform, model_name, task = _model.split("_")
+ models[i] = model_name
+
+ # if not all(x.startswith("imagenhub_") and x.endswith("_edition") for x in models):
+ # # print(f"Invalid model names: {models}")
+ # ct_invalid += 1
+ # continue
+
+ # models = [x[len("imagenhub_"):-len("_edition")] for x in models]
+ elif task_name == "t2i_generation":
+ valid = True
+ for _model in models:
+ try:
+ platform, model_name, task = _model.split("_")
+ except ValueError:
+ print(f"Invalid model names: {_model}")
+ valid = False
+ break
+ if not (platform.lower() in ["playground", "imagenhub", 'fal'] and (task == "generation" or task == "text2image")):
+ valid = False
+ break
+ if not valid:
+ ct_invalid += 1
+ continue
+ for i, _model in enumerate(models):
+ platform, model_name, task = _model.split("_")
+ models[i] = model_name
+ # if not all("playground" in x.lower() or (x.startswith("imagenhub_") and x.endswith("_generation")) for x in models):
+ # print(f"Invalid model names: {models}")
+ # ct_invalid += 1
+ # continue
+ # models = [x[len("imagenhub_"):-len("_generation")] for x in models]
+ # for i, model_name in enumerate(models):
+ # mode
+ # if model_name.startswith("imagenhub_"):
+ # models[i] = model_name[len("imagenhub_"):-len("_generation")]
+
+ else:
+ raise ValueError(f"Invalid task_name: {task_name}")
+ models = [replace_model_name(m, row["tstamp"]) for m in models]
+
+ # Exclude certain models
+ if exclude_model_names and any(x in exclude_model_names for x in models):
+ ct_invalid += 1
+ continue
+
+ # if models[0] not in model_infos or models[1] not in model_infos:
+ # continue
+
+ # # Exclude votes before the starting date
+ # if model_infos and (model_infos[models[0]]["starting_from"] > row["tstamp"] or model_infos[models[1]]["starting_from"] > row["tstamp"]):
+ # print(f"Invalid vote before the valid starting date for {models[0]} and {models[1]}")
+ # ct_invalid += 1
+ # continue
+
+
+
+ if mode == "conv_release":
+ # assert the two images are the same
+ date = datetime.datetime.fromtimestamp(row["tstamp"], tz=timezone("US/Pacific")).strftime("%Y-%m-%d") # 2024-02-29
+ image_path_format = f"{LOG_ROOT_DIR}/{date}-convinput_images/input_image_"
+ image_path_0 = image_path_format + str(row["states"][0]["conv_id"]) + ".png"
+ image_path_1 = image_path_format + str(row["states"][1]["conv_id"]) + ".png"
+ if not os.path.exists(image_path_0) or not os.path.exists(image_path_1):
+ print(f"Image not found for {image_path_0} or {image_path_1}")
+ ct_invalid += 1
+ continue
+
+ image_0 = load_image(image_path_0)
+ image_1 = load_image(image_path_1)
+ if image_0 is None or image_1 is None:
+ print(f"Image not found for {image_path_0} or {image_path_1}")
+ ct_invalid += 1
+ continue
+ if image_0.tobytes() != image_1.tobytes():
+ print(f"Image not the same for {image_path_0} and {image_path_1}")
+ ct_invalid += 1
+ continue
+
+
+ question_id = row["states"][0]["conv_id"]
+ # conversation_a = to_openai_format(
+ # row["states"][0]["messages"][row["states"][0]["offset"] :]
+ # )
+ # conversation_b = to_openai_format(
+ # row["states"][1]["messages"][row["states"][1]["offset"] :]
+ # )
+
+ ip = row["ip"]
+ if ip not in all_ips:
+ all_ips[ip] = {"ip": ip, "count": 0, "sanitized_id": len(all_ips)}
+ all_ips[ip]["count"] += 1
+ if sanitize_ip:
+ user_id = f"arena_user_{all_ips[ip]['sanitized_id']}"
+ else:
+ user_id = f"{all_ips[ip]['ip']}"
+
+ if ban_ip_list is not None and ip in ban_ip_list:
+ ct_banned += 1
+ continue
+
+ # Save the results
+ battles.append(
+ dict(
+ question_id=question_id,
+ model_a=models[0],
+ model_b=models[1],
+ winner=convert_type[row["type"]],
+ judge=f"arena_user_{user_id}",
+ # conversation_a=conversation_a,
+ # conversation_b=conversation_b,
+ # turn=len(conversation_a) // 2,
+ anony=anony,
+ # language=lang_code,
+ tstamp=row["tstamp"],
+ )
+ )
+
+ all_models.update(models_hidden)
+ battles.sort(key=lambda x: x["tstamp"])
+ last_updated_tstamp = battles[-1]["tstamp"]
+
+ last_updated_datetime = datetime.datetime.fromtimestamp(
+ last_updated_tstamp, tz=timezone("US/Pacific")
+ ).strftime("%Y-%m-%d %H:%M:%S %Z")
+
+ print(
+ f"#votes: {len(data)}, #invalid votes: {ct_invalid}, "
+ f"#leaked_identity: {ct_leaked_identity} "
+ f"#banned: {ct_banned} "
+ )
+ print(f"#battles: {len(battles)}, #anony: {ct_anony}")
+ print(f"#models: {len(all_models)}, {all_models}")
+ print(f"last-updated: {last_updated_datetime}")
+
+ if ban_ip_list is not None:
+ for ban_ip in ban_ip_list:
+ if ban_ip in all_ips:
+ del all_ips[ban_ip]
+ print("Top 30 IPs:")
+ print(sorted(all_ips.values(), key=lambda x: x["count"], reverse=True)[:30])
+ return battles
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--max-num-files", type=int)
+ parser.add_argument(
+ "--mode", type=str, choices=["simple", "conv_release"], default="simple"
+ )
+ parser.add_argument("--task_name", type=str, default="image_editing", choices=["image_editing", "t2i_generation"])
+ parser.add_argument("--exclude-model-names", type=str, nargs="+")
+ parser.add_argument("--ban-ip-file", type=str)
+ parser.add_argument("--sanitize-ip", action="store_true", default=False)
+ args = parser.parse_args()
+
+ log_files = get_log_files(args.max_num_files)
+ ban_ip_list = json.load(open(args.ban_ip_file)) if args.ban_ip_file else None
+
+ battles = clean_battle_data(
+ log_files, args.exclude_model_names or [], ban_ip_list, args.sanitize_ip, args.mode, args.task_name
+ )
+ last_updated_tstamp = battles[-1]["tstamp"]
+ cutoff_date = datetime.datetime.fromtimestamp(
+ last_updated_tstamp, tz=timezone("US/Pacific")
+ ).strftime("%Y%m%d")
+
+ if args.mode == "simple":
+ for x in battles:
+ for key in [
+ "conversation_a",
+ "conversation_b",
+ "question_id",
+ ]:
+ if key in x:
+ del x[key]
+ print("Samples:")
+ for i in range(min(4, len(battles))):
+ print(battles[i])
+ output = f"clean_battle_{args.task_name}_{cutoff_date}.json"
+ elif args.mode == "conv_release":
+ # new_battles = []
+ # for x in battles:
+ # if not x["anony"]:
+ # continue
+ # for key in []:
+ # del x[key]
+ # new_battles.append(x)
+ # battles = new_battles
+ output = f"clean_battle_{args.task_name}_conv_{cutoff_date}.json"
+
+ with open(output, "w") as fout:
+ json.dump(battles, fout, indent=2, ensure_ascii=False)
+ print(f"Write cleaned data to {output}")
+
+ with open("cut_off_date.txt", "w") as fout:
+ fout.write(cutoff_date)
\ No newline at end of file
diff --git a/arena_elo/elo_rating/elo_analysis.py b/arena_elo/elo_rating/elo_analysis.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c5365cc8d9c84c1b0745adf226a3c682719fedd
--- /dev/null
+++ b/arena_elo/elo_rating/elo_analysis.py
@@ -0,0 +1,379 @@
+import argparse
+from collections import defaultdict
+import datetime
+import json
+import math
+import pickle
+from pytz import timezone
+
+import numpy as np
+import pandas as pd
+import plotly.express as px
+from tqdm import tqdm
+
+from .model_registry import get_model_info
+from .basic_stats import get_log_files
+from .clean_battle_data import clean_battle_data
+
+pd.options.display.float_format = "{:.2f}".format
+
+
+def compute_elo(battles, K=4, SCALE=400, BASE=10, INIT_RATING=1000):
+ rating = defaultdict(lambda: INIT_RATING)
+
+ for rd, model_a, model_b, winner in battles[
+ ["model_a", "model_b", "winner"]
+ ].itertuples():
+ ra = rating[model_a]
+ rb = rating[model_b]
+ ea = 1 / (1 + BASE ** ((rb - ra) / SCALE))
+ eb = 1 / (1 + BASE ** ((ra - rb) / SCALE))
+ if winner == "model_a":
+ sa = 1
+ elif winner == "model_b":
+ sa = 0
+ elif winner == "tie" or winner == "tie (bothbad)":
+ sa = 0.5
+ else:
+ raise Exception(f"unexpected vote {winner}")
+ rating[model_a] += K * (sa - ea)
+ rating[model_b] += K * (1 - sa - eb)
+
+ return dict(rating)
+
+
+def get_bootstrap_result(battles, func_compute_elo, num_round=1000):
+ rows = []
+ for i in tqdm(range(num_round), desc="bootstrap"):
+ tmp_battles = battles.sample(frac=1.0, replace=True)
+ rows.append(func_compute_elo(tmp_battles))
+ df = pd.DataFrame(rows)
+ return df[df.median().sort_values(ascending=False).index]
+
+
+def compute_elo_mle_with_tie(df, SCALE=400, BASE=10, INIT_RATING=1000):
+ from sklearn.linear_model import LogisticRegression
+
+ models = pd.concat([df["model_a"], df["model_b"]]).unique()
+ models = pd.Series(np.arange(len(models)), index=models)
+
+ # duplicate battles
+ df = pd.concat([df, df], ignore_index=True)
+ p = len(models.index)
+ n = df.shape[0]
+
+ X = np.zeros([n, p])
+ X[np.arange(n), models[df["model_a"]]] = +math.log(BASE)
+ X[np.arange(n), models[df["model_b"]]] = -math.log(BASE)
+
+ # one A win => two A win
+ Y = np.zeros(n)
+ Y[df["winner"] == "model_a"] = 1.0
+
+ # one tie => one A win + one B win
+ # find tie + tie (both bad) index
+ tie_idx = (df["winner"] == "tie") | (df["winner"] == "tie (bothbad)")
+ tie_idx[len(tie_idx) // 2 :] = False
+ Y[tie_idx] = 1.0
+
+ lr = LogisticRegression(fit_intercept=False)
+ lr.fit(X, Y)
+
+ elo_scores = SCALE * lr.coef_[0] + INIT_RATING
+ # calibrate llama-13b to 800 if applicable
+ if "llama-13b" in models.index:
+ elo_scores += 800 - elo_scores[models["llama-13b"]]
+ return pd.Series(elo_scores, index=models.index).sort_values(ascending=False)
+
+
+def get_median_elo_from_bootstrap(bootstrap_df):
+ median = dict(bootstrap_df.quantile(0.5))
+ median = {k: int(v + 0.5) for k, v in median.items()}
+ return median
+
+
+def compute_pairwise_win_fraction(battles, model_order, limit_show_number=None):
+ # Times each model wins as Model A
+ a_win_ptbl = pd.pivot_table(
+ battles[battles["winner"] == "model_a"],
+ index="model_a",
+ columns="model_b",
+ aggfunc="size",
+ fill_value=0,
+ )
+
+ # Table counting times each model wins as Model B
+ b_win_ptbl = pd.pivot_table(
+ battles[battles["winner"] == "model_b"],
+ index="model_a",
+ columns="model_b",
+ aggfunc="size",
+ fill_value=0,
+ )
+
+ # Table counting number of A-B pairs
+ num_battles_ptbl = pd.pivot_table(
+ battles, index="model_a", columns="model_b", aggfunc="size", fill_value=0
+ )
+
+ # Computing the proportion of wins for each model as A and as B
+ # against all other models
+ row_beats_col_freq = (a_win_ptbl + b_win_ptbl.T) / (
+ num_battles_ptbl + num_battles_ptbl.T
+ )
+
+ if model_order is None:
+ prop_wins = row_beats_col_freq.mean(axis=1).sort_values(ascending=False)
+ model_order = list(prop_wins.keys())
+
+ if limit_show_number is not None:
+ model_order = model_order[:limit_show_number]
+
+ # Arrange ordering according to proprition of wins
+ row_beats_col = row_beats_col_freq.loc[model_order, model_order]
+ return row_beats_col
+
+
+def visualize_leaderboard_table(rating):
+ models = list(rating.keys())
+ models.sort(key=lambda k: -rating[k])
+
+ emoji_dict = {
+ 1: "🥇",
+ 2: "🥈",
+ 3: "🥉",
+ }
+
+ md = ""
+ md += "| Rank | Model | Elo Rating | Description |\n"
+ md += "| --- | --- | --- | --- |\n"
+ for i, model in enumerate(models):
+ rank = i + 1
+ minfo = get_model_info(model)
+ emoji = emoji_dict.get(rank, "")
+ md += f"| {rank} | {emoji} [{model}]({minfo.link}) | {rating[model]:.0f} | {minfo.description} |\n"
+
+ return md
+
+
+def visualize_pairwise_win_fraction(battles, model_order):
+ row_beats_col = compute_pairwise_win_fraction(battles, model_order)
+ fig = px.imshow(
+ row_beats_col,
+ color_continuous_scale="RdBu",
+ text_auto=".2f",
+ height=700,
+ width=700,
+ )
+ fig.update_layout(
+ xaxis_title="Model B",
+ yaxis_title="Model A",
+ xaxis_side="top",
+ title_y=0.07,
+ title_x=0.5,
+ )
+ fig.update_traces(
+ hovertemplate="Model A: %{y}
Model B: %{x}
Fraction of A Wins: %{z}"
+ )
+
+ return fig
+
+
+def visualize_battle_count(battles, model_order):
+ ptbl = pd.pivot_table(
+ battles, index="model_a", columns="model_b", aggfunc="size", fill_value=0
+ )
+ battle_counts = ptbl + ptbl.T
+ fig = px.imshow(
+ battle_counts.loc[model_order, model_order],
+ text_auto=True,
+ height=700,
+ width=700,
+ )
+ fig.update_layout(
+ xaxis_title="Model B",
+ yaxis_title="Model A",
+ xaxis_side="top",
+ title_y=0.07,
+ title_x=0.5,
+ )
+ fig.update_traces(
+ hovertemplate="Model A: %{y}
Model B: %{x}
Count: %{z}"
+ )
+ return fig
+
+
+def visualize_average_win_rate(battles, limit_show_number):
+ row_beats_col_freq = compute_pairwise_win_fraction(
+ battles, None, limit_show_number=limit_show_number
+ )
+ fig = px.bar(
+ row_beats_col_freq.mean(axis=1).sort_values(ascending=False),
+ text_auto=".2f",
+ height=500,
+ width=700,
+ )
+ fig.update_layout(
+ yaxis_title="Average Win Rate", xaxis_title="Model", showlegend=False
+ )
+ return fig
+
+
+def visualize_bootstrap_elo_rating(df, df_final, limit_show_number):
+ bars = (
+ pd.DataFrame(
+ dict(
+ lower=df.quantile(0.025),
+ rating=df_final,
+ upper=df.quantile(0.975),
+ )
+ )
+ .reset_index(names="model")
+ .sort_values("rating", ascending=False)
+ )
+ bars = bars[:limit_show_number]
+ bars["error_y"] = bars["upper"] - bars["rating"]
+ bars["error_y_minus"] = bars["rating"] - bars["lower"]
+ bars["rating_rounded"] = np.round(bars["rating"], 2)
+ fig = px.scatter(
+ bars,
+ x="model",
+ y="rating",
+ error_y="error_y",
+ error_y_minus="error_y_minus",
+ text="rating_rounded",
+ height=500,
+ width=700,
+ )
+ fig.update_layout(xaxis_title="Model", yaxis_title="Rating")
+ return fig
+
+
+def report_elo_analysis_results(battles_json, rating_system="bt", num_bootstrap=100, anony_only=True):
+ battles = pd.DataFrame(battles_json)
+ battles = battles.sort_values(ascending=True, by=["tstamp"])
+ # Only use anonymous votes
+ if anony_only:
+ battles = battles[battles["anony"]].reset_index(drop=True)
+ battles_no_ties = battles[~battles["winner"].str.contains("tie")]
+
+ # Online update
+ elo_rating_online = compute_elo(battles)
+
+ if rating_system == "bt":
+ bootstrap_df = get_bootstrap_result(
+ battles, compute_elo_mle_with_tie, num_round=num_bootstrap
+ )
+ elo_rating_final = compute_elo_mle_with_tie(battles)
+ elif rating_system == "elo":
+ bootstrap_df = get_bootstrap_result(
+ battles, compute_elo, num_round=num_bootstrap
+ )
+ elo_rating_median = get_median_elo_from_bootstrap(bootstrap_df)
+ elo_rating_final = elo_rating_median
+
+ model_order = list(elo_rating_final.keys())
+ model_order.sort(key=lambda k: -elo_rating_final[k])
+
+ limit_show_number = 25 # limit show number to make plots smaller
+ model_order = model_order[:limit_show_number]
+
+ # leaderboard_table_df: elo rating, variance, 95% interval, number of battles
+ leaderboard_table_df = pd.DataFrame(
+ {
+ "rating": elo_rating_final,
+ "variance": bootstrap_df.var(),
+ "rating_q975": bootstrap_df.quantile(0.975),
+ "rating_q025": bootstrap_df.quantile(0.025),
+ "num_battles": battles["model_a"].value_counts()
+ + battles["model_b"].value_counts(),
+ }
+ )
+
+ # Plots
+ leaderboard_table = visualize_leaderboard_table(elo_rating_final)
+ win_fraction_heatmap = visualize_pairwise_win_fraction(battles_no_ties, model_order)
+ battle_count_heatmap = visualize_battle_count(battles_no_ties, model_order)
+ average_win_rate_bar = visualize_average_win_rate(
+ battles_no_ties, limit_show_number
+ )
+ bootstrap_elo_rating = visualize_bootstrap_elo_rating(
+ bootstrap_df, elo_rating_final, limit_show_number
+ )
+
+ last_updated_tstamp = battles["tstamp"].max()
+ last_updated_datetime = datetime.datetime.fromtimestamp(
+ last_updated_tstamp, tz=timezone("US/Pacific")
+ ).strftime("%Y-%m-%d %H:%M:%S %Z")
+
+ return {
+ "rating_system": rating_system,
+ "elo_rating_online": elo_rating_online,
+ "elo_rating_final": elo_rating_final,
+ "leaderboard_table": leaderboard_table,
+ "win_fraction_heatmap": win_fraction_heatmap,
+ "battle_count_heatmap": battle_count_heatmap,
+ "average_win_rate_bar": average_win_rate_bar,
+ "bootstrap_elo_rating": bootstrap_elo_rating,
+ "last_updated_datetime": last_updated_datetime,
+ "last_updated_tstamp": last_updated_tstamp,
+ "bootstrap_df": bootstrap_df,
+ "leaderboard_table_df": leaderboard_table_df,
+ }
+
+
+def pretty_print_elo_rating(rating):
+ model_order = list(rating.keys())
+ model_order.sort(key=lambda k: -rating[k])
+ for i, model in enumerate(model_order):
+ print(f"{i+1:2d}, {model:25s}, {rating[model]:.0f}")
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--clean-battle-file", type=str)
+ parser.add_argument("--max-num-files", type=int)
+ parser.add_argument("--num-bootstrap", type=int, default=100)
+ parser.add_argument(
+ "--rating-system", type=str, choices=["bt", "elo"], default="bt"
+ )
+ parser.add_argument("--exclude-tie", action="store_true", default=False)
+ args = parser.parse_args()
+
+ np.random.seed(42)
+
+ if args.clean_battle_file:
+ # Read data from a cleaned battle files
+ battles = pd.read_json(args.clean_battle_file)
+ else:
+ # Read data from all log files
+ log_files = get_log_files(args.max_num_files)
+ battles = clean_battle_data(log_files)
+
+ anony_results = report_elo_analysis_results(
+ battles, rating_system=args.rating_system, num_bootstrap=args.num_bootstrap, anony_only=True
+ )
+ full_results = report_elo_analysis_results(
+ battles, rating_system=args.rating_system, num_bootstrap=args.num_bootstrap, anony_only=False
+ )
+
+
+ print("# Online Elo")
+ pretty_print_elo_rating(anony_results["elo_rating_online"])
+ print("# Median")
+ pretty_print_elo_rating(anony_results["elo_rating_final"])
+ print(f"Annoy last update : {anony_results['last_updated_datetime']}")
+ print(f"Full last update : {full_results['last_updated_datetime']}")
+
+ last_updated_tstamp = full_results["last_updated_tstamp"]
+ cutoff_date = datetime.datetime.fromtimestamp(
+ last_updated_tstamp, tz=timezone("US/Pacific")
+ ).strftime("%Y%m%d")
+
+
+ results = {
+ "anony": anony_results,
+ "full": full_results,
+ }
+ with open(f"elo_results_{cutoff_date}.pkl", "wb") as fout:
+ pickle.dump(results, fout)
diff --git a/arena_elo/elo_rating/generate_leaderboard.py b/arena_elo/elo_rating/generate_leaderboard.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9f438d5d1664e921d48a57bede92aa071b96b91
--- /dev/null
+++ b/arena_elo/elo_rating/generate_leaderboard.py
@@ -0,0 +1,72 @@
+import fire
+import json
+import pandas as pd
+import pickle
+
+
+def main(
+ model_info_file: str,
+ elo_rating_pkl: str,
+ output_csv: str
+):
+ model_info = json.load(open(model_info_file))
+
+ with open(elo_rating_pkl, "rb") as fin:
+ elo_rating_results = pickle.load(fin)
+
+ anony_elo_rating_results = elo_rating_results["anony"]
+ full_elo_rating_results = elo_rating_results["full"]
+ anony_leaderboard_data = anony_elo_rating_results["leaderboard_table_df"]
+ full_leaderboard_data = full_elo_rating_results["leaderboard_table_df"]
+
+ # Model,MT-bench (score),Arena Elo rating,MMLU,License,Link
+ fields = ["key", "Model", "Arena Elo rating (anony)", "Arena Elo rating (full)", "License", "Organization", "Link"]
+ # set Organization and license to empty for now
+ all_models = anony_leaderboard_data.index.tolist()
+
+ for model in all_models:
+ if not model in model_info:
+ model_info[model] = {}
+ model_info[model]["License"] = "N/A"
+ model_info[model]["Organization"] = "N/A"
+ model_info[model]["Link"] = "N/A"
+ print(f"Model {model} not found in model_info.json")
+ model_info[model]["Model"] = model
+ model_info[model]["key"] = model
+
+ if model in anony_leaderboard_data.index:
+ model_info[model]["Arena Elo rating (anony)"] = anony_leaderboard_data.loc[model, "rating"]
+ else:
+ model_info[model]["Arena Elo rating (anony)"] = 0
+
+ if model in full_elo_rating_results["leaderboard_table_df"].index:
+ model_info[model]["Arena Elo rating (full)"] = full_leaderboard_data.loc[model, "rating"]
+ else:
+ model_info[model]["Arena Elo rating (full)"] = 0
+ # if model in anony_leaderboard_data.index:
+ # model_info[model]["Arena Elo rating"] = anony_leaderboard_data.loc[model, "rating"]
+ # else:
+ # model_info[model]["Arena Elo rating"] = 0
+
+ final_model_info = {}
+ for model in model_info:
+ if "Model" in model_info[model]:
+ final_model_info[model] = model_info[model]
+ model_info = final_model_info
+
+ exclude_keys = ['starting_from']
+ for key in exclude_keys:
+ for model in model_info:
+ if key in model_info[model]:
+ del model_info[model][key]
+ df = pd.DataFrame(model_info).T
+ df = df[fields]
+ # sort by anony rating
+ df = df.sort_values(by=["Arena Elo rating (anony)"], ascending=False)
+ df.to_csv(output_csv, index=False)
+ print("Leaderboard data saved to", output_csv)
+ print(df)
+
+
+if __name__ == "__main__":
+ fire.Fire(main)
\ No newline at end of file
diff --git a/arena_elo/elo_rating/inspect_conv_rating.py b/arena_elo/elo_rating/inspect_conv_rating.py
new file mode 100644
index 0000000000000000000000000000000000000000..7a69f0b0d2042112f0cc2323deee89b225663079
--- /dev/null
+++ b/arena_elo/elo_rating/inspect_conv_rating.py
@@ -0,0 +1,234 @@
+import argparse
+import code
+import datetime
+import json
+import os
+from pytz import timezone
+import time
+
+import pandas as pd
+from tqdm import tqdm
+import csv
+
+import base64
+from icecream import ic
+from openai import OpenAI
+
+# Function to encode the image
+def encode_image(image_path):
+ with open(image_path, "rb") as image_file:
+ return base64.b64encode(image_file.read()).decode('utf-8')
+
+def get_log_files(max_num_files=None):
+ dates = []
+ for month in [2, 3]:
+ for day in range(1, 32):
+ dates.append(f"2024-{month:02d}-{day:02d}")
+
+ num_servers = 1
+ filenames = []
+ for d in dates:
+ for i in range(num_servers):
+ # name = os.path.expanduser(f"~/fastchat_logs/server{i}/{d}-conv.json")
+ name = os.path.expanduser(f"vision-arena-logs/{d}-conv.json")
+ if os.path.exists(name):
+ filenames.append(name)
+ max_num_files = max_num_files or len(filenames)
+ filenames = filenames[-max_num_files:]
+ return filenames
+
+
+def pretty_print_conversation(messages):
+ for role, msg in messages:
+ print(f"[[{role}]]: {msg}")
+
+
+def get_gpt4v_response(client, img_bs64=None, text_prompt="", use_vision=False):
+ if use_vision:
+ response = client.chat.completions.create(
+ model="gpt-4-vision-preview",
+ messages=[
+ {
+ "role": "user",
+ "content": [
+ {"type": "text", "text": text_prompt},
+ {
+ "type": "image_url",
+ "image_url": {
+ "url": f"data:image/jpeg;base64,{img_bs64}"
+ }
+ },
+ ],
+ }
+ ],
+ max_tokens=100,
+ )
+ else:
+ response = client.chat.completions.create(
+ model="gpt-4-vision-preview",
+ messages=[
+ {
+ "role": "user",
+ "content": [
+ {"type": "text", "text": text_prompt},
+ ],
+ }
+ ],
+ max_tokens=100,
+ )
+ return response.choices[0].message.content
+
+task_template_map = {
+ "image_caption": "Give me the semantic alignment score between the given image and the given caption: \"{generated_sentence}\" on a scale of 0-100. Only reply the score value.",
+ "vqa": "Rate the answer correctness regarding the question within the context of the given image on a scale of 0-100. Only reply the score value.",
+ "pair_rate_old": "[Instruction]\n\"{instruction}\"\n\n\"{generated_sentence}\"\n\n[System]\nGiven the instruction and the image, please compare the correctness of responses A and B. Reply with \"leftvote\" if you find A better, \"rightvote\" if B is better, \"bothbad_vote\" if both responses are wrong, and \"tievote\" if both responses are equally satisfactory. If you are unable to make a decision, please reply with \"NA\".",
+ "pair_rate_wexplanation": "[Instruction]\n\"{instruction}\"\n\n\"{generated_sentence}\"[System]\nPlease act as an impartial judge and evaluate the quality of the responses provided by two AI assistants to the user question displayed below. You should choose the assistant that follows the user’s instructions and answers the user’s question better. Your evaluation should consider factors such as the helpfulness, relevance, accuracy, depth, creativity, and level of detail of their responses. Begin your evaluation by comparing the two responses and provide a short explanation. Avoid any positional biases and ensure that the order in which the responses were presented does not influence your decision. Do not allow the length of the responses to influence your evaluation. Do not favor certain names of the assistants. Be as objective as possible. After providing your explanation, output your final verdict by strictly following this format: \"[[A]]\" if assistant A is better, \"[[B]]\" if assistant B is better, and \"[[C]]\" for a tie.",
+ "pair_rate": "[Instruction]\n\"{instruction}\"\n\n\"{generated_sentence}\"\n\n[System]\nPlease act as an impartial judge and evaluate the quality of the responses provided by two AI assistants to the user question displayed below. You should choose the assistant that follows the user’s instructions and answers the user’s question better. Your evaluation should consider factors such as the helpfulness, relevance, accuracy, depth, creativity, and level of detail of their responses. Begin your evaluation by comparing the two responses and provide a short explanation. Avoid any positional biases and ensure that the order in which the responses were presented does not influence your decision. Do not allow the length of the responses to influence your evaluation. Do not favor certain names of the assistants. Be as objective as possible. Reply with \"leftvote\" if you find assistant A better, \"rightvote\" if assistant B is better, \"bothbad_vote\" if both responses are wrong, and \"tievote\" if both assistants provide equally satisfactory answers. If you are unable to make a decision, please reply with \"NA\"."
+}
+
+def inspect_convs(log_files):
+ ic(log_files)
+ data = []
+ total_vote = 0
+ correct_vote = 0
+
+ client = OpenAI()
+ with open('all_pairvote_log_wgpt_prtchatbot.csv', 'w', newline='') as csvfile:
+ # fieldnames = ['tstamp', 'type', 'model_1', 'model_2', 'template_name_1', 'template_name_2', 'system_message_1', 'system_message_2', 'role_1', 'role_2', 'instruction_1', 'instruction_2', 'message_1', 'message_2', 'offset_1', 'offset_2', 'conv_id_1', 'conv_id_2', 'model_name_1', 'model_name_2', 'ip']
+ fieldnames = ['tstamp', 'type', 'models', 'states', 'ip', 'gpt_vote']
+ writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
+
+ # Write the header
+ writer.writeheader()
+
+ for filename in tqdm(log_files, desc="read files"):
+ for retry in range(5):
+ try:
+ lines = open(filename).readlines()
+ break
+ except FileNotFoundError:
+ time.sleep(2)
+
+ for l in lines:
+ row = json.loads(l)
+
+ if "states" not in row:
+ continue
+ if row["type"] not in ["leftvote", "rightvote", "bothbad_vote", "tievote"]:
+ continue
+
+ model_names = row["states"][0]["model_name"], row["states"][1]["model_name"]
+
+
+ # Iterate through each state and write the relevant information
+ if not len(row["states"][0]['messages']): continue
+ # ic(row["states"][0]['messages'][1][1])
+
+ if row["states"][0]['messages'][1][1] is None or row["states"][1]['messages'][1][1] is None or "NETWORK ERROR" in row["states"][0]['messages'][1][1] or "NETWORK ERROR" in row["states"][1]['messages'][1][1]: continue
+ total_vote += 1
+ # row = {
+ # 'tstamp': row['tstamp'],
+ # 'type': row['type'],
+ # 'model_1': row['models'][0],
+ # 'model_2': row['models'][1],
+ # 'template_name_1': row["states"][0]['template_name'],
+ # 'system_message_1': row["states"][0]['system_message'],
+ # 'template_name_2': row["states"][1]['template_name'],
+ # 'system_message_2': row["states"][1]['system_message'],
+ # 'role_1': row["states"][0]['roles'],
+ # 'role_2': row["states"][1]['roles'],
+ # 'instruction_1': row["states"][0]['messages'][0][1],
+ # 'instruction_2': row["states"][1]['messages'][0][1],
+ # 'message_1': row["states"][0]['messages'][1][1],
+ # 'message_2': row["states"][1]['messages'][1][1],
+ # 'offset_1': row["states"][0]['offset'],
+ # 'offset_2': row["states"][1]['offset'],
+ # 'conv_id_1': row["states"][0]['conv_id'],
+ # 'conv_id_2': row["states"][1]['conv_id'],
+ # 'model_name_1': row["states"][0]['model_name'],
+ # 'model_name_2': row["states"][1]['model_name'],
+ # 'ip': row['ip']
+ # }
+ # writer.writerow(row)
+ # Convert complex objects to JSON strings
+ # TODO: check two image are the same
+ conv_id = row["states"][0]['conv_id']
+ image_path = os.path.join("/local/home/yujielu/project/Arena-Elo/vision-arena-logs", os.path.basename(filename)[:-5]+"input_images", f"input_image_{conv_id}.png")
+ if not os.path.exists(image_path):
+ response = "NA"
+ ic(image_path)
+ else:
+ base64_image = encode_image(image_path)
+ left_response = row["states"][0]['messages'][1][1]
+ right_response = row["states"][1]['messages'][1][1]
+ sep = "-" * 20
+ instruction = row["states"][0]['messages'][0][1]
+ generated_sentence = f"[The Start of Assistant A’s Answer]\n{left_response}\n[The End of Assistant A’s Answer]\n\n[The Start of Assistant B’s Answer]\n{right_response}\n[The End of Assistant B’s Answer]"
+ text_prompt = task_template_map["pair_rate"].format(instruction=instruction, generated_sentence=generated_sentence)
+ # ic(text_prompt)
+ try:
+ response = get_gpt4v_response(client, img_bs64=base64_image, text_prompt=text_prompt, use_vision=True)
+ except:
+ ic(">>> skip")
+ response = "NA"
+
+ # response = get_gpt4v_response(client, img_bs64=base64_image, text_prompt=text_prompt, use_vision=True)
+ ic(row['type'], response)
+ if response.strip() not in ["leftvote", "rightvote", "bothbad_vote", "tievote"]:
+ response = "NA"
+ # ic(generated_sentence)
+
+ # if row['type'] == "leftvote":
+ # row['type'] = "A"
+ # elif row['type'] == "rightvote":
+ # row['type'] = "B"
+ # elif row['type'] in ["bothbad_vote", "tievote"]:
+ # row['type'] = "C"
+ if row['type'] == response.strip():
+ correct_vote += 1
+ row['models'] = json.dumps(row['models'])
+ row['states'] = json.dumps(row['states'], ensure_ascii=False)
+ row['gpt_vote'] = response
+
+ # Write the modified row to the CSV file
+ writer.writerow(row)
+ # if row["type"] == "leftvote":
+ # winner, loser = model_names[0], model_names[1]
+ # winner_conv, loser_conv = row["states"][0], row["states"][1]
+ # elif row["type"] == "rightvote":
+ # loser, winner = model_names[0], model_names[1]
+ # loser_conv, winner_conv = row["states"][0], row["states"][1]
+
+ # if loser == "llava-v1.5-13b" and winner == "llava-v1.5-13b":
+ # print("=" * 20)
+ # print(f"Winner: {winner}")
+ # pretty_print_conversation(winner_conv["messages"])
+ # print(f"Loser: {loser}")
+ # pretty_print_conversation(loser_conv["messages"])
+ # print("=" * 20)
+ # input()
+ # if row['type'] == 'bothbad_vote':
+ # from icecream import ic
+ # ic(model_names)
+ # if row["type"] == "bothbad_vote" and "gpt-4-vision-preview" in model_names:
+ # print("=" * 20)
+ # print(f"Model A: {model_names[0]}")
+ # pretty_print_conversation(row["states"][0]["messages"])
+ # print(f"Model B: {model_names[1]}")
+ # pretty_print_conversation(row["states"][1]["messages"])
+ # print("=" * 20)
+ # input()
+ # if correct_vote >= 300: break
+ ic(total_vote, correct_vote)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--max-num-files", type=int)
+ args = parser.parse_args()
+
+ log_files = get_log_files(args.max_num_files)
+
+
+
+ inspect_convs(log_files)
diff --git a/arena_elo/elo_rating/inspect_cost.py b/arena_elo/elo_rating/inspect_cost.py
new file mode 100644
index 0000000000000000000000000000000000000000..d071ca3ac4e9d7eb8e0559bb7e8fa50b1799e6cf
--- /dev/null
+++ b/arena_elo/elo_rating/inspect_cost.py
@@ -0,0 +1,177 @@
+import fire
+import time
+import json
+from collections import defaultdict
+from .basic_stats import get_log_files, NUM_SERVERS, LOG_ROOT_DIR
+from .utils import detect_language, get_time_stamp_from_date, get_input_image_path, load_image_from_path
+from tqdm import tqdm
+VOTES = ["tievote", "leftvote", "rightvote", "bothbad_vote", "chat"]
+
+
+def remove_html(raw):
+ if raw.startswith(""):
+ return raw[raw.find(": ") + 2 : -len("
\n")]
+ if raw.startswith("### Model A: ") or raw.startswith("### Model B: "):
+ return raw[13:]
+ return raw
+
+
+def read_file(filename):
+ data = []
+ for retry in range(5):
+ try:
+ # lines = open(filename).readlines()
+ for l in open(filename):
+ row = json.loads(l)
+ if row["type"] in VOTES:
+ data.append(row)
+ break
+ except FileNotFoundError:
+ time.sleep(2)
+ return data
+
+
+def read_file_parallel(log_files, num_threads=16):
+ data_all = []
+ from multiprocessing import Pool
+
+ with Pool(num_threads) as p:
+ ret_all = list(tqdm(p.imap(read_file, log_files), total=len(log_files)))
+ for ret in ret_all:
+ data_all.extend(ret)
+ return data_all
+
+def num_tokens(s:str):
+ if s is None:
+ return 0
+ return len(s) / 4
+
+def main(
+):
+ log_files = get_log_files()
+ data = read_file_parallel(log_files)
+
+ all_model_counts = defaultdict(int)
+ all_model_input_tokens_counts = defaultdict(list)
+ all_model_output_tokens_counts = defaultdict(list)
+ all_model_image_sizes = defaultdict(list)
+ chat_battle_counts = defaultdict(int)
+ for row in tqdm(data, desc="counting"):
+ if row['type'] == "chat":
+ chat_battle_counts["chat"] += 1
+ all_model_counts[row['model']] += 1
+ tstamp = row["tstamp"]
+ conv_id = row["state"]["conv_id"]
+
+ image = load_image_from_path(get_input_image_path(tstamp, conv_id))
+ if image is None:
+ image_size = None
+ else:
+ image_size = load_image_from_path(get_input_image_path(tstamp, conv_id)).size
+ all_model_image_sizes[row['model']].append(image_size)
+ try:
+ for message in row["state"]["messages"][row["state"]["offset"] :: 2]:
+ all_model_input_tokens_counts[row['model']].append(num_tokens(message[1]))
+ for message in row["state"]["messages"][row["state"]["offset"] + 1 :: 2]:
+ all_model_output_tokens_counts[row['model']].append(num_tokens(message[1]))
+ except Exception as e:
+ print(row)
+ raise e
+
+ else:
+ chat_battle_counts[row['type']] += 1
+ if row["models"][0] is None or row["models"][1] is None:
+ continue
+
+ # Resolve model names
+ models_public = [remove_html(row["models"][0]), remove_html(row["models"][1])]
+ if "model_name" in row["states"][0]:
+ models_hidden = [
+ row["states"][0]["model_name"],
+ row["states"][1]["model_name"],
+ ]
+ if models_hidden[0] is None:
+ models_hidden = models_public
+ else:
+ models_hidden = models_public
+
+ if (models_public[0] == "" and models_public[1] != "") or (
+ models_public[1] == "" and models_public[0] != ""
+ ):
+ continue
+
+ if models_public[0] == "" or models_public[0] == "Model A":
+ anony = True
+ models = models_hidden
+ else:
+ anony = False
+ models = models_public
+ if not models_public == models_hidden:
+ continue
+
+ all_model_counts[models[0]] += 1
+ all_model_counts[models[1]] += 1
+ tstamp = row["tstamp"]
+ conv_id1 = row["states"][0]["conv_id"]
+ conv_id2 = row["states"][1]["conv_id"]
+
+ image1 = load_image_from_path(get_input_image_path(tstamp, conv_id1))
+ image2 = load_image_from_path(get_input_image_path(tstamp, conv_id2))
+ all_model_image_sizes[models[0]].append(None if image1 is None else image1.size)
+ all_model_image_sizes[models[1]].append(None if image2 is None else image2.size)
+
+ for message in row["states"][0]["messages"][row["states"][0]["offset"] :: 2]:
+ all_model_input_tokens_counts[models[0]].append(num_tokens(message[1]))
+ for message in row["states"][0]["messages"][row["states"][0]["offset"] + 1 :: 2]:
+ all_model_output_tokens_counts[models[0]].append(num_tokens(message[1]))
+ for message in row["states"][1]["messages"][row["states"][1]["offset"] :: 2]:
+ all_model_input_tokens_counts[models[1]].append(num_tokens(message[1]))
+ for message in row["states"][1]["messages"][row["states"][1]["offset"] + 1 :: 2]:
+ all_model_output_tokens_counts[models[1]].append(num_tokens(message[1]))
+
+ print("### Chat battle counts (requests)")
+ print(json.dumps(chat_battle_counts, indent=4))
+
+ print("### Model counts (requests)")
+ print(json.dumps(all_model_counts, indent=4))
+
+ print("### Model Avg input tokens counts (tokens)")
+ average_input_tokens_counts = {}
+ for model, counts in all_model_input_tokens_counts.items():
+ average_input_tokens_counts[model] = sum(counts) / len(counts)
+ print(json.dumps(average_input_tokens_counts, indent=4))
+
+ print("### Model AVg output tokens counts (tokens)")
+ average_output_tokens_counts = {}
+ for model, counts in all_model_output_tokens_counts.items():
+ average_output_tokens_counts[model] = sum(counts) / len(counts)
+ print(json.dumps(average_output_tokens_counts, indent=4))
+
+ print("### Model Avg image sizes (height, width)")
+ average_image_sizes = {}
+ for model, sizes in all_model_image_sizes.items():
+ avg_height = sum([size[0] for size in sizes if size is not None]) / len(sizes)
+ avg_width = sum([size[1] for size in sizes if size is not None]) / len(sizes)
+ average_image_sizes[model] = (avg_height, avg_width)
+ print(json.dumps(average_image_sizes, indent=4))
+
+ print("### GPT-4V estimated cost (USD)")
+ gpt_4v_name = "gpt-4-vision-preview"
+ gpt_4v_cost = {}
+ gpt_4v_cost['input'] = sum(all_model_input_tokens_counts[gpt_4v_name]) / 1000 * 0.01
+ gpt_4v_cost['output'] = sum(all_model_output_tokens_counts[gpt_4v_name]) / 1000 * 0.03
+
+ all_image_cost = 0
+ for size in all_model_image_sizes[gpt_4v_name]:
+ if size is None:
+ continue
+ all_image_tokens = (size[0] // 512 + 1) * (size[1] // 512 + 1) * 170 + 85
+ all_image_cost += all_image_tokens / 1000 * 0.01
+ gpt_4v_cost['image'] = all_image_cost
+ print(json.dumps(gpt_4v_cost, indent=4))
+
+
+
+
+if __name__ == "__main__":
+ fire.Fire(main)
\ No newline at end of file
diff --git a/arena_elo/elo_rating/inspect_elo_rating_pkl.py b/arena_elo/elo_rating/inspect_elo_rating_pkl.py
new file mode 100644
index 0000000000000000000000000000000000000000..b6eda95f9a330338dee8108ba7d0a1a459035f6f
--- /dev/null
+++ b/arena_elo/elo_rating/inspect_elo_rating_pkl.py
@@ -0,0 +1,33 @@
+import pickle
+import plotly.graph_objects as go
+
+def output_figure(data, figure_name="battle_count_heatmap", label="annoy"):
+ fig = data[label][figure_name]
+ fig.update_layout(
+ height=700,
+ width=700,
+ title={'text': f'{figure_name}', 'x': 0.5, 'y': 0.07},
+ xaxis_title="Model B",
+ yaxis_title="Model A",
+ # coloraxis_colorscale=[[0.0, '#0d0887'], [1.0, '#f0f921']],
+ margin={'t': 60}
+ )
+ fig.write_image(f"{figure_name}.png")
+
+with open("./results/latest/elo_results.pkl",'rb') as f:
+ data = pickle.load(f)
+ print()
+ df = data["anony"]["leaderboard_table_df"]
+ # sort by rating
+ print(data["anony"].keys())
+
+ for figure_name in [ 'win_fraction_heatmap', 'battle_count_heatmap',]:
+ output_figure(data, figure_name, "anony")
+
+ df = df.sort_values(by=["rating"], ascending=False)
+ print(df)
+ df = data["full"]["leaderboard_table_df"]
+ # sort by rating
+ df = df.sort_values(by=["rating"], ascending=False)
+ print(df)
+ print('done')
diff --git a/arena_elo/elo_rating/model_registry.py b/arena_elo/elo_rating/model_registry.py
new file mode 100644
index 0000000000000000000000000000000000000000..7635ba884847f95ccebd0f7af688a2ca25c37695
--- /dev/null
+++ b/arena_elo/elo_rating/model_registry.py
@@ -0,0 +1,578 @@
+"""Additional information of the models."""
+from collections import namedtuple, OrderedDict
+from typing import List
+
+
+ModelInfo = namedtuple("ModelInfo", ["simple_name", "link", "description"])
+
+
+model_info = OrderedDict()
+
+
+def register_model_info(
+ full_names: List[str], simple_name: str, link: str, description: str
+):
+ info = ModelInfo(simple_name, link, description)
+
+ for full_name in full_names:
+ model_info[full_name] = info
+
+
+def get_model_info(name: str) -> ModelInfo:
+ if name in model_info:
+ return model_info[name]
+ else:
+ # To fix this, please use `register_model_info` to register your model
+ return ModelInfo(
+ name, "", "Register the description at arena.model/model_registry.py"
+ )
+
+
+register_model_info(
+ [
+ "IEITYuan/Yuan2-2B-Janus-hf",
+ "IEITYuan/Yuan2-2B-hf",
+ "IEITYuan/Yuan2-51B-hf",
+ "IEITYuan/Yuan2-102B-hf",
+ ],
+ "IEIT-Yuan2",
+ "https://github.com/IEIT-Yuan/Yuan-2.0",
+ "Yuan2.0 is a new generation Fundamental Large Language Model developed by IEIT System.",
+)
+
+register_model_info(
+ ["mixtral-8x7b-instruct-v0.1", "mistral-7b-instruct"],
+ "Mixtral of experts",
+ "https://mistral.ai/news/mixtral-of-experts/",
+ "A Mixture-of-Experts model by Mistral AI",
+)
+
+register_model_info(
+ ["gemini-pro"],
+ "Gemini",
+ "https://blog.google/technology/ai/google-gemini-pro-imagen-duet-ai-update/",
+ "Gemini by Google",
+)
+
+register_model_info(
+ ["gemini-pro-vision"],
+ "Gemini",
+ "https://blog.google/technology/ai/google-gemini-pro-imagen-duet-ai-update/",
+ "Gemini by Google",
+)
+
+register_model_info(
+ ["solar-10.7b-instruct-v1.0"],
+ "SOLAR-10.7B-Instruct",
+ "https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0",
+ "A model trained using depth up-scaling by Upstage AI",
+)
+
+register_model_info(
+ ["gpt-4-turbo"],
+ "GPT-4-Turbo",
+ "https://platform.openai.com/docs/models/gpt-4-and-gpt-4-turbo",
+ "GPT-4-Turbo by OpenAI",
+)
+
+register_model_info(
+ ["gpt-4-vision-preview"],
+ "gpt-4-vision-preview",
+ "https://platform.openai.com/docs/models/gpt-4-and-gpt-4-turbo",
+ "GPT-4(V) by OpenAI",
+)
+
+register_model_info(
+ ["gpt-3.5-turbo", "gpt-3.5-turbo-0314", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-1106"],
+ "GPT-3.5",
+ "https://platform.openai.com/docs/models/gpt-3-5",
+ "GPT-3.5-Turbo by OpenAI",
+)
+
+register_model_info(
+ ["gpt-4", "gpt-4-0314", "gpt-4-0613"],
+ "GPT-4",
+ "https://openai.com/research/gpt-4",
+ "GPT-4 by OpenAI",
+)
+
+register_model_info(
+ ["claude-2.1", "claude-2.0"],
+ "Claude",
+ "https://www.anthropic.com/index/claude-2",
+ "Claude 2 by Anthropic",
+)
+
+register_model_info(
+ ["claude-1"],
+ "Claude",
+ "https://www.anthropic.com/index/introducing-claude",
+ "Claude 1 by Anthropic",
+)
+
+register_model_info(
+ ["claude-instant-1", "claude-instant-1.2"],
+ "Claude Instant",
+ "https://www.anthropic.com/index/introducing-claude",
+ "Claude Instant by Anthropic",
+)
+
+register_model_info(
+ ["pplx-70b-online", "pplx-7b-online"],
+ "pplx-online-llms",
+ "https://blog.perplexity.ai/blog/introducing-pplx-online-llms",
+ "Online LLM API by Perplexity AI",
+)
+
+register_model_info(
+ ["openhermes-2.5-mistral-7b"],
+ "OpenHermes-2.5-Mistral-7B",
+ "https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B",
+ "a mistral-based model fine-tuned on 1M GPT-4 outputs",
+)
+
+register_model_info(
+ ["starling-lm-7b-alpha"],
+ "Starling-LM-7B-alpha",
+ "https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha",
+ "an open model trained using RLAIF by Berkeley",
+)
+
+register_model_info(
+ ["tulu-2-dpo-70b"],
+ "Tulu 2",
+ "https://huggingface.co/allenai/tulu-2-dpo-70b",
+ "an instruction and RLHF model by UW/AllenAI",
+)
+
+register_model_info(
+ ["yi-34b-chat", "yi-6b-chat"],
+ "Yi-Chat",
+ "https://huggingface.co/01-ai/Yi-34B-Chat",
+ "A large language model by 01 AI",
+)
+
+register_model_info(
+ ["llama-2-70b-chat", "llama-2-34b-chat", "llama-2-13b-chat", "llama-2-7b-chat"],
+ "Llama 2",
+ "https://ai.meta.com/llama/",
+ "open foundation and fine-tuned chat models by Meta",
+)
+
+register_model_info(
+ [
+ "vicuna-33b",
+ "vicuna-33b-v1.3",
+ "vicuna-13b",
+ "vicuna-13b-v1.3",
+ "vicuna-7b",
+ "vicuna-7b-v1.3",
+ ],
+ "Vicuna",
+ "https://lmsys.org/blog/2023-03-30-vicuna/",
+ "a chat assistant fine-tuned on user-shared conversations by LMSYS",
+)
+
+register_model_info(
+ ["chatglm3-6b", "chatglm2-6b", "chatglm-6b"],
+ "ChatGLM",
+ "https://chatglm.cn/blog",
+ "an open bilingual dialogue language model by Tsinghua University",
+)
+
+register_model_info(
+ ["openchat-3.5"],
+ "OpenChat 3.5",
+ "https://github.com/imoneoi/openchat",
+ "an open model fine-tuned on Mistral-7B using C-RLFT",
+)
+
+register_model_info(
+ ["tenyxchat-7b-v1"],
+ "TenyxChat-7B",
+ "https://huggingface.co/tenyx/TenyxChat-7B-v1",
+ "an open model DPO trained on top of OpenChat-3.5 using Tenyx fine-tuning",
+)
+
+register_model_info(
+ ["zephyr-7b-beta", "zephyr-7b-alpha"],
+ "Zephyr",
+ "https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha",
+ "a chatbot fine-tuned from Mistral by Hugging Face",
+)
+
+register_model_info(
+ ["notus-7b-v1"],
+ "Notus",
+ "https://huggingface.co/argilla/notus-7b-v1",
+ "a chatbot fine-tuned from Zephyr SFT by Argilla",
+)
+
+register_model_info(
+ ["catppt"],
+ "CatPPT",
+ "https://huggingface.co/rishiraj/CatPPT",
+ "a chatbot fine-tuned from a SLERP merged model by Rishiraj Acharya",
+)
+
+register_model_info(
+ ["TinyLlama"],
+ "TinyLlama",
+ "https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+ "The TinyLlama project is an open endeavor to pretrain a 1.1B Llama model on 3 trillion tokens.",
+)
+
+register_model_info(
+ ["qwen-14b-chat"],
+ "Qwen",
+ "https://huggingface.co/Qwen/Qwen-14B-Chat",
+ "a large language model by Alibaba Cloud",
+)
+
+register_model_info(
+ ["codellama-34b-instruct", "codellama-13b-instruct", "codellama-7b-instruct"],
+ "Code Llama",
+ "https://ai.meta.com/blog/code-llama-large-language-model-coding/",
+ "open foundation models for code by Meta",
+)
+
+register_model_info(
+ ["wizardlm-70b", "wizardlm-30b", "wizardlm-13b"],
+ "WizardLM",
+ "https://github.com/nlpxucan/WizardLM",
+ "an instruction-following LLM using evol-instruct by Microsoft",
+)
+
+register_model_info(
+ ["wizardcoder-15b-v1.0"],
+ "WizardLM",
+ "https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder",
+ "Empowering Code Large Language Models with Evol-Instruct",
+)
+
+register_model_info(
+ ["mpt-7b-chat", "mpt-30b-chat"],
+ "MPT-Chat",
+ "https://www.mosaicml.com/blog/mpt-30b",
+ "a chatbot fine-tuned from MPT by MosaicML",
+)
+
+register_model_info(
+ ["guanaco-33b", "guanaco-65b"],
+ "Guanaco",
+ "https://github.com/artidoro/qlora",
+ "a model fine-tuned with QLoRA by UW",
+)
+
+register_model_info(
+ ["gpt4all-13b-snoozy"],
+ "GPT4All-Snoozy",
+ "https://github.com/nomic-ai/gpt4all",
+ "a finetuned LLaMA model on assistant style data by Nomic AI",
+)
+
+register_model_info(
+ ["koala-13b"],
+ "Koala",
+ "https://bair.berkeley.edu/blog/2023/04/03/koala",
+ "a dialogue model for academic research by BAIR",
+)
+
+register_model_info(
+ ["RWKV-4-Raven-14B"],
+ "RWKV-4-Raven",
+ "https://huggingface.co/BlinkDL/rwkv-4-raven",
+ "an RNN with transformer-level LLM performance",
+)
+
+register_model_info(
+ ["alpaca-13b"],
+ "Alpaca",
+ "https://crfm.stanford.edu/2023/03/13/alpaca.html",
+ "a model fine-tuned from LLaMA on instruction-following demonstrations by Stanford",
+)
+
+register_model_info(
+ ["oasst-pythia-12b"],
+ "OpenAssistant (oasst)",
+ "https://open-assistant.io",
+ "an Open Assistant for everyone by LAION",
+)
+
+register_model_info(
+ ["oasst-sft-7-llama-30b"],
+ "OpenAssistant (oasst)",
+ "https://open-assistant.io",
+ "an Open Assistant for everyone by LAION",
+)
+
+register_model_info(
+ ["palm-2"],
+ "PaLM 2 Chat",
+ "https://cloud.google.com/vertex-ai/docs/release-notes#May_10_2023",
+ "PaLM 2 for Chat (chat-bison@001) by Google",
+)
+
+register_model_info(
+ ["llama-7b", "llama-13b"],
+ "LLaMA",
+ "https://arxiv.org/abs/2302.13971",
+ "open and efficient foundation language models by Meta",
+)
+
+register_model_info(
+ ["open-llama-7b-v2-open-instruct", "open-llama-7b-open-instruct"],
+ "Open LLaMa (Open Instruct)",
+ "https://medium.com/vmware-data-ml-blog/starter-llm-for-the-enterprise-instruction-tuning-openllama-7b-d05fc3bbaccc",
+ "Open LLaMa fine-tuned on instruction-following data by VMware",
+)
+
+register_model_info(
+ ["dolly-v2-12b"],
+ "Dolly",
+ "https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm",
+ "an instruction-tuned open large language model by Databricks",
+)
+
+register_model_info(
+ ["stablelm-tuned-alpha-7b"],
+ "StableLM",
+ "https://github.com/stability-AI/stableLM",
+ "Stability AI language models",
+)
+
+register_model_info(
+ ["codet5p-6b"],
+ "CodeT5p-6b",
+ "https://huggingface.co/Salesforce/codet5p-6b",
+ "Code completion model released by Salesforce",
+)
+
+register_model_info(
+ ["fastchat-t5-3b", "fastchat-t5-3b-v1.0"],
+ "FastChat-T5",
+ "https://huggingface.co/lmsys/fastchat-t5-3b-v1.0",
+ "a chat assistant fine-tuned from FLAN-T5 by LMSYS",
+)
+
+register_model_info(
+ ["phoenix-inst-chat-7b"],
+ "Phoenix-7B",
+ "https://huggingface.co/FreedomIntelligence/phoenix-inst-chat-7b",
+ "a multilingual chat assistant fine-tuned from Bloomz to democratize ChatGPT across languages by CUHK(SZ)",
+)
+
+register_model_info(
+ ["realm-7b-v1"],
+ "ReaLM",
+ "https://github.com/FreedomIntelligence/ReaLM",
+ "A chatbot fine-tuned from LLaMA2 with data generated via iterative calls to UserGPT and ChatGPT by CUHK(SZ) and SRIBD.",
+)
+
+register_model_info(
+ ["billa-7b-sft"],
+ "BiLLa-7B-SFT",
+ "https://huggingface.co/Neutralzz/BiLLa-7B-SFT",
+ "an instruction-tuned bilingual LLaMA with enhanced reasoning ability by an independent researcher",
+)
+
+register_model_info(
+ ["h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt-v2"],
+ "h2oGPT-GM-7b",
+ "https://huggingface.co/h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt-v2",
+ "an instruction-tuned OpenLLaMA with enhanced conversational ability by H2O.ai",
+)
+
+register_model_info(
+ ["baize-v2-7b", "baize-v2-13b"],
+ "Baize v2",
+ "https://github.com/project-baize/baize-chatbot#v2",
+ "A chatbot fine-tuned from LLaMA with ChatGPT self-chat data and Self-Disillation with Feedback (SDF) by UCSD and SYSU.",
+)
+
+register_model_info(
+ [
+ "airoboros-l2-7b-2.1",
+ "airoboros-l2-13b-2.1",
+ "airoboros-c34b-2.1",
+ "airoboros-l2-70b-2.1",
+ ],
+ "airoboros",
+ "https://huggingface.co/jondurbin/airoboros-l2-70b-2.1",
+ "an instruction-tuned LlaMa model tuned with 100% synthetic instruction-response pairs from GPT4",
+)
+
+register_model_info(
+ [
+ "spicyboros-7b-2.2",
+ "spicyboros-13b-2.2",
+ "spicyboros-70b-2.2",
+ ],
+ "spicyboros",
+ "https://huggingface.co/jondurbin/spicyboros-70b-2.2",
+ "de-aligned versions of the airoboros models",
+)
+
+register_model_info(
+ ["Robin-7b-v2", "Robin-13b-v2", "Robin-33b-v2"],
+ "Robin-v2",
+ "https://huggingface.co/OptimalScale/robin-7b-v2-delta",
+ "A chatbot fine-tuned from LLaMA-7b, achieving competitive performance on chitchat, commonsense reasoning and instruction-following tasks, by OptimalScale, HKUST.",
+)
+
+register_model_info(
+ ["manticore-13b-chat"],
+ "Manticore 13B Chat",
+ "https://huggingface.co/openaccess-ai-collective/manticore-13b-chat-pyg",
+ "A chatbot fine-tuned from LlaMa across several CoT and chat datasets.",
+)
+
+register_model_info(
+ ["redpajama-incite-7b-chat"],
+ "RedPajama-INCITE-7B-Chat",
+ "https://huggingface.co/togethercomputer/RedPajama-INCITE-7B-Chat",
+ "A chatbot fine-tuned from RedPajama-INCITE-7B-Base by Together",
+)
+
+register_model_info(
+ [
+ "falcon-7b",
+ "falcon-7b-instruct",
+ "falcon-40b",
+ "falcon-40b-instruct",
+ "falcon-180b",
+ "falcon-180b-chat",
+ ],
+ "Falcon",
+ "https://huggingface.co/tiiuae/falcon-180B",
+ "TII's flagship series of large language models",
+)
+
+register_model_info(
+ ["tigerbot-7b-sft"],
+ "Tigerbot",
+ "https://huggingface.co/TigerResearch/tigerbot-7b-sft",
+ "TigerBot is a large-scale language model (LLM) with multiple languages and tasks.",
+)
+
+register_model_info(
+ ["internlm-chat-7b", "internlm-chat-7b-8k"],
+ "InternLM",
+ "https://huggingface.co/internlm/internlm-chat-7b",
+ "InternLM is a multi-language large-scale language model (LLM), developed by SHLAB.",
+)
+
+register_model_info(
+ ["Qwen-7B-Chat"],
+ "Qwen",
+ "https://huggingface.co/Qwen/Qwen-7B-Chat",
+ "Qwen is a multi-language large-scale language model (LLM), developed by Damo Academy.",
+)
+
+register_model_info(
+ ["Llama2-Chinese-13b-Chat", "LLama2-Chinese-13B"],
+ "Llama2-Chinese",
+ "https://huggingface.co/FlagAlpha/Llama2-Chinese-13b-Chat",
+ "Llama2-Chinese is a multi-language large-scale language model (LLM), developed by FlagAlpha.",
+)
+
+register_model_info(
+ ["Chinese-Alpaca-2-7B", "Chinese-Alpaca-2-13B"],
+ "Chinese-Alpaca",
+ "https://huggingface.co/hfl/chinese-alpaca-2-13b",
+ "New extended Chinese vocabulary beyond Llama-2, open-sourcing the Chinese LLaMA-2 and Alpaca-2 LLMs.",
+)
+
+register_model_info(
+ ["Vigogne-2-7B-Instruct", "Vigogne-2-13B-Instruct"],
+ "Vigogne-Instruct",
+ "https://huggingface.co/bofenghuang/vigogne-2-7b-instruct",
+ "Vigogne-Instruct is a French large language model (LLM) optimized for instruction-following, developed by Bofeng Huang",
+)
+
+register_model_info(
+ ["Vigogne-2-7B-Chat", "Vigogne-2-13B-Chat"],
+ "Vigogne-Chat",
+ "https://huggingface.co/bofenghuang/vigogne-2-7b-chat",
+ "Vigogne-Chat is a French large language model (LLM) optimized for instruction-following and multi-turn dialogues, developed by Bofeng Huang",
+)
+
+register_model_info(
+ ["stable-vicuna-13B-HF"],
+ "stable-vicuna",
+ "https://huggingface.co/TheBloke/stable-vicuna-13B-HF",
+ "StableVicuna is a Vicuna model fine-tuned using RLHF via PPO on various conversational and instructional datasets.",
+)
+
+register_model_info(
+ ["deluxe-chat-v1", "deluxe-chat-v1.1", "deluxe-chat-v1.2"],
+ "DeluxeChat",
+ "",
+ "Deluxe Chat",
+)
+
+register_model_info(
+ [
+ "Xwin-LM-7B-V0.1",
+ "Xwin-LM-13B-V0.1",
+ "Xwin-LM-70B-V0.1",
+ "Xwin-LM-7B-V0.2",
+ "Xwin-LM-13B-V0.2",
+ ],
+ "Xwin-LM",
+ "https://github.com/Xwin-LM/Xwin-LM",
+ "Chat models developed by Xwin-LM team",
+)
+
+register_model_info(
+ ["lemur-70b-chat"],
+ "Lemur-Chat",
+ "https://huggingface.co/OpenLemur/lemur-70b-chat-v1",
+ "an openly accessible language model optimized for both natural language and coding capabilities ",
+)
+
+register_model_info(
+ ["Mistral-7B-OpenOrca"],
+ "Open-Orca",
+ "https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca",
+ "A fine-tune of [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) using [OpenOrca dataset](https://huggingface.co/datasets/Open-Orca/OpenOrca)",
+)
+
+register_model_info(
+ ["dolphin-2.2.1-mistral-7b"],
+ "dolphin-mistral",
+ "https://huggingface.co/ehartford/dolphin-2.2.1-mistral-7b",
+ "An uncensored fine-tuned Mistral 7B",
+)
+
+register_model_info(
+ [
+ "AquilaChat-7B",
+ "AquilaChat2-7B",
+ "AquilaChat2-34B",
+ ],
+ "Aquila-Chat",
+ "https://huggingface.co/BAAI/AquilaChat2-34B",
+ "Chat models developed by BAAI team",
+)
+
+register_model_info(
+ ["xDAN-L1-Chat-RL-v1"],
+ "xDAN-L1-Chat",
+ "https://huggingface.co/xDAN-AI/xDAN-L1-Chat-RL-v1",
+ "A large language chat model created by xDAN-AI.",
+)
+
+register_model_info(
+ ["MetaMath-70B-V1.0", "MetaMath-7B-V1.0"],
+ "MetaMath",
+ "https://huggingface.co/meta-math",
+ "MetaMath is a finetune of Llama2 on [MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA) that specializes in mathematical reasoning.",
+)
+
+register_model_info(
+ ["Yuan2-2B-hf", "Yuan2-51B-hf", "Yuan2-102B-hf"],
+ "IEIYuan",
+ "https://huggingface.co/IEITYuan",
+ "Yuan2 is a Basemodel developed by IEI.",
+)
diff --git a/arena_elo/elo_rating/upload_battle_data.py b/arena_elo/elo_rating/upload_battle_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ca5f242eea33bf5e266b26a8c40a91db1106e02
--- /dev/null
+++ b/arena_elo/elo_rating/upload_battle_data.py
@@ -0,0 +1,193 @@
+import fire
+import json
+import os
+import datasets
+import datetime
+from pathlib import Path
+from datetime import datetime
+from PIL import Image
+
+datasets.config.DEFAULT_MAX_BATCH_SIZE = 500
+def create_hf_dataset(data_file: str, split="test"):
+ hf_dataset = datasets.Dataset.from_list(
+ data_file,
+ features=datasets.Features(
+ {
+ "question_id": datasets.Value("string"),
+ "model": datasets.Value("string"),
+ "conversation": [
+ {
+ "role": datasets.Value("string"),
+ "content": datasets.Value("string"),
+ }
+ ],
+ "language": datasets.Value("string"),
+ "image": datasets.Image(),
+ "turn": datasets.Value("int32"),
+ }
+ ),
+ split=split,
+ )
+ return hf_dataset
+
+def create_hf_battle_dataset(data_file: str, split="test"):
+ hf_dataset = datasets.Dataset.from_list(
+ data_file,
+ features=datasets.Features(
+ {
+ "question_id": datasets.Value("string"),
+ "model_a": datasets.Value("string"),
+ "model_b": datasets.Value("string"),
+ "conversation_a": [
+ {
+ "role": datasets.Value("string"),
+ "content": datasets.Value("string"),
+ }
+ ],
+ "conversation_b": [
+ {
+ "role": datasets.Value("string"),
+ "content": datasets.Value("string"),
+ }
+ ],
+ "language": datasets.Value("string"),
+ "image": datasets.Image(),
+ "turn": datasets.Value("int32"),
+ "anony": datasets.Value("bool"),
+ }
+ ),
+ split=split,
+ )
+ return hf_dataset
+
+
+
+
+def load_image(path:str):
+ try:
+ return Image.open(path)
+ except Exception as e:
+ print(f"Error loading image {path}: {e}")
+ return None
+
+def get_date_from_time_stamp(unix_timestamp: int):
+ # Create a datetime object from the Unix timestamp
+ dt = datetime.fromtimestamp(unix_timestamp)
+
+ # Convert the datetime object to a string with the desired format
+ date_str = dt.strftime("%Y-%m-%d")
+ return date_str
+
+def load_battle_image(battle, log_dir):
+ image_path = Path(log_dir) / f"{get_date_from_time_stamp(battle['tstamp'])}-convinput_images" / f"input_image_{battle['question_id']}.png"
+ return load_image(image_path)
+
+
+def main(
+ data_file: str = "./results/latest/clean_battle_conv.json",
+ repo_id: str = "DongfuTingle/wildvision-bench",
+ log_dir: str = os.getenv("LOGDIR", "./vision-arena-logs/"),
+ mode="battle",
+ token = os.environ.get("HUGGINGFACE_TOKEN", None)
+):
+ with open(data_file, "r") as f:
+ data = json.load(f)
+
+
+
+ has_image_stats = {
+ "has_image": 0,
+ "no_image": 0,
+ }
+ if mode == "keep_bad_only":
+ # anony only
+ data = [d for d in data if d["anony"]]
+
+ new_data = []
+ for battle in data:
+ image = load_battle_image(battle, log_dir)
+ if image is None:
+ has_image_stats["no_image"] += 1
+ # we don't keep the data without image
+ continue
+ has_image_stats["has_image"] += 1
+
+ if battle["winner"] in ["model_a", "model_b"]:
+ if battle["winner"] == "model_a":
+ worse_model = "model_b"
+ worse_conv = "conversation_b"
+ if battle["winner"] == "model_b":
+ worse_model = "model_a"
+ worse_conv = "conversation_a"
+
+ new_data.append({
+ "question_id": battle["question_id"],
+ "model": battle[worse_model],
+ "conversation": battle[worse_conv],
+ "language": battle["language"],
+ "image": image,
+ "turn": battle["turn"],
+ })
+ elif battle["winner"] == "tie (bothbad)":
+
+ new_data.append({
+ "question_id": battle["question_id"],
+ "model": battle["model_a"],
+ "conversation": battle["conversation_a"],
+ "language": battle["language"],
+ "image": image,
+ "turn": battle["turn"],
+ })
+
+ new_data.append({
+ "question_id": battle["question_id"],
+ "model": battle["model_b"],
+ "conversation": battle["conversation_b"],
+ "language": battle["language"],
+ "image": image,
+ "turn": battle["turn"],
+ })
+
+ split = "test"
+ hf_dataset = create_hf_dataset(new_data, "test")
+
+ elif mode == "battle":
+ new_data = []
+ for battle in data:
+ image = load_battle_image(battle, log_dir)
+ if image is None:
+ has_image_stats["no_image"] += 1
+ continue
+ has_image_stats["has_image"] += 1
+ new_data.append({
+ "question_id": battle["question_id"],
+ "model_a": battle["model_a"],
+ "model_b": battle["model_b"],
+ "conversation_a": battle["conversation_a"],
+ "conversation_b": battle["conversation_b"],
+ "language": battle["language"],
+ "image": image,
+ "turn": battle["turn"],
+ "anony": battle["anony"],
+ })
+ split = "test"
+ hf_dataset = create_hf_battle_dataset(new_data, "test")
+ else:
+ raise ValueError(f"Invalid mode: {mode}")
+
+ print(f"Stats: {has_image_stats}")
+ print(hf_dataset)
+ print(f"Uploading to part {repo_id}:{split}...")
+ hf_dataset.push_to_hub(
+ repo_id=repo_id,
+ config_name=mode,
+ split=split,
+ token=token,
+ commit_message=f"Add vision-arena {split} dataset",
+ )
+
+ print("Done!")
+
+
+if __name__ == "__main__":
+ fire.Fire(main)
\ No newline at end of file
diff --git a/arena_elo/elo_rating/utils.py b/arena_elo/elo_rating/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..250a1d97bcc5f7ba175e4e57a4b7becafb06ad4b
--- /dev/null
+++ b/arena_elo/elo_rating/utils.py
@@ -0,0 +1,83 @@
+from datetime import datetime
+import pytz
+import PIL
+import os
+
+def detect_language(text: str) -> str:
+ """Detect the langauge of a string."""
+ import polyglot # pip3 install polyglot pyicu pycld2
+ from polyglot.detect import Detector
+ from polyglot.detect.base import logger as polyglot_logger
+ import pycld2
+
+ polyglot_logger.setLevel("ERROR")
+
+ try:
+ lang_code = Detector(text).language.name
+ except (pycld2.error, polyglot.detect.base.UnknownLanguage):
+ lang_code = "unknown"
+ return lang_code
+
+
+def get_time_stamp_from_date(date_str:str):
+ """
+ Convert a date string to a Unix timestamp
+ Args:
+ date_str (str): The input date string in the format 'YYYY-MM-DD-HH:MM-TZ', e.g. '2024-02-10-14:00-PT'
+ """
+
+ # Convert the date string into a format that Python's datetime can understand
+ # and specify the correct timezone for PT, which is 'US/Pacific'
+ date_format = "%Y-%m-%d-%H:%M-%Z"
+
+ # Parse the date string into a datetime object
+ # Note: PT is not directly recognized by pytz, so we manually map it to 'US/Pacific'
+ timezone_map = {
+ "PT": "US/Pacific",
+ }
+
+ # Extract the timezone abbreviation
+ tz_abbr = date_str.split("-")[-1]
+ # Map the abbreviation to a pytz timezone
+ tz_info = pytz.timezone(timezone_map[tz_abbr])
+
+ # Remove the timezone abbreviation for parsing
+ date_str_parsed = date_str.rsplit("-", 1)[0]
+
+ # Create a datetime object with the corresponding timezone
+ dt = datetime.strptime(date_str_parsed, "%Y-%m-%d-%H:%M").replace(tzinfo=tz_info)
+
+ # Convert the datetime object to a Unix timestamp
+ unix_timestamp = dt.timestamp()
+ return unix_timestamp
+
+def get_date_from_time_stamp(unix_timestamp: int):
+ # Create a datetime object from the Unix timestamp
+ dt = datetime.fromtimestamp(unix_timestamp)
+
+ # Convert the datetime object to a string with the desired format
+ date_str = dt.strftime("%Y-%m-%d %H:%M:%S %Z")
+ return date_str
+
+
+def get_input_image_path(tstamp, conv_id):
+ # from tstamp to date e.g. 2024-02-10
+ date_str = datetime.fromtimestamp(tstamp, tz=pytz.timezone("US/Pacific")).strftime("%Y-%m-%d")
+ LOGDIR = os.getenv("LOGDIR")
+ return f"{LOGDIR}/{date_str}-convinput_images/input_image_{conv_id}.png"
+
+def load_image_from_path(image_path):
+ # Load the image from the specified
+ # path using the Python Imaging Library (PIL)
+ try:
+ image = PIL.Image.open(image_path)
+ return image
+ except FileNotFoundError:
+ print(f"Image not found at path: {image_path}")
+ return None
+ except PIL.UnidentifiedImageError:
+ print(f"Unidentified image format at path: {image_path}")
+ return None
+
+
+
\ No newline at end of file
diff --git a/arena_elo/evaluator/convert_to_evaluator_data.py b/arena_elo/evaluator/convert_to_evaluator_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..83b04b2ad962b5e63cc682c20378bd1d1ea68e8a
--- /dev/null
+++ b/arena_elo/evaluator/convert_to_evaluator_data.py
@@ -0,0 +1,134 @@
+import argparse
+import json
+import os
+import time
+from pytz import timezone
+from tqdm import tqdm
+import base64
+from icecream import ic
+from PIL import Image
+
+
+# Function to encode the image
+def encode_image(image_path):
+ with open(image_path, "rb") as image_file:
+ return base64.b64encode(image_file.read()).decode('utf-8')
+
+def get_log_files(max_num_files=None):
+ dates = []
+ for month in [2, 3]:
+ for day in range(1, 32):
+ dates.append(f"2024-{month:02d}-{day:02d}")
+
+ num_servers = 1
+ filenames = []
+ for d in dates:
+ for i in range(num_servers):
+ # name = os.path.expanduser(f"~/fastchat_logs/server{i}/{d}-conv.json")
+ name = os.path.expanduser(f"vision-arena-logs/{d}-conv.json")
+ if os.path.exists(name):
+ filenames.append(name)
+ max_num_files = max_num_files or len(filenames)
+ filenames = filenames[-max_num_files:]
+ return filenames
+
+
+def pretty_print_conversation(messages):
+ for role, msg in messages:
+ print(f"[[{role}]]: {msg}")
+
+task_template_map = {
+ "image_caption": "Give me the semantic alignment score between the given image and the given caption: \"{generated_sentence}\" on a scale of 0-100. Only reply the score value.",
+ "vqa": "Rate the answer correctness regarding the question within the context of the given image on a scale of 0-100. Only reply the score value.",
+ "pair_rate_old": "[Instruction]\n\"{instruction}\"\n\n\"{generated_sentence}\"\n\n[System]\nGiven the instruction and the image, please compare the correctness of responses A and B. Reply with \"leftvote\" if you find A better, \"rightvote\" if B is better, \"bothbad_vote\" if both responses are wrong, and \"tievote\" if both responses are equally satisfactory. If you are unable to make a decision, please reply with \"NA\".",
+ "pair_rate_wexplanation": "[Instruction]\n\"{instruction}\"\n\n\"{generated_sentence}\"[System]\nPlease act as an impartial judge and evaluate the quality of the responses provided by two AI assistants to the user question displayed below. You should choose the assistant that follows the user’s instructions and answers the user’s question better. Your evaluation should consider factors such as the helpfulness, relevance, accuracy, depth, creativity, and level of detail of their responses. Begin your evaluation by comparing the two responses and provide a short explanation. Avoid any positional biases and ensure that the order in which the responses were presented does not influence your decision. Do not allow the length of the responses to influence your evaluation. Do not favor certain names of the assistants. Be as objective as possible. After providing your explanation, output your final verdict by strictly following this format: \"[[A]]\" if assistant A is better, \"[[B]]\" if assistant B is better, and \"[[C]]\" for a tie.",
+ "pair_rate": "[Instruction]\n\"{instruction}\"\n\n\"{generated_sentence}\"\n\n[System]\nPlease act as an impartial judge and evaluate the quality of the responses provided by two AI assistants to the user question displayed below. You should choose the assistant that follows the user’s instructions and answers the user’s question better. Your evaluation should consider factors such as the helpfulness, relevance, accuracy, depth, creativity, and level of detail of their responses. Begin your evaluation by comparing the two responses and provide a short explanation. Avoid any positional biases and ensure that the order in which the responses were presented does not influence your decision. Do not allow the length of the responses to influence your evaluation. Do not favor certain names of the assistants. Be as objective as possible. Reply with \"leftvote\" if you find assistant A better, \"rightvote\" if assistant B is better, \"bothbad_vote\" if both responses are wrong, and \"tievote\" if both assistants provide equally satisfactory answers. If you are unable to make a decision, please reply with \"NA\"."
+}
+
+def inspect_convs(log_files):
+ json_data = []
+
+ ic(log_files)
+ total_vote = 0
+
+ for filename in tqdm(log_files, desc="read files"):
+ for retry in range(5):
+ try:
+ lines = open(filename).readlines()
+ break
+ except FileNotFoundError:
+ time.sleep(2)
+
+ for l in lines:
+ row = json.loads(l)
+
+ if "states" not in row:
+ continue
+ if row["type"] not in ["leftvote", "rightvote", "bothbad_vote", "tievote"]:
+ continue
+
+ model_names = row["states"][0]["model_name"], row["states"][1]["model_name"]
+
+
+ # Iterate through each state and write the relevant information
+ if not len(row["states"][0]['messages']): continue
+ # ic(row["states"][0]['messages'][1][1])
+
+ if row["states"][0]['messages'][1][1] is None or row["states"][1]['messages'][1][1] is None or "NETWORK ERROR" in row["states"][0]['messages'][1][1] or "NETWORK ERROR" in row["states"][1]['messages'][1][1]: continue
+ total_vote += 1
+
+ conv_id = row["states"][0]['conv_id']
+ image_path = os.path.join("/local/home/yujielu/project/Arena-Elo/vision-arena-logs", os.path.basename(filename)[:-5]+"input_images", f"input_image_{conv_id}.png")
+ if not os.path.exists(image_path) :
+ continue
+ try:
+ image = Image.open(image_path).convert("RGB")
+ except:
+ continue
+
+ left_response = row["states"][0]['messages'][1][1]
+ right_response = row["states"][1]['messages'][1][1]
+ instruction = row["states"][0]['messages'][0][1]
+ generated_sentence = f"[The Start of Assistant A’s Answer]\n{left_response}\n[The End of Assistant A’s Answer]\n\n[The Start of Assistant B’s Answer]\n{right_response}\n[The End of Assistant B’s Answer]"
+ text_prompt = task_template_map["pair_rate"].format(instruction=instruction, generated_sentence=generated_sentence)
+
+ user_input = text_prompt
+ # Create the conversation structure
+ conversation = [
+ {
+ "from": "human",
+ "value": user_input
+ },
+ {
+ "from": "gpt",
+ "value": row["type"]
+ }
+ ]
+
+ # Create the JSON object for each row
+ json_obj = {
+ "id": conv_id,
+ "image": image_path,
+ "conversations": conversation
+ }
+
+ # Append the JSON object to the list
+ json_data.append(json_obj)
+
+ # Write the JSON data to a file
+ with open('output_evaluator_data.json', 'w') as json_file:
+ json.dump(json_data, json_file, indent=2)
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--max-num-files", type=int)
+ args = parser.parse_args()
+
+ log_files = get_log_files(args.max_num_files)
+
+
+
+ inspect_convs(log_files)
+
+
+
diff --git a/arena_elo/evaluator/rating_analysis.ipynb b/arena_elo/evaluator/rating_analysis.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..e1d574c04ee5e4896929e933ff35b1c02a309d8f
--- /dev/null
+++ b/arena_elo/evaluator/rating_analysis.ipynb
@@ -0,0 +1,321 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1338\n",
+ "1044\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "import pandas as pd\n",
+ "import json\n",
+ "\n",
+ "# Replace 'your_file_name.csv' with the path to your CSV file\n",
+ "file_name = 'all_pairvote_log_wgpt.csv'\n",
+ "\n",
+ "# Load the CSV file into a DataFrame\n",
+ "df = pd.read_csv(file_name)\n",
+ "\n",
+ "# Define a function to parse JSON data\n",
+ "def parse_json(data):\n",
+ " try:\n",
+ " # Parse the JSON data\n",
+ " return json.loads(data)\n",
+ " except ValueError as e:\n",
+ " # Return None or an empty dictionary if the data cannot be parsed\n",
+ " return None\n",
+ "\n",
+ "# Apply the parse_json function to the 'models' and 'states' columns\n",
+ "df['models'] = df['models'].apply(parse_json)\n",
+ "df['states'] = df['states'].apply(parse_json)\n",
+ "# row[\"states\"][0]['messages'][0][1]\n",
+ "\n",
+ "# Now df contains the parsed JSON data in the 'models' and 'states' columns\n",
+ "# print(df.head())\n",
+ "print(len(df))\n",
+ "# filter_vote_df = df[df[\"gpt_vote\"].isin([\"leftvote\", \"rightvote\"])]#, \"tievote\", \"bothbad_vote\"\n",
+ "# \\#1\n",
+ "filter_vote_df = df[df[\"gpt_vote\"].isin([\"leftvote\", \"rightvote\", \"tievote\", \"bothbad_vote\"])]\n",
+ "# \\#2\n",
+ "# filter_vote_df = df\n",
+ "filter_vote_df.loc[~filter_vote_df[\"gpt_vote\"].isin([\"leftvote\", \"rightvote\"]), \"gpt_vote\"] = \"tie\"\n",
+ "filter_vote_df.loc[~filter_vote_df[\"type\"].isin([\"leftvote\", \"rightvote\"]), \"type\"] = \"tie\"\n",
+ "# \\#3\n",
+ "#[df[\"gpt_vote\"].isin([\"leftvote\", \"rightvote\"]) & df[\"type\"].isin([\"leftvote\", \"rightvote\"])]\n",
+ "filtered_df = filter_vote_df[filter_vote_df[\"states\"].apply(lambda x: len(x[0]['messages'][0][1]) > 10)]\n",
+ "print(len(filtered_df))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Confusion Matrix:\n",
+ "[[300 61 34]\n",
+ " [102 269 27]\n",
+ " [ 99 111 41]]\n",
+ "\n",
+ "Accuracy: 0.5842911877394636\n"
+ ]
+ }
+ ],
+ "source": [
+ "import warnings\n",
+ "warnings.filterwarnings('ignore')\n",
+ "\n",
+ "from sklearn.metrics import confusion_matrix, accuracy_score\n",
+ "import pandas as pd\n",
+ "\n",
+ "# Assuming df is your DataFrame\n",
+ "\n",
+ "# True labels\n",
+ "y_true = filtered_df[\"type\"]\n",
+ "\n",
+ "# Predictions\n",
+ "y_pred = filtered_df[\"gpt_vote\"]\n",
+ "\n",
+ "# Compute the confusion matrix\n",
+ "# conf_matrix = confusion_matrix(y_true, y_pred, labels=[\"leftvote\", \"rightvote\", \"tievote\", \"bothbad_vote\"])\n",
+ "conf_matrix = confusion_matrix(y_true, y_pred, labels=[\"leftvote\", \"rightvote\", \"tie\"])\n",
+ "\n",
+ "# Compute the accuracy\n",
+ "accuracy = accuracy_score(y_true, y_pred)\n",
+ "\n",
+ "print(\"Confusion Matrix:\")\n",
+ "print(conf_matrix)\n",
+ "\n",
+ "print(\"\\nAccuracy:\", accuracy)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import seaborn as sns\n",
+ "import matplotlib.pyplot as plt\n",
+ "from sklearn.metrics import confusion_matrix\n",
+ "import pandas as pd\n",
+ "\n",
+ "# Assuming df is your DataFrame\n",
+ "\n",
+ "# True labels and predictions\n",
+ "y_true = filtered_df[\"type\"]\n",
+ "y_pred = filtered_df[\"gpt_vote\"]\n",
+ "\n",
+ "# Compute the confusion matrix\n",
+ "conf_matrix = confusion_matrix(y_true, y_pred, labels=[\"leftvote\", \"rightvote\", \"tievote\", \"bothbad_vote\"])\n",
+ "\n",
+ "# Create a pandas DataFrame from the confusion matrix\n",
+ "conf_matrix_df = pd.DataFrame(conf_matrix, index=[\"leftvote\", \"rightvote\", \"tievote\", \"bothbad_vote\"], columns=[\"leftvote\", \"rightvote\", \"tievote\", \"bothbad_vote\"])\n",
+ "\n",
+ "# Plotting the heatmap\n",
+ "plt.figure(figsize=(10, 7))\n",
+ "sns.heatmap(conf_matrix_df, annot=True, fmt=\"d\", cmap=\"Blues\", cbar=False)\n",
+ "plt.title(\"Arena Human vs GPT-4V Confusion Matrix\")\n",
+ "plt.xlabel(\"GPT-4V Vote\")\n",
+ "plt.ylabel(\"Arena Human Vote\")\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy: 0.5842911877394636\n",
+ "F1 Score (Macro): 0.514392348541452\n",
+ "F1 Score (Micro): 0.5842911877394636\n",
+ "F1 Score (Weighted): 0.5536668839130223\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.metrics import accuracy_score, f1_score\n",
+ "\n",
+ "# Assuming df is your DataFrame and it contains 'type' as true labels and 'gpt_vote' as predictions\n",
+ "y_true = filtered_df['type']\n",
+ "y_pred = filtered_df['gpt_vote']\n",
+ "\n",
+ "# Calculate accuracy\n",
+ "accuracy = accuracy_score(y_true, y_pred)\n",
+ "print(f'Accuracy: {accuracy}')\n",
+ "\n",
+ "# Calculate F1 score, here using 'macro' average to treat all classes equally\n",
+ "f1 = f1_score(y_true, y_pred, average='macro')\n",
+ "print(f'F1 Score (Macro): {f1}')\n",
+ "\n",
+ "# If you want to calculate F1 score with other averages, for example 'micro' or 'weighted', you can do:\n",
+ "f1_micro = f1_score(y_true, y_pred, average='micro')\n",
+ "print(f'F1 Score (Micro): {f1_micro}')\n",
+ "\n",
+ "f1_weighted = f1_score(y_true, y_pred, average='weighted')\n",
+ "print(f'F1 Score (Weighted): {f1_weighted}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Cohen's Kappa Score: 0.3442144615665177\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.metrics import cohen_kappa_score\n",
+ "\n",
+ "# Assuming df is your DataFrame and it contains 'type' as true labels and 'gpt_vote' as predictions\n",
+ "y_true = filtered_df['type']\n",
+ "y_pred = filtered_df['gpt_vote']\n",
+ "\n",
+ "# Calculate Cohen's Kappa score\n",
+ "kappa = cohen_kappa_score(y_true, y_pred)\n",
+ "print(f'Cohen\\'s Kappa Score: {kappa}')\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy Score: 0.5842911877394636\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.metrics import accuracy_score\n",
+ "accuracy = accuracy_score(y_true, y_pred)\n",
+ "print(f'Accuracy Score: {accuracy}')\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Pearson Correlation Coefficient: 0.2880096104357029\n"
+ ]
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "\n",
+ "# Assuming filtered_df is your DataFrame and it contains 'type' and 'gpt_vote' columns\n",
+ "# Convert 'type' and 'gpt_vote' to categorical codes\n",
+ "filtered_df['type_int'] = pd.factorize(filtered_df['type'])[0]\n",
+ "filtered_df['gpt_vote_int'] = pd.factorize(filtered_df['gpt_vote'])[0]\n",
+ "\n",
+ "# Now you can calculate Pearson correlation between these new integer columns\n",
+ "pearson_correlation = filtered_df['type_int'].corr(filtered_df['gpt_vote_int'])\n",
+ "print(f'Pearson Correlation Coefficient: {pearson_correlation}')\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "otask",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.13"
+ },
+ "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/arena_elo/generation_model_info.json b/arena_elo/generation_model_info.json
new file mode 100644
index 0000000000000000000000000000000000000000..1c3e3775ac2e3c0985221d841a596c6e2137a556
--- /dev/null
+++ b/arena_elo/generation_model_info.json
@@ -0,0 +1,57 @@
+{
+ "LCM": {
+ "Link": "https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7",
+ "License": "MIT License",
+ "Organization": "Tsinghua University"
+ },
+ "PlayGround V2": {
+ "Link": "https://huggingface.co/playgroundai/playground-v2-1024px-aesthetic",
+ "License": "Playground v2 Community License",
+ "Organization": "Playground"
+ },
+ "PlayGround V2.5": {
+ "Link": "https://huggingface.co/playgroundai/playground-v2.5-1024px-aesthetic",
+ "License": "Playground v2.5 Community License",
+ "Organization": "Playground"
+ },
+ "OpenJourney": {
+ "Link": "https://huggingface.co/prompthero/openjourney",
+ "License": "creativeml-openrail-m",
+ "Organization": "PromptHero"
+ },
+ "SDXLTurbo": {
+ "Link": "https://huggingface.co/stabilityai/sdxl-turbo",
+ "License": "sai-nc-community (other)",
+ "Organization": "Stability AI"
+ },
+ "SDXL": {
+ "Link": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0",
+ "License": "openrail++",
+ "Organization": "Stability AI"
+ },
+ "PixArtAlpha": {
+ "Link": "https://huggingface.co/PixArt-alpha/PixArt-XL-2-1024-MS",
+ "License": "openrail++",
+ "Organization": "PixArt-alpha"
+ },
+ "SDXLLightning": {
+ "Link": "https://huggingface.co/ByteDance/SDXL-Lightning",
+ "License": "openrail++",
+ "Organization": "ByteDance"
+ },
+ "StableCascade": {
+ "Link": "https://huggingface.co/stabilityai/stable-cascade",
+ "License": "stable-cascade-nc-community (other)",
+ "Organization": "Stability AI"
+ },
+ "LCM(v1.5/XL)": {
+ "Link": "https://fal.ai/models/fal-ai/fast-lcm-diffusion/api",
+ "License": "openrail++",
+ "Organization": "Latent Consistency"
+ },
+ "PixArtSigma": {
+ "Link": "https://fal.ai/models/fal-ai/pixart-sigma",
+ "License": "openrail++",
+ "Organization": "PixArt-alpha"
+ }
+}
diff --git a/arena_elo/get_latest_data.sh b/arena_elo/get_latest_data.sh
new file mode 100644
index 0000000000000000000000000000000000000000..e1d9229b5267ac89d5dba442d78a3a04e7829547
--- /dev/null
+++ b/arena_elo/get_latest_data.sh
@@ -0,0 +1,17 @@
+
+# set LOGDIR to default if not set before
+if [ -z "$LOGDIR" ]; then
+ export LOGDIR="./vision-arena-logs"
+fi
+mkdir -p results
+
+
+# # for battle data
+python -m elo_rating.clean_battle_data --model_infos_file "./model_infos.json" --mode conv_release
+battle_cutoff_date=`cat cut_off_date.txt` && rm cut_off_date.txt && echo "Battle data last updated on $battle_cutoff_date"
+
+mkdir -p ./results/latest
+mkdir -p ./results/$battle_cutoff_date && mv ./clean_battle_conv_$battle_cutoff_date.json ./results/$battle_cutoff_date/clean_battle_conv.json
+cp ./results/$battle_cutoff_date/clean_battle_conv.json ./results/latest/clean_battle_conv.json
+
+echo "Battle data last updated on $battle_cutoff_date" >> ./results/latest/latest_updated_date.txt
diff --git a/arena_elo/pyproject.toml b/arena_elo/pyproject.toml
new file mode 100644
index 0000000000000000000000000000000000000000..215c759e4659b366073cf278ee17b67e3960f181
--- /dev/null
+++ b/arena_elo/pyproject.toml
@@ -0,0 +1,28 @@
+[build-system]
+requires = ["setuptools>=61.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "arena_elo"
+version = "0.2.35"
+description = "Elo rating system for WildVision Bench Arena"
+readme = "README.md"
+requires-python = ">=3.9"
+classifiers = [
+ "Programming Language :: Python :: 3",
+ "License :: OSI Approved :: Apache Software License",
+]
+dependencies = [
+ "numpy", "prompt_toolkit>=3.0.0", "uvicorn","polyglot", "pyicu", "pycld2", "morfessor", "scikit-learn",
+ "pytz", "tqdm", "pandas", "plotly", "fire", "Pillow"
+]
+
+[project.urls]
+"Homepage" = "https://github.com/WildVision-Bench/Arena-Elo"
+"Bug Tracker" = "https://github.com/WildVision-Bench/Arena-Elo/issues"
+
+[tool.setuptools.packages.find]
+exclude = ["assets*", "benchmark*", "docs", "dist*", "playground*", "scripts*", "tests*"]
+
+[tool.wheel]
+exclude = ["assets*", "benchmark*", "docs", "dist*", "playground*", "scripts*", "tests*"]
\ No newline at end of file
diff --git a/arena_elo/requirements.txt b/arena_elo/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0b55ee1857cb4321f1d15bf82ac4d19d2f02fb9e
--- /dev/null
+++ b/arena_elo/requirements.txt
@@ -0,0 +1,28 @@
+-e git+https://github.com/WildVision-Bench/Arena-Elo.git@9dc2fa8543a2e9eda3d5bc01c2212fdfcdd4bfb5#egg=arena_elo
+click==8.1.7
+fire==0.5.0
+h11==0.14.0
+joblib==1.3.2
+Morfessor==2.0.6
+numpy==1.26.4
+packaging==23.2
+pandas==2.2.0
+pillow==10.2.0
+plotly==5.18.0
+polyglot==16.7.4
+prompt-toolkit==3.0.43
+pycld2==0.41
+PyICU==2.12
+python-dateutil==2.8.2
+pytz==2024.1
+scikit-learn==1.4.0
+scipy==1.12.0
+six==1.16.0
+tenacity==8.2.3
+termcolor==2.4.0
+threadpoolctl==3.2.0
+tqdm==4.66.2
+typing_extensions==4.9.0
+tzdata==2024.1
+uvicorn==0.27.1
+wcwidth==0.2.13
diff --git a/arena_elo/results/latest/clean_battle_image_editing.json b/arena_elo/results/latest/clean_battle_image_editing.json
new file mode 100644
index 0000000000000000000000000000000000000000..532cb34d718ec89c761eb0f947f85bb1550cd040
--- /dev/null
+++ b/arena_elo/results/latest/clean_battle_image_editing.json
@@ -0,0 +1,1578 @@
+[
+ {
+ "model_a": "CycleDiffusion",
+ "model_b": "InstructPix2Pix",
+ "winner": "model_b",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707712630.872
+ },
+ {
+ "model_a": "CycleDiffusion",
+ "model_b": "InstructPix2Pix",
+ "winner": "model_b",
+ "judge": "arena_user_::1",
+ "anony": false,
+ "tstamp": 1707712699.668
+ },
+ {
+ "model_a": "Pix2PixZero",
+ "model_b": "MagicBrush",
+ "winner": "model_a",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707712896.0427
+ },
+ {
+ "model_a": "CycleDiffusion",
+ "model_b": "InstructPix2Pix",
+ "winner": "model_b",
+ "judge": "arena_user_::1",
+ "anony": false,
+ "tstamp": 1707712929.7061
+ },
+ {
+ "model_a": "CycleDiffusion",
+ "model_b": "InstructPix2Pix",
+ "winner": "model_b",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707713147.0445
+ },
+ {
+ "model_a": "CycleDiffusion",
+ "model_b": "PNP",
+ "winner": "model_b",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707713198.9284
+ },
+ {
+ "model_a": "CycleDiffusion",
+ "model_b": "Prompt2prompt",
+ "winner": "model_b",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707713210.1306
+ },
+ {
+ "model_a": "Prompt2prompt",
+ "model_b": "SDEdit",
+ "winner": "model_a",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707713747.5115
+ },
+ {
+ "model_a": "PNP",
+ "model_b": "Pix2PixZero",
+ "winner": "model_a",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707715613.7226
+ },
+ {
+ "model_a": "CycleDiffusion",
+ "model_b": "MagicBrush",
+ "winner": "model_b",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707765708.2644
+ },
+ {
+ "model_a": "PNP",
+ "model_b": "CycleDiffusion",
+ "winner": "model_a",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707765861.2742
+ },
+ {
+ "model_a": "PNP",
+ "model_b": "CycleDiffusion",
+ "winner": "model_a",
+ "judge": "arena_user_::1",
+ "anony": false,
+ "tstamp": 1707765975.0206
+ },
+ {
+ "model_a": "PNP",
+ "model_b": "CycleDiffusion",
+ "winner": "model_a",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707768866.9065
+ },
+ {
+ "model_a": "SDEdit",
+ "model_b": "MagicBrush",
+ "winner": "model_b",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707771673.2989
+ },
+ {
+ "model_a": "SDEdit",
+ "model_b": "MagicBrush",
+ "winner": "model_b",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707784377.6617
+ },
+ {
+ "model_a": "SDEdit",
+ "model_b": "MagicBrush",
+ "winner": "model_b",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707784466.8915
+ },
+ {
+ "model_a": "CycleDiffusion",
+ "model_b": "PNP",
+ "winner": "model_b",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707784983.9581
+ },
+ {
+ "model_a": "MagicBrush",
+ "model_b": "SDEdit",
+ "winner": "model_a",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707785277.16
+ },
+ {
+ "model_a": "MagicBrush",
+ "model_b": "SDEdit",
+ "winner": "model_a",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707795299.0619
+ },
+ {
+ "model_a": "MagicBrush",
+ "model_b": "SDEdit",
+ "winner": "tie (bothbad)",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707795798.752
+ },
+ {
+ "model_a": "SDEdit",
+ "model_b": "Prompt2prompt",
+ "winner": "model_b",
+ "judge": "arena_user_::1",
+ "anony": false,
+ "tstamp": 1707796435.7996
+ },
+ {
+ "model_a": "SDEdit",
+ "model_b": "CycleDiffusion",
+ "winner": "model_b",
+ "judge": "arena_user_::1",
+ "anony": false,
+ "tstamp": 1707797278.7369
+ },
+ {
+ "model_a": "SDEdit",
+ "model_b": "CycleDiffusion",
+ "winner": "model_a",
+ "judge": "arena_user_::1",
+ "anony": false,
+ "tstamp": 1707797279.6004
+ },
+ {
+ "model_a": "SDEdit",
+ "model_b": "Prompt2prompt",
+ "winner": "model_b",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707805086.9739
+ },
+ {
+ "model_a": "PNP",
+ "model_b": "SDEdit",
+ "winner": "model_a",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707805220.3253
+ },
+ {
+ "model_a": "InstructPix2Pix",
+ "model_b": "CycleDiffusion",
+ "winner": "tie (bothbad)",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707805332.6322
+ },
+ {
+ "model_a": "InstructPix2Pix",
+ "model_b": "Prompt2prompt",
+ "winner": "model_b",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707805476.0509
+ },
+ {
+ "model_a": "InstructPix2Pix",
+ "model_b": "Prompt2prompt",
+ "winner": "model_b",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707818374.3438
+ },
+ {
+ "model_a": "PNP",
+ "model_b": "Prompt2prompt",
+ "winner": "model_b",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707834631.9088
+ },
+ {
+ "model_a": "InstructPix2Pix",
+ "model_b": "SDEdit",
+ "winner": "model_a",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707834954.0147
+ },
+ {
+ "model_a": "Prompt2prompt",
+ "model_b": "Pix2PixZero",
+ "winner": "tie (bothbad)",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707835366.544
+ },
+ {
+ "model_a": "PNP",
+ "model_b": "SDEdit",
+ "winner": "model_a",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707835643.6178
+ },
+ {
+ "model_a": "MagicBrush",
+ "model_b": "InstructPix2Pix",
+ "winner": "tie (bothbad)",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707835789.25
+ },
+ {
+ "model_a": "MagicBrush",
+ "model_b": "PNP",
+ "winner": "tie (bothbad)",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707836852.671
+ },
+ {
+ "model_a": "MagicBrush",
+ "model_b": "InstructPix2Pix",
+ "winner": "model_a",
+ "judge": "arena_user_::1",
+ "anony": false,
+ "tstamp": 1707836952.6082
+ },
+ {
+ "model_a": "CycleDiffusion",
+ "model_b": "SDEdit",
+ "winner": "tie (bothbad)",
+ "judge": "arena_user_::1",
+ "anony": false,
+ "tstamp": 1707837020.7148
+ },
+ {
+ "model_a": "InstructPix2Pix",
+ "model_b": "PNP",
+ "winner": "model_a",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707837226.2259
+ },
+ {
+ "model_a": "Prompt2prompt",
+ "model_b": "Pix2PixZero",
+ "winner": "model_a",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707838166.1449
+ },
+ {
+ "model_a": "InstructPix2Pix",
+ "model_b": "MagicBrush",
+ "winner": "tie (bothbad)",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707838405.0013
+ },
+ {
+ "model_a": "MagicBrush",
+ "model_b": "CycleDiffusion",
+ "winner": "model_a",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707839133.3126
+ },
+ {
+ "model_a": "Prompt2prompt",
+ "model_b": "InstructPix2Pix",
+ "winner": "model_a",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707839484.6824
+ },
+ {
+ "model_a": "PNP",
+ "model_b": "InstructPix2Pix",
+ "winner": "tie (bothbad)",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707850104.2499
+ },
+ {
+ "model_a": "InstructPix2Pix",
+ "model_b": "Pix2PixZero",
+ "winner": "model_a",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707851384.7689
+ },
+ {
+ "model_a": "PNP",
+ "model_b": "MagicBrush",
+ "winner": "model_b",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707851936.9466
+ },
+ {
+ "model_a": "CycleDiffusion",
+ "model_b": "MagicBrush",
+ "winner": "tie (bothbad)",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707852836.3291
+ },
+ {
+ "model_a": "CycleDiffusion",
+ "model_b": "MagicBrush",
+ "winner": "tie (bothbad)",
+ "judge": "arena_user_::1",
+ "anony": false,
+ "tstamp": 1707852878.673
+ },
+ {
+ "model_a": "Prompt2prompt",
+ "model_b": "InstructPix2Pix",
+ "winner": "model_a",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707853008.1359
+ },
+ {
+ "model_a": "InstructPix2Pix",
+ "model_b": "Pix2PixZero",
+ "winner": "model_a",
+ "judge": "arena_user_::1",
+ "anony": false,
+ "tstamp": 1707856807.6229
+ },
+ {
+ "model_a": "MagicBrush",
+ "model_b": "Pix2PixZero",
+ "winner": "tie (bothbad)",
+ "judge": "arena_user_::1",
+ "anony": false,
+ "tstamp": 1707863740.3507
+ },
+ {
+ "model_a": "MagicBrush",
+ "model_b": "PNP",
+ "winner": "model_b",
+ "judge": "arena_user_::1",
+ "anony": true,
+ "tstamp": 1707866312.1118
+ },
+ {
+ "model_a": "Pix2PixZero",
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diff --git a/arena_elo/results/latest/clean_battle_t2i_generation.json b/arena_elo/results/latest/clean_battle_t2i_generation.json
new file mode 100644
index 0000000000000000000000000000000000000000..aa70015940ec05b308506bc6488b598b5d3a1188
--- /dev/null
+++ b/arena_elo/results/latest/clean_battle_t2i_generation.json
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:93808a9ce2f497109d0fc708e4055b6463a692502ef541ff28352f52b612916d
+size 68172
diff --git a/arena_elo/results/latest/image_editing_leaderboard.csv b/arena_elo/results/latest/image_editing_leaderboard.csv
new file mode 100644
index 0000000000000000000000000000000000000000..db61e6d7ea46c54dac3579fab71a81032ba56274
--- /dev/null
+++ b/arena_elo/results/latest/image_editing_leaderboard.csv
@@ -0,0 +1,10 @@
+key,Model,Arena Elo rating (anony),Arena Elo rating (full),License,Organization,Link
+CosXLEdit,CosXLEdit,1097.63559213644,1085.7285800995926,cosxl-nc-community,Stability AI,https://huggingface.co/spaces/multimodalart/cosxl
+MagicBrush,MagicBrush,1075.1489922450316,1086.8819832924794,CC-BY-4.0,"The Ohio State University, University of Waterloo",https://osu-nlp-group.github.io/MagicBrush
+InfEdit,InfEdit,1065.4719519196174,1090.684638162955,Apache-2.0,"University of Michigan, University of California, Berkeley",https://huggingface.co/spaces/sled-umich/InfEdit
+Prompt2prompt,Prompt2prompt,1063.1432047252297,1060.8146250689238,Apache-2.0,"Google, Tel Aviv University",https://prompt-to-prompt.github.io
+InstructPix2Pix,InstructPix2Pix,1043.9312648233226,1028.7932718869638,"Copyright 2023 Timothy Brooks, Aleksander Holynski, Alexei A. Efros","University of California, Berkeley",https://www.timothybrooks.com/instruct-pix2pix
+PNP,PNP,1022.4342554377677,1043.322342347598,-,Weizmann Institute of Science,https://github.com/MichalGeyer/plug-and-play
+Pix2PixZero,Pix2PixZero,891.2979039265506,886.7359371585381,MIT License,"Carnegie Mellon University, Adobe Research",https://pix2pixzero.github.io
+SDEdit,SDEdit,890.443823405714,880.5508125882768,MIT License,Stanford University,https://sde-image-editing.github.io
+CycleDiffusion,CycleDiffusion,850.4930113803264,836.4878093946726,X11,Carnegie Mellon University,https://github.com/ChenWu98/cycle-diffusion
diff --git a/arena_elo/results/latest/t2i_generation_leaderboard.csv b/arena_elo/results/latest/t2i_generation_leaderboard.csv
new file mode 100644
index 0000000000000000000000000000000000000000..665d340a9368e016ca65ab96f829ce3b899c70c9
--- /dev/null
+++ b/arena_elo/results/latest/t2i_generation_leaderboard.csv
@@ -0,0 +1,12 @@
+key,Model,Arena Elo rating (anony),Arena Elo rating (full),License,Organization,Link
+SDXL,SDXL,0,0,openrail++,Stability AI,https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0
+SDXLTurbo,SDXLTurbo,0,0,sai-nc-community (other),Stability AI,https://huggingface.co/stabilityai/sdxl-turbo
+LCM(v1.5/XL),LCM(v1.5/XL),0,0,openrail++,Latent Consistency,https://fal.ai/models/fal-ai/fast-lcm-diffusion/api
+OpenJourney,OpenJourney,0,0,creativeml-openrail-m,PromptHero,https://huggingface.co/prompthero/openjourney
+LCM,LCM,0,0,MIT License,Tsinghua University,https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7
+PixArtAlpha,PixArtAlpha,0,0,openrail++,PixArt-alpha,https://huggingface.co/PixArt-alpha/PixArt-XL-2-1024-MS
+PixArtSigma,PixArtSigma,0,0,openrail++,PixArt-alpha,https://fal.ai/models/fal-ai/pixart-sigma
+StableCascade,StableCascade,0,0,stable-cascade-nc-community (other),Stability AI,https://huggingface.co/stabilityai/stable-cascade
+PlayGround V2.5,PlayGround V2.5,0,0,Playground v2.5 Community License,Playground,https://huggingface.co/playgroundai/playground-v2.5-1024px-aesthetic
+PlayGround V2,PlayGround V2,0,0,Playground v2 Community License,Playground,https://huggingface.co/playgroundai/playground-v2-1024px-aesthetic
+SDXLLightning,SDXLLightning,0,0,openrail++,ByteDance,https://huggingface.co/ByteDance/SDXL-Lightning
\ No newline at end of file
diff --git a/arena_elo/simple_test.py b/arena_elo/simple_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e52ac57444e7fc3e53991379de196e3e9844955
--- /dev/null
+++ b/arena_elo/simple_test.py
@@ -0,0 +1,16 @@
+import pickle
+with open("./results/latest/elo_results.pkl",'rb') as f:
+ data = pickle.load(f)
+ print()
+ df = data["anony"]["leaderboard_table_df"]
+ # sort by rating
+ df = df.sort_values(by=["rating"], ascending=False)
+ print(df)
+
+ print()
+
+ df = data["full"]["leaderboard_table_df"]
+ # sort by rating
+ df = df.sort_values(by=["rating"], ascending=False)
+ print(df)
+ print('done')
\ No newline at end of file
diff --git a/arena_elo/update_elo_rating.sh b/arena_elo/update_elo_rating.sh
new file mode 100644
index 0000000000000000000000000000000000000000..f7d5e5d953dfc842cc0c2ce2ef853add86fd86fc
--- /dev/null
+++ b/arena_elo/update_elo_rating.sh
@@ -0,0 +1,49 @@
+# set LOGDIR to default if not set before
+if [ -z "$LOGDIR" ]; then
+ echo "LOGDIR is not set. Using default '../GenAI-Arena-hf-logs/vote_log'"
+ export LOGDIR="../GenAI-Arena-hf-logs/vote_log"
+fi
+
+mkdir -p results
+
+# # for battle data
+python -m elo_rating.clean_battle_data --task_name "image_editing"
+edition_battle_cutoff_date=`cat cut_off_date.txt` && rm cut_off_date.txt && echo "Image editing battle data last updated on $edition_battle_cutoff_date"
+
+python -m elo_rating.clean_battle_data --task_name "t2i_generation"
+generation_battle_cutoff_date=`cat cut_off_date.txt` && rm cut_off_date.txt && echo "T2I image generation battle data last updated on $generation_battle_cutoff_date"
+
+mkdir -p ./results/$edition_battle_cutoff_date
+mkdir -p ./results/$generation_battle_cutoff_date
+
+cp clean_battle_image_editing_$edition_battle_cutoff_date.json ./results/latest/clean_battle_image_editing.json
+cp clean_battle_t2i_generation_$generation_battle_cutoff_date.json ./results/latest/clean_battle_t2i_generation.json
+mv clean_battle_image_editing_$edition_battle_cutoff_date.json ./results/$edition_battle_cutoff_date/clean_battle_image_editing.json
+mv clean_battle_t2i_generation_$generation_battle_cutoff_date.json ./results/$generation_battle_cutoff_date/clean_battle_t2i_generation.json
+
+
+python3 -m elo_rating.elo_analysis --clean-battle-file ./results/$edition_battle_cutoff_date/clean_battle_image_editing.json
+mv ./elo_results_$edition_battle_cutoff_date.pkl ./results/$edition_battle_cutoff_date/elo_results_image_editing.pkl
+
+python3 -m elo_rating.elo_analysis --clean-battle-file ./results/$generation_battle_cutoff_date/clean_battle_t2i_generation.json
+mv ./elo_results_$generation_battle_cutoff_date.pkl ./results/$generation_battle_cutoff_date/elo_results_t2i_generation.pkl
+
+# generat the leaderboard
+
+python -m elo_rating.generate_leaderboard \
+ --model_info_file "./edition_model_info.json" \
+ --elo_rating_pkl "./results/$edition_battle_cutoff_date/elo_results_image_editing.pkl" \
+ --output_csv "./results/$edition_battle_cutoff_date/image_editing_leaderboard.csv"
+
+python -m elo_rating.generate_leaderboard \
+ --model_info_file "./generation_model_info.json" \
+ --elo_rating_pkl "./results/$generation_battle_cutoff_date/elo_results_t2i_generation.pkl" \
+ --output_csv "./results/$generation_battle_cutoff_date/t2i_generation_leaderboard.csv"
+
+mkdir -p ./results/latest
+cp ./results/$edition_battle_cutoff_date/image_editing_leaderboard.csv ./results/latest/image_editing_leaderboard.csv
+cp ./results/$generation_battle_cutoff_date/t2i_generation_leaderboard.csv ./results/latest/t2i_generation_leaderboard.csv
+cp ./results/$edition_battle_cutoff_date/elo_results_image_editing.pkl ./results/latest/elo_results_image_editing.pkl
+cp ./results/$generation_battle_cutoff_date/elo_results_t2i_generation.pkl ./results/latest/elo_results_t2i_generation.pkl
+
+
diff --git a/examples/.DS_Store b/examples/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6
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diff --git a/examples/banana.jpg b/examples/banana.jpg
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diff --git a/examples/cat.jpeg b/examples/cat.jpeg
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diff --git a/examples/city.jpg b/examples/city.jpg
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diff --git a/examples/dog.jpg b/examples/dog.jpg
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diff --git a/examples/duck.jpg b/examples/duck.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..cd8ed15cfca160daa213a97b9dd964c8c55093ed
--- /dev/null
+++ b/examples/duck.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ba499b554d5d6344d72411643f76a678a86fa09d90912507d32b4d8f016fbc7d
+size 1490146
diff --git a/examples/duck_hat.jpg b/examples/duck_hat.jpg
new file mode 100644
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diff --git a/examples/fire.jpg b/examples/fire.jpg
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diff --git a/examples/mouse.jpg b/examples/mouse.jpg
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diff --git a/examples/oranges.jpg b/examples/oranges.jpg
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diff --git a/examples/pig.jpg b/examples/pig.jpg
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diff --git a/examples/rabbit.jpg b/examples/rabbit.jpg
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diff --git a/examples/strawberries.jpg b/examples/strawberries.jpg
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diff --git a/model/__init__.py b/model/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/model/fetch_museum_results/__init__.py b/model/fetch_museum_results/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d81c75a1dafd13bbd4962a2d3f9e7659aa0d834
--- /dev/null
+++ b/model/fetch_museum_results/__init__.py
@@ -0,0 +1,62 @@
+from .imagen_museum import TASK_DICT, DOMAIN
+from .imagen_museum import fetch_indexes
+import random
+
+ARENA_TO_IG_MUSEUM = {"LCM(v1.5/XL)":"LCM",
+ "PlayGroundV2.5": "PlayGroundV2_5"}
+
+def draw2_from_imagen_museum(task, model_name1, model_name2, model_name3, model_name4):
+ task_name = TASK_DICT[task]
+ model_name1 = ARENA_TO_IG_MUSEUM[model_name1] if model_name1 in ARENA_TO_IG_MUSEUM else model_name1
+ model_name2 = ARENA_TO_IG_MUSEUM[model_name2] if model_name2 in ARENA_TO_IG_MUSEUM else model_name2
+ model_name3 = ARENA_TO_IG_MUSEUM[model_name3] if model_name3 in ARENA_TO_IG_MUSEUM else model_name3
+ model_name4 = ARENA_TO_IG_MUSEUM[model_name4] if model_name4 in ARENA_TO_IG_MUSEUM else model_name4
+
+ domain = DOMAIN
+ baselink = domain + task_name
+
+ matched_results = fetch_indexes(baselink)
+ r = random.Random()
+ uid, value = r.choice(list(matched_results.items()))
+ image_link_1 = baselink + "/" + model_name1 + "/" + uid
+ image_link_2 = baselink + "/" + model_name2 + "/" + uid
+ image_link_3 = baselink + "/" + model_name3 + "/" + uid
+ image_link_4 = baselink + "/" + model_name4 + "/" + uid
+
+ if task == "t2i": # Image Gen
+ prompt = value['prompt']
+ return [[image_link_1, image_link_2, image_link_3, image_link_4], [prompt]]
+ if task == "tie": # Image Edit
+ instruction = value['instruction']
+ input_caption = value['source_global_caption']
+ output_caption = value['target_global_caption']
+ source_image_link = baselink + "/" + "input" + "/" + uid
+ return [[source_image_link, image_link_1, image_link_2, image_link_3, image_link_4], [input_caption, output_caption, instruction]]
+ else:
+ raise ValueError("Task not supported")
+
+def draw_from_imagen_museum(task, model_name):
+ task_name = TASK_DICT[task]
+ model_name = ARENA_TO_IG_MUSEUM[model_name] if model_name in ARENA_TO_IG_MUSEUM else model_name
+
+ domain = DOMAIN
+ baselink = domain + task_name
+
+ matched_results = fetch_indexes(baselink)
+ r = random.Random()
+ uid, value = r.choice(list(matched_results.items()))
+ model = model_name
+ image_link = baselink + "/" + model + "/" + uid
+ print(image_link)
+
+ if task == "t2i": # Image Gen
+ prompt = value['prompt']
+ return [image_link, prompt]
+ if task == "tie": # Image Edit
+ instruction = value['instruction']
+ input_caption = value['source_global_caption']
+ output_caption = value['target_global_caption']
+ source_image_link = baselink + "/" + "input" + "/" + uid
+ return [[source_image_link, image_link], [input_caption, output_caption, instruction]]
+ else:
+ raise ValueError("Task not supported")
diff --git a/model/fetch_museum_results/imagen_museum/__init__.py b/model/fetch_museum_results/imagen_museum/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f5414bd2dcf42fe73c0a0ca973357f3ab22b0fc1
--- /dev/null
+++ b/model/fetch_museum_results/imagen_museum/__init__.py
@@ -0,0 +1,129 @@
+import csv
+import requests
+from io import StringIO
+from typing import Union, Optional, Tuple
+from PIL import Image
+import random
+
+__version__ = "0.0.1_GenAI_Arena"
+
+DOMAIN = "https://chromaica.github.io/Museum/"
+
+TASK_DICT = {
+ "t2i": "ImagenHub_Text-Guided_IG",
+ "tie": "ImagenHub_Text-Guided_IE",
+ "mie": "ImagenHub_Control-Guided_IG",
+ "cig": "ImagenHub_Control-Guided_IE",
+ "msdig": "ImagenHub_Multi-Concept_IC",
+ "sdig": "ImagenHub_Subject-Driven_IG",
+ "sdie": "ImagenHub_Subject-Driven_IE"
+}
+
+t2i_models= [
+ "SD",
+ "SDXL",
+ "OpenJourney",
+ "DeepFloydIF",
+ "DALLE2"
+ ]
+
+mie_models = [
+ "Glide",
+ "SDInpaint",
+ "BlendedDiffusion",
+ "SDXLInpaint"
+ ]
+
+tie_models = [
+ "DiffEdit",
+ "MagicBrush",
+ "InstructPix2Pix",
+ "Prompt2prompt",
+ "Text2Live",
+ "SDEdit",
+ "CycleDiffusion",
+ "Pix2PixZero"
+ ]
+
+sdig_models = [
+ "DreamBooth",
+ "DreamBoothLora",
+ "TextualInversion",
+ "BLIPDiffusion_Gen"
+ ]
+
+sdie_models = [
+ "PhotoSwap",
+ "DreamEdit",
+ "BLIPDiffusion_Edit"
+ ]
+
+msdig_models = [
+ "DreamBooth",
+ "CustomDiffusion",
+ "TextualInversion"
+ ]
+
+cig_models = [
+ "ControlNet",
+ "UniControl"
+ ]
+
+def fetch_csv_keys(url):
+ """
+ Fetches a CSV file from a given URL and parses it into a list of keys,
+ ignoring the header line.
+ """
+ response = requests.get(url)
+ response.raise_for_status() # Ensure we notice bad responses
+
+ # Use StringIO to turn the fetched text data into a file-like object
+ csv_file = StringIO(response.text)
+
+ # Create a CSV reader
+ csv_reader = csv.reader(csv_file)
+
+ # Skip the header
+ next(csv_reader, None)
+
+ # Return the list of keys
+ return [row[0] for row in csv_reader if row]
+
+def fetch_json_data(url):
+ """
+ Fetches JSON data from a given URL.
+ """
+ response = requests.get(url)
+ response.raise_for_status()
+ return response.json()
+
+def fetch_data_and_match(csv_url, json_url):
+ """
+ Fetches a list of keys from a CSV and then fetches JSON data and matches the keys to the JSON.
+ """
+ # Fetch keys from CSV
+ keys = fetch_csv_keys(csv_url)
+
+ # Fetch JSON data
+ json_data = fetch_json_data(json_url)
+
+ # Extract relevant data using keys
+ matched_data = {key: json_data.get(key) for key in keys if key in json_data}
+
+ return matched_data
+
+def fetch_indexes(baselink):
+ matched_results = fetch_data_and_match(baselink+"/dataset_lookup.csv", baselink+"/dataset_lookup.json")
+ return matched_results
+
+if __name__ == "__main__":
+ domain = "https://chromaica.github.io/Museum/"
+ baselink = domain + "ImagenHub_Text-Guided_IE"
+ matched_results = fetch_indexes(baselink)
+ for uid, value in matched_results.items():
+ print(uid)
+ model = "CycleDiffusion"
+ image_link = baselink + "/" + model + "/" + uid
+ print(image_link)
+ instruction = value['instruction']
+ print(instruction)
diff --git a/model/matchmaker.py b/model/matchmaker.py
new file mode 100644
index 0000000000000000000000000000000000000000..cdcba9d35a460e881a880f5decd9d4145531b166
--- /dev/null
+++ b/model/matchmaker.py
@@ -0,0 +1,83 @@
+import numpy as np
+import json
+from trueskill import TrueSkill
+import paramiko
+import io, os
+import sys
+sys.path.append('../')
+from serve.constants import SSH_SERVER, SSH_PORT, SSH_USER, SSH_PASSWORD, SSH_SKILL, SSH_SKILL_M
+
+
+trueskill_env = TrueSkill()
+
+def ucb_score(trueskill_diff, t, n):
+ exploration_term = np.sqrt((2 * np.log(t + 1e-5)) / (n + 1e-5))
+ ucb = -trueskill_diff + 1.0 * exploration_term
+ return ucb
+
+def update_trueskill(ratings, ranks):
+ new_ratings = trueskill_env.rate(ratings, ranks)
+ return new_ratings
+
+def serialize_rating(rating):
+ return {'mu': rating.mu, 'sigma': rating.sigma}
+
+def deserialize_rating(rating_dict):
+ return trueskill_env.Rating(mu=rating_dict['mu'], sigma=rating_dict['sigma'])
+
+def create_ssh_client(server, port, user, password):
+ ssh = paramiko.SSHClient()
+ ssh.load_system_host_keys()
+ ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())
+ ssh.connect(server, port, user, password)
+ return ssh
+
+def save_json_via_sftp(ratings, comparison_counts, total_comparisons):
+ ssh = create_ssh_client(SSH_SERVER, SSH_PORT, SSH_USER, SSH_PASSWORD)
+ data = {
+ 'ratings': [serialize_rating(r) for r in ratings],
+ 'comparison_counts': comparison_counts.tolist(),
+ 'total_comparisons': total_comparisons
+ }
+ json_data = json.dumps(data)
+ sftp = ssh.open_sftp()
+ with sftp.open(SSH_SKILL, 'w') as f:
+ f.write(json_data)
+
+def load_json_via_sftp():
+ ssh = create_ssh_client(SSH_SERVER, SSH_PORT, SSH_USER, SSH_PASSWORD)
+ sftp = ssh.open_sftp()
+ with sftp.open(SSH_SKILL, 'r') as f:
+ data = json.load(f)
+ ratings = [deserialize_rating(r) for r in data['ratings']]
+ comparison_counts = np.array(data['comparison_counts'])
+ total_comparisons = data['total_comparisons']
+ return ratings, comparison_counts, total_comparisons
+
+
+def matchmaker(num_players, k_group=4):
+ trueskill_env = TrueSkill()
+
+ ratings, comparison_counts, total_comparisons = load_json_via_sftp()
+
+ # Randomly select a player
+ selected_player = np.random.randint(0, num_players)
+
+ selected_trueskill_score = trueskill_env.expose(ratings[selected_player])
+ trueskill_scores = np.array([trueskill_env.expose(p) for p in ratings])
+ trueskill_diff = np.abs(trueskill_scores - selected_trueskill_score)
+ n = comparison_counts[selected_player]
+ ucb_scores = ucb_score(trueskill_diff, total_comparisons, n)
+
+ # Exclude self, select opponent with highest UCB score
+ ucb_scores[selected_player] = -float('inf') # minimize the score for the selected player to exclude it
+ opponents = np.argsort(ucb_scores)[-k_group + 1:].tolist()
+
+ # Group players
+ model_ids = [selected_player] + opponents
+
+ from serve.update_skill import Model_ID
+ Model_ID.group = model_ids
+
+ return model_ids
+
diff --git a/model/model_manager.py b/model/model_manager.py
new file mode 100644
index 0000000000000000000000000000000000000000..ebd21b2b8469d6bfe0e3ecf47b6e90c386a7ffbd
--- /dev/null
+++ b/model/model_manager.py
@@ -0,0 +1,168 @@
+import concurrent.futures
+import random
+import gradio as gr
+import requests
+import io, base64, json
+import spaces
+from PIL import Image
+from .models import IMAGE_GENERATION_MODELS, IMAGE_EDITION_MODELS, load_pipeline
+from .fetch_museum_results import draw_from_imagen_museum, draw2_from_imagen_museum
+
+class ModelManager:
+ def __init__(self):
+ self.model_ig_list = IMAGE_GENERATION_MODELS
+ self.model_ie_list = IMAGE_EDITION_MODELS
+ self.loaded_models = {}
+
+ def load_model_pipe(self, model_name):
+ if not model_name in self.loaded_models:
+ pipe = load_pipeline(model_name)
+ self.loaded_models[model_name] = pipe
+ else:
+ pipe = self.loaded_models[model_name]
+ return pipe
+
+ @spaces.GPU(duration=120)
+ def generate_image_ig(self, prompt, model_name):
+ pipe = self.load_model_pipe(model_name)
+ result = pipe(prompt=prompt)
+ return result
+
+ def generate_image_ig_api(self, prompt, model_name):
+ pipe = self.load_model_pipe(model_name)
+ result = pipe(prompt=prompt)
+ return result
+
+ def generate_image_ig_museum(self, model_name):
+ model_name = model_name.split('_')[1]
+ result_list = draw_from_imagen_museum("t2i", model_name)
+ image_link = result_list[0]
+ prompt = result_list[1]
+
+ return image_link, prompt
+
+
+ def generate_image_ig_parallel_anony(self, prompt, model_A, model_B, model_C, model_D):
+ if model_A == "" and model_B == "" and model_C == "" and model_D == "":
+ # model_names = random.sample([model for model in self.model_ig_list], 4)
+ from .matchmaker import matchmaker
+ model_ids = matchmaker(num_players=len(self.model_ig_list))
+ model_names = [self.model_ig_list[i] for i in model_ids]
+ else:
+ model_names = [model_A, model_B, model_C, model_D]
+
+ # with concurrent.futures.ThreadPoolExecutor() as executor:
+ # futures = [executor.submit(self.generate_image_ig, prompt, model) if model.startswith("imagenhub")
+ # else executor.submit(self.generate_image_ig_api, prompt, model) for model in model_names]
+ # results = [future.result() for future in futures]
+
+
+ results = [self.generate_image_ig(prompt, model) for model in model_names]
+ # results = [future.result() for future in futures]
+ return results[0], results[1], results[2], results[3], \
+ model_names[0], model_names[1], model_names[2], model_names[3]
+
+ def generate_image_ig_museum_parallel_anony(self, model_A, model_B, model_C, model_D):
+ if model_A == "" and model_B == "" and model_C == "" and model_D == "":
+ # model_names = random.sample([model for model in self.model_ig_list], 4)
+ from .matchmaker import matchmaker
+ model_ids = matchmaker(num_players=len(self.model_ig_list))
+ model_names = [self.model_ig_list[i] for i in model_ids]
+ else:
+ model_names = [model_A, model_B, model_C, model_D]
+
+ with concurrent.futures.ThreadPoolExecutor() as executor:
+ model_1 = model_names[0].split('_')[1]
+ model_2 = model_names[1].split('_')[1]
+ model_3 = model_names[2].split('_')[1]
+ model_4 = model_names[3].split('_')[1]
+
+ result_list = draw2_from_imagen_museum("t2i", model_1, model_2, model_3, model_4)
+ image_links = result_list[0]
+ prompt_list = result_list[1]
+ print(prompt_list[0])
+
+ return image_links[0], image_links[1], image_links[2], image_links[3], \
+ model_names[0], model_names[1], model_names[2], model_names[3], prompt_list[0]
+
+ def generate_image_ig_parallel(self, prompt, model_A, model_B):
+ model_names = [model_A, model_B]
+ with concurrent.futures.ThreadPoolExecutor() as executor:
+ futures = [executor.submit(self.generate_image_ig, prompt, model) if model.startswith("imagenhub")
+ else executor.submit(self.generate_image_ig_api, prompt, model) for model in model_names]
+ results = [future.result() for future in futures]
+ return results[0], results[1]
+
+ def generate_image_ig_museum_parallel(self, model_A, model_B):
+ with concurrent.futures.ThreadPoolExecutor() as executor:
+ model_1 = model_A.split('_')[1]
+ model_2 = model_B.split('_')[1]
+ result_list = draw2_from_imagen_museum("t2i", model_1, model_2)
+ image_links = result_list[0]
+ prompt_list = result_list[1]
+ return image_links[0], image_links[1], prompt_list[0]
+
+
+ @spaces.GPU(duration=200)
+ def generate_image_ie(self, textbox_source, textbox_target, textbox_instruct, source_image, model_name):
+ pipe = self.load_model_pipe(model_name)
+ result = pipe(src_image = source_image, src_prompt = textbox_source, target_prompt = textbox_target, instruct_prompt = textbox_instruct)
+ return result
+
+ def generate_image_ie_museum(self, model_name):
+ model_name = model_name.split('_')[1]
+ result_list = draw_from_imagen_museum("tie", model_name)
+ image_links = result_list[0]
+ prompt_list = result_list[1]
+ # image_links = [src, model]
+ # prompt_list = [source_caption, target_caption, instruction]
+ return image_links[0], image_links[1], prompt_list[0], prompt_list[1], prompt_list[2]
+
+ def generate_image_ie_parallel(self, textbox_source, textbox_target, textbox_instruct, source_image, model_A, model_B):
+ model_names = [model_A, model_B]
+ with concurrent.futures.ThreadPoolExecutor() as executor:
+ futures = [
+ executor.submit(self.generate_image_ie, textbox_source, textbox_target, textbox_instruct, source_image,
+ model) for model in model_names]
+ results = [future.result() for future in futures]
+ return results[0], results[1]
+
+ def generate_image_ie_museum_parallel(self, model_A, model_B):
+ model_names = [model_A, model_B]
+ with concurrent.futures.ThreadPoolExecutor() as executor:
+ model_1 = model_names[0].split('_')[1]
+ model_2 = model_names[1].split('_')[1]
+ result_list = draw2_from_imagen_museum("tie", model_1, model_2)
+ image_links = result_list[0]
+ prompt_list = result_list[1]
+ # image_links = [src, model_A, model_B]
+ # prompt_list = [source_caption, target_caption, instruction]
+ return image_links[0], image_links[1], image_links[2], prompt_list[0], prompt_list[1], prompt_list[2]
+
+ def generate_image_ie_parallel_anony(self, textbox_source, textbox_target, textbox_instruct, source_image, model_A, model_B):
+ if model_A == "" and model_B == "":
+ model_names = random.sample([model for model in self.model_ie_list], 2)
+ else:
+ model_names = [model_A, model_B]
+ with concurrent.futures.ThreadPoolExecutor() as executor:
+ futures = [executor.submit(self.generate_image_ie, textbox_source, textbox_target, textbox_instruct, source_image, model) for model in model_names]
+ results = [future.result() for future in futures]
+ return results[0], results[1], model_names[0], model_names[1]
+
+ def generate_image_ie_museum_parallel_anony(self, model_A, model_B):
+ if model_A == "" and model_B == "":
+ model_names = random.sample([model for model in self.model_ie_list], 2)
+ else:
+ model_names = [model_A, model_B]
+ with concurrent.futures.ThreadPoolExecutor() as executor:
+ model_1 = model_names[0].split('_')[1]
+ model_2 = model_names[1].split('_')[1]
+ result_list = draw2_from_imagen_museum("tie", model_1, model_2)
+ image_links = result_list[0]
+ prompt_list = result_list[1]
+ # image_links = [src, model_A, model_B]
+ # prompt_list = [source_caption, target_caption, instruction]
+ return image_links[0], image_links[1], image_links[2], prompt_list[0], prompt_list[1], prompt_list[2], model_names[0], model_names[1]
+
+
+ raise NotImplementedError
\ No newline at end of file
diff --git a/model/model_registry.py b/model/model_registry.py
new file mode 100644
index 0000000000000000000000000000000000000000..e64464ee7d908f1541289c245763e4291824a1f7
--- /dev/null
+++ b/model/model_registry.py
@@ -0,0 +1,231 @@
+from collections import namedtuple
+from typing import List
+
+ModelInfo = namedtuple("ModelInfo", ["simple_name", "link", "description"])
+model_info = {}
+
+def register_model_info(
+ full_names: List[str], simple_name: str, link: str, description: str
+):
+ info = ModelInfo(simple_name, link, description)
+
+ for full_name in full_names:
+ model_info[full_name] = info
+
+def get_model_info(name: str) -> ModelInfo:
+ if name in model_info:
+ return model_info[name]
+ else:
+ # To fix this, please use `register_model_info` to register your model
+ return ModelInfo(
+ name, "", "Register the description at fastchat/model/model_registry.py"
+ )
+
+def get_model_description_md(model_list):
+ model_description_md = """
+| | | |
+| ---- | ---- | ---- |
+"""
+ ct = 0
+ visited = set()
+ for i, name in enumerate(model_list):
+ minfo = get_model_info(name)
+ if minfo.simple_name in visited:
+ continue
+ visited.add(minfo.simple_name)
+ one_model_md = f"[{minfo.simple_name}]({minfo.link}): {minfo.description}"
+
+ if ct % 3 == 0:
+ model_description_md += "|"
+ model_description_md += f" {one_model_md} |"
+ if ct % 3 == 2:
+ model_description_md += "\n"
+ ct += 1
+ return model_description_md
+
+# regist image generation models
+
+register_model_info(
+ ["imagenhub_LCM_generation", "fal_LCM_text2image"],
+ "LCM",
+ "https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7",
+ "Latent Consistency Models.",
+)
+
+register_model_info(
+ ["fal_LCM(v1.5/XL)_text2image"],
+ "LCM(v1.5/XL)",
+ "https://fal.ai/models/fast-lcm-diffusion-turbo",
+ "Latent Consistency Models (v1.5/XL)",
+)
+
+register_model_info(
+ ["imagenhub_PlayGroundV2_generation", 'playground_PlayGroundV2_generation'],
+ "Playground v2",
+ "https://huggingface.co/playgroundai/playground-v2-1024px-aesthetic",
+ "Playground v2 – 1024px Aesthetic Model",
+)
+
+register_model_info(
+ ["imagenhub_PlayGroundV2.5_generation", 'playground_PlayGroundV2.5_generation'],
+ "Playground v2.5",
+ "https://huggingface.co/playgroundai/playground-v2.5-1024px-aesthetic",
+ "Playground v2.5 is the state-of-the-art open-source model in aesthetic quality",
+)
+
+register_model_info(
+ ["imagenhub_OpenJourney_generation"],
+ "Openjourney",
+ "https://huggingface.co/prompthero/openjourney",
+ "Openjourney is an open source Stable Diffusion fine tuned model on Midjourney images, by PromptHero.",
+)
+
+register_model_info(
+ ["imagenhub_SDXLTurbo_generation", "fal_SDXLTurbo_text2image"],
+ "SDXLTurbo",
+ "https://huggingface.co/stabilityai/sdxl-turbo",
+ "SDXL-Turbo is a fast generative text-to-image model.",
+)
+
+register_model_info(
+ ["imagenhub_SDXL_generation", "fal_SDXL_text2image"],
+ "SDXL",
+ "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0",
+ "SDXL is a Latent Diffusion Model that uses two fixed, pretrained text encoders.",
+)
+
+register_model_info(
+ ["imagenhub_PixArtAlpha_generation"],
+ "PixArtAlpha",
+ "https://huggingface.co/PixArt-alpha/PixArt-XL-2-1024-MS",
+ "Pixart-α consists of pure transformer blocks for latent diffusion.",
+)
+
+register_model_info(
+ ["imagenhub_PixArtSigma_generation", "fal_PixArtSigma_text2image"],
+ "PixArtSigma",
+ "https://github.com/PixArt-alpha/PixArt-sigma",
+ "Improved version of Pixart-α.",
+)
+
+register_model_info(
+ ["imagenhub_SDXLLightning_generation", "fal_SDXLLightning_text2image"],
+ "SDXL-Lightning",
+ "https://huggingface.co/ByteDance/SDXL-Lightning",
+ "SDXL-Lightning is a lightning-fast text-to-image generation model.",
+)
+
+register_model_info(
+ ["imagenhub_StableCascade_generation", "fal_StableCascade_text2image"],
+ "StableCascade",
+ "https://huggingface.co/stabilityai/stable-cascade",
+ "StableCascade is built upon the Würstchen architecture and working at a much smaller latent space.",
+)
+
+# regist image edition models
+register_model_info(
+ ["imagenhub_CycleDiffusion_edition"],
+ "CycleDiffusion",
+ "https://github.com/ChenWu98/cycle-diffusion?tab=readme-ov-file",
+ "A latent space for stochastic diffusion models.",
+)
+
+register_model_info(
+ ["imagenhub_Pix2PixZero_edition"],
+ "Pix2PixZero",
+ "https://pix2pixzero.github.io/",
+ "A zero-shot Image-to-Image translation model.",
+)
+
+register_model_info(
+ ["imagenhub_Prompt2prompt_edition"],
+ "Prompt2prompt",
+ "https://prompt-to-prompt.github.io/",
+ "Image Editing with Cross-Attention Control.",
+)
+
+
+register_model_info(
+ ["imagenhub_InstructPix2Pix_edition"],
+ "InstructPix2Pix",
+ "https://www.timothybrooks.com/instruct-pix2pix",
+ "An instruction-based image editing model.",
+)
+
+register_model_info(
+ ["imagenhub_MagicBrush_edition"],
+ "MagicBrush",
+ "https://osu-nlp-group.github.io/MagicBrush/",
+ "Manually Annotated Dataset for Instruction-Guided Image Editing.",
+)
+
+register_model_info(
+ ["imagenhub_PNP_edition"],
+ "PNP",
+ "https://github.com/MichalGeyer/plug-and-play",
+ "Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation.",
+)
+
+register_model_info(
+ ["imagenhub_InfEdit_edition"],
+ "InfEdit",
+ "https://sled-group.github.io/InfEdit/",
+ "Inversion-Free Image Editing with Natural Language.",
+)
+
+register_model_info(
+ ["imagenhub_CosXLEdit_edition"],
+ "CosXLEdit",
+ "https://huggingface.co/stabilityai/cosxl",
+ "An instruction-based image editing model from SDXL.",
+)
+
+register_model_info(
+ ["fal_stable-cascade_text2image"],
+ "StableCascade",
+ "https://fal.ai/models/stable-cascade/api",
+ "StableCascade is a generative model that can generate high-quality images from text prompts.",
+)
+
+register_model_info(
+ ["fal_AnimateDiff_text2video"],
+ "AnimateDiff",
+ "https://fal.ai/models/fast-animatediff-t2v",
+ "AnimateDiff is a text-driven models that produce diverse and personalized animated images.",
+)
+
+register_model_info(
+ ["fal_AnimateDiffTurbo_text2video"],
+ "AnimateDiff Turbo",
+ "https://fal.ai/models/fast-animatediff-t2v-turbo",
+ "AnimateDiff Turbo is a lightning version of AnimateDiff.",
+)
+
+register_model_info(
+ ["videogenhub_LaVie_generation"],
+ "LaVie",
+ "https://github.com/Vchitect/LaVie",
+ "LaVie is a video generation model with cascaded latent diffusion models.",
+)
+
+register_model_info(
+ ["videogenhub_VideoCrafter2_generation"],
+ "VideoCrafter2",
+ "https://ailab-cvc.github.io/videocrafter2/",
+ "VideoCrafter2 is a T2V model that disentangling motion from appearance.",
+)
+
+register_model_info(
+ ["videogenhub_ModelScope_generation"],
+ "ModelScope",
+ "https://arxiv.org/abs/2308.06571",
+ "ModelScope is a a T2V synthesis model that evolves from a T2I synthesis model.",
+)
+
+register_model_info(
+ ["videogenhub_OpenSora_generation"],
+ "OpenSora",
+ "https://github.com/hpcaitech/Open-Sora",
+ "A community-driven opensource implementation of Sora.",
+)
+
diff --git a/model/models/__init__.py b/model/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b0910b500ac40dfd460e4249dff844dfef929ea4
--- /dev/null
+++ b/model/models/__init__.py
@@ -0,0 +1,66 @@
+from .imagenhub_models import load_imagenhub_model
+# from .playground_api import load_playground_model
+from .fal_api_models import load_fal_model
+# from .videogenhub_models import load_videogenhub_model
+# from .huggingface_models import load_huggingface_model
+
+# IMAGE_GENERATION_MODELS = ['huggingface_SD-v1.5_text2image',
+# 'huggingface_SD-v2.1_text2image',
+# 'huggingface_SD-XL-v1.0_text2image',
+# 'huggingface_IF-I-XL-v1.0_text2image',
+# ]
+IMAGE_GENERATION_MODELS = [ 'imagenhub_SD_generation',
+ 'imagenhub_SDXL_generation',
+ 'imagenhub_OpenJourney_generation',
+ 'imagenhub_LCM_generation',
+ 'imagenhub_DeepFloydIF_generation',
+ 'imagenhub_PixArtAlpha_generation',
+ 'imagenhub_Kandinsky_generation',
+ ]
+# IMAGE_GENERATION_MODELS = [ 'imagenhub_SD_generation',
+# 'imagenhub_SD_generation',
+# 'imagenhub_SD_generation',
+# 'imagenhub_SD_generation',
+# ]
+# IMAGE_GENERATION_MODELS = ['fal_LCM(v1.5/XL)_text2image',
+# 'fal_SDXLTurbo_text2image',
+# 'fal_SDXL_text2image',
+# 'imagenhub_PixArtAlpha_generation',
+# 'fal_PixArtSigma_text2image',
+# 'imagenhub_OpenJourney_generation',
+# 'fal_SDXLLightning_text2image',
+# 'fal_StableCascade_text2image',
+# ]
+IMAGE_EDITION_MODELS = ['imagenhub_CycleDiffusion_edition', 'imagenhub_Pix2PixZero_edition', 'imagenhub_Prompt2prompt_edition',
+ 'imagenhub_SDEdit_edition', 'imagenhub_InstructPix2Pix_edition',
+ 'imagenhub_MagicBrush_edition', 'imagenhub_PNP_edition',
+ 'imagenhub_InfEdit_edition', 'imagenhub_CosXLEdit_edition']
+VIDEO_GENERATION_MODELS = ['fal_AnimateDiff_text2video',
+ 'fal_AnimateDiffTurbo_text2video',
+ 'videogenhub_LaVie_generation', 'videogenhub_VideoCrafter2_generation',
+ 'videogenhub_ModelScope_generation', 'videogenhub_OpenSora_generation']
+
+
+def load_pipeline(model_name):
+ """
+ Load a model pipeline based on the model name
+ Args:
+ model_name (str): The name of the model to load, should be of the form {source}_{name}_{type}
+ the source can be either imagenhub or playground
+ the name is the name of the model used to load the model
+ the type is the type of the model, either generation or edition
+ """
+ model_source, model_name, model_type = model_name.split("_")
+ # if model_source == "huggingface":
+ # pipe = load_huggingface_model(model_name)
+ if model_source == "imagenhub":
+ pipe = load_imagenhub_model(model_name, model_type)
+ # elif model_source == "playground":
+ # pipe = load_playground_model(model_name)
+ elif model_source == "fal":
+ pipe = load_fal_model(model_name, model_type)
+ # elif model_source == "videogenhub":
+ # pipe = load_videogenhub_model(model_name)
+ else:
+ raise ValueError(f"Model source {model_source} not supported")
+ return pipe
\ No newline at end of file
diff --git a/model/models/fal_api_models.py b/model/models/fal_api_models.py
new file mode 100644
index 0000000000000000000000000000000000000000..348f5fe1148b86ff7bfbb692c72f6e85c28bdbb1
--- /dev/null
+++ b/model/models/fal_api_models.py
@@ -0,0 +1,103 @@
+import fal_client
+from PIL import Image
+import requests
+import io
+import os
+import base64
+
+FAL_MODEl_NAME_MAP = {"SDXL": "fast-sdxl", "SDXLTurbo": "fast-turbo-diffusion", "SDXLLightning": "fast-lightning-sdxl",
+ "LCM(v1.5/XL)": "fast-lcm-diffusion", "PixArtSigma": "pixart-sigma", "StableCascade": "stable-cascade"}
+
+class FalModel():
+ def __init__(self, model_name, model_type):
+ self.model_name = model_name
+ self.model_type = model_type
+ os.environ['FAL_KEY'] = os.environ['FalAPI']
+
+ def __call__(self, *args, **kwargs):
+ def decode_data_url(data_url):
+ # Find the start of the Base64 encoded data
+ base64_start = data_url.find(",") + 1
+ if base64_start == 0:
+ raise ValueError("Invalid data URL provided")
+
+ # Extract the Base64 encoded data
+ base64_string = data_url[base64_start:]
+
+ # Decode the Base64 string
+ decoded_bytes = base64.b64decode(base64_string)
+
+ return decoded_bytes
+
+ if self.model_type == "text2image":
+ assert "prompt" in kwargs, "prompt is required for text2image model"
+ handler = fal_client.submit(
+ f"fal-ai/{FAL_MODEl_NAME_MAP[self.model_name]}",
+ arguments={
+ "prompt": kwargs["prompt"]
+ },
+ )
+ for event in handler.iter_events(with_logs=True):
+ if isinstance(event, fal_client.InProgress):
+ print('Request in progress')
+ print(event.logs)
+ result = handler.get()
+ print(result)
+ result_url = result['images'][0]['url']
+ if self.model_name in ["SDXLTurbo", "LCM(v1.5/XL)"]:
+ result_url = io.BytesIO(decode_data_url(result_url))
+ result = Image.open(result_url)
+ else:
+ response = requests.get(result_url)
+ result = Image.open(io.BytesIO(response.content))
+ return result
+ elif self.model_type == "image2image":
+ raise NotImplementedError("image2image model is not implemented yet")
+ # assert "image" in kwargs or "image_url" in kwargs, "image or image_url is required for image2image model"
+ # if "image" in kwargs:
+ # image_url = None
+ # pass
+ # handler = fal_client.submit(
+ # f"fal-ai/{self.model_name}",
+ # arguments={
+ # "image_url": image_url
+ # },
+ # )
+ #
+ # for event in handler.iter_events():
+ # if isinstance(event, fal_client.InProgress):
+ # print('Request in progress')
+ # print(event.logs)
+ #
+ # result = handler.get()
+ # return result
+ elif self.model_type == "text2video":
+ assert "prompt" in kwargs, "prompt is required for text2video model"
+ if self.model_name == 'AnimateDiff':
+ fal_model_name = 'fast-animatediff/text-to-video'
+ elif self.model_name == 'AnimateDiffTurbo':
+ fal_model_name = 'fast-animatediff/turbo/text-to-video'
+ else:
+ raise NotImplementedError(f"text2video model of {self.model_name} in fal is not implemented yet")
+ handler = fal_client.submit(
+ f"fal-ai/{fal_model_name}",
+ arguments={
+ "prompt": kwargs["prompt"]
+ },
+ )
+
+ for event in handler.iter_events(with_logs=True):
+ if isinstance(event, fal_client.InProgress):
+ print('Request in progress')
+ print(event.logs)
+
+ result = handler.get()
+ print("result video: ====")
+ print(result)
+ result_url = result['video']['url']
+ return result_url
+ else:
+ raise ValueError("model_type must be text2image or image2image")
+
+def load_fal_model(model_name, model_type):
+ return FalModel(model_name, model_type)
\ No newline at end of file
diff --git a/model/models/huggingface_models.py b/model/models/huggingface_models.py
new file mode 100644
index 0000000000000000000000000000000000000000..c173987672b540040d1213b9e1b02d08a10c1322
--- /dev/null
+++ b/model/models/huggingface_models.py
@@ -0,0 +1,20 @@
+from diffusers import DiffusionPipeline
+import torch
+
+
+
+HF_MODEl_ID = {"SD-v1.5": "runwayml/stable-diffusion-v1-5",
+ "SD-v2.1": "runwayml/stable-diffusion-v2-1",
+ "SD-XL-v1.0": "stabilityai/stable-diffusion-xl-base-1.0",
+ "IF-I-XL-v1.0": "DeepFloyd/IF-I-XL-v1.0"}
+
+
+def load_huggingface_model(model_name):
+ model_id = HF_MODEl_ID[model_name]
+ pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+ pipe = pipe.to("cuda")
+ return pipe
+
+
+for name in ["SD-v1.5", "SD-v2.1", "SD-XL-v1.0", "IF-I-XL-v1.0"]:
+ load_huggingface_model(name)
\ No newline at end of file
diff --git a/model/models/imagenhub_models.py b/model/models/imagenhub_models.py
new file mode 100644
index 0000000000000000000000000000000000000000..8294e2bde981f02787e35a4897aef70652ea8633
--- /dev/null
+++ b/model/models/imagenhub_models.py
@@ -0,0 +1,50 @@
+import imagen_hub
+
+class ImagenHubModel():
+ def __init__(self, model_name):
+ self.model = imagen_hub.load(model_name)
+
+ def __call__(self, *args, **kwargs):
+ return self.model.infer_one_image(*args, **kwargs)
+
+class PNP(ImagenHubModel):
+ def __init__(self):
+ super().__init__('PNP')
+
+ def __call__(self, *args, **kwargs):
+ if "num_inversion_steps" not in kwargs:
+ kwargs["num_inversion_steps"] = 200
+ return super().__call__(*args, **kwargs)
+
+class Prompt2prompt(ImagenHubModel):
+ def __init__(self):
+ super().__init__('Prompt2prompt')
+
+ def __call__(self, *args, **kwargs):
+ if "num_inner_steps" not in kwargs:
+ kwargs["num_inner_steps"] = 3
+ return super().__call__(*args, **kwargs)
+
+def load_imagenhub_model(model_name, model_type=None):
+ if model_name == 'PNP':
+ return PNP()
+ if model_name == 'Prompt2prompt':
+ return Prompt2prompt()
+ return ImagenHubModel(model_name)
+
+
+# for name in ['DeepFloydIF', 'PixArtAlpha', 'Kandinsky']: #, 'OpenJourney', 'LCM', 'SD' 'SDXL'
+# #
+# pipe = ImagenHubModel(name)
+# result = pipe(prompt='a cute dog is playing a ball')
+# print(result)
+
+# for name in ['SD']:
+# from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
+# import torch
+# pipe = DiffusionPipeline.from_pretrained(
+# "stabilityai/stable-diffusion-2-base",
+# torch_dtype=torch.float16,
+# safety_checker=None,
+# ).to("cuda")
+# pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
\ No newline at end of file
diff --git a/model/models/playground_api.py b/model/models/playground_api.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a7111052d7bd18f9d34d798fc5a6cd5c8563306
--- /dev/null
+++ b/model/models/playground_api.py
@@ -0,0 +1,35 @@
+import os
+import json
+import requests
+from PIL import Image
+import io
+import base64
+class PlayGround():
+ def __init__(self, model_name, model_type=None):
+ self.model_name = model_name
+ self.model_type = model_type
+ self.api_key = os.environ['PlaygroundAPI']
+ if model_name == "PlayGroundV2":
+ self._model_name = "Playground_v2"
+ elif model_name == "PlayGroundV2.5":
+ self._model_name = "Playground_v2.5"
+
+
+ def __call__(self, prompt):
+ headers = {
+ 'Content-Type': 'application/json',
+ 'Authorization': "Bearer " + self.api_key,
+ }
+
+ data = json.dumps({"prompt": prompt, "filter_model": self._model_name, "scheduler": "DPMPP_2M_K", "guidance_scale": 3})
+
+ response = requests.post('https://playground.com/api/models/external/v1', headers=headers, data=data)
+ response.raise_for_status()
+ json_obj = response.json()
+ image_base64 = json_obj['images'][0]
+ img = Image.open(io.BytesIO(base64.decodebytes(bytes(image_base64, "utf-8"))))
+
+ return img
+
+def load_playground_model(model_name, model_type="generation"):
+ return PlayGround(model_name, model_type)
\ No newline at end of file
diff --git a/model/other_models.py b/model/other_models.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..064923c2d184c96aa1a1d1cbe25cdb588b81e679
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,71 @@
+flask
+flask_restful
+flask_cors
+faiss-cpu
+fire
+h5py
+# xformers~=0.0.20
+numpy>=1.23.5
+pandas<2.0.0
+peft
+torch==2.0.1
+torchvision==0.15.2
+torchaudio==2.0.2
+jupyterlab>=4.0.2
+notebook>=6.5.4
+albumentations>=1.1.0
+opencv-python>=4.2.0
+pudb~=2019.2
+imageio>=2.14.1
+imageio-ffmpeg>=0.4.7
+pytorch-lightning>=1.5.9
+omegaconf~=2.1.1
+gradio==4.21.0
+pillow~=9.5.0
+einops>=0.4.1
+torch-fidelity>=0.3.0
+setuptools>=59.5.0
+transformers==4.37.2
+torchmetrics>=0.6.0
+lpips
+dreamsim
+image-reward
+kornia>=0.6
+diffusers>=0.18.0
+accelerate>=0.20.3
+safetensors
+datasets
+tqdm>=4.64.1
+matplotlib>=3.7.1
+taming-transformers-rom1504~=0.0.6
+madgrad>=1.1
+-e git+https://github.com/openai/CLIP.git@main#egg=clip
+dominate>=2.8.0
+-e git+https://github.com/CompVis/latent-diffusion.git#egg=latent-diffusion #ldm
+openai
+nltk~=3.8.1
+krippendorff
+statsmodels
+plotly
+-e git+https://github.com/TIGER-AI-Lab/ImagenHub.git#egg=imagen-hub
+fal_client
+-e git+https://github.com/TIGER-AI-Lab/VideoGenHub.git#egg=videogen-hub
+open_clip_torch
+decord
+huggingface_hub
+open-clip-torch-any-py3
+modelscope
+protobuf==3.20.*
+rotary_embedding_torch
+av
+natsort
+colossalai
+mmengine
+pre-commit
+pyav
+pyarrow
+tensorboard
+timm
+pandarallel
+wandb
+trueskill
diff --git a/serve/Arial.ttf b/serve/Arial.ttf
new file mode 100644
index 0000000000000000000000000000000000000000..9512aea86467504e94d97ddd6396955e1e6786f7
Binary files /dev/null and b/serve/Arial.ttf differ
diff --git a/serve/Ksort.py b/serve/Ksort.py
new file mode 100644
index 0000000000000000000000000000000000000000..08cc0b92ea48a5604d08d2125a5478dc17020d37
--- /dev/null
+++ b/serve/Ksort.py
@@ -0,0 +1,287 @@
+import gradio as gr
+from PIL import Image, ImageDraw, ImageFont
+import os
+from .constants import KSORT_IMAGE_DIR
+from .vote_utils import save_any_image
+from .utils import disable_btn, enable_btn, invisible_btn
+from .upload import create_remote_directory, upload_image, upload_informance
+import json
+
+def reset_level(Top_btn):
+ if Top_btn == "Top 1":
+ level = 0
+ elif Top_btn == "Top 2":
+ level = 1
+ elif Top_btn == "Top 3":
+ level = 2
+ elif Top_btn == "Top 4":
+ level = 3
+ return level
+
+def reset_rank(windows, rank, vote_level):
+ if windows == "Model A":
+ rank[0] = vote_level
+ elif windows == "Model B":
+ rank[1] = vote_level
+ elif windows == "Model C":
+ rank[2] = vote_level
+ elif windows == "Model D":
+ rank[3] = vote_level
+ return rank
+
+def reset_btn_rank(windows, rank, btn, vote_level):
+ if windows == "Model A" and btn == "1":
+ rank[0] = 0
+ elif windows == "Model A" and btn == "2":
+ rank[0] = 1
+ elif windows == "Model A" and btn == "3":
+ rank[0] = 2
+ elif windows == "Model A" and btn == "4":
+ rank[0] = 3
+ elif windows == "Model B" and btn == "1":
+ rank[1] = 0
+ elif windows == "Model B" and btn == "2":
+ rank[1] = 1
+ elif windows == "Model B" and btn == "3":
+ rank[1] = 2
+ elif windows == "Model B" and btn == "4":
+ rank[1] = 3
+ elif windows == "Model C" and btn == "1":
+ rank[2] = 0
+ elif windows == "Model C" and btn == "2":
+ rank[2] = 1
+ elif windows == "Model C" and btn == "3":
+ rank[2] = 2
+ elif windows == "Model C" and btn == "4":
+ rank[2] = 3
+ elif windows == "Model D" and btn == "1":
+ rank[3] = 0
+ elif windows == "Model D" and btn == "2":
+ rank[3] = 1
+ elif windows == "Model D" and btn == "3":
+ rank[3] = 2
+ elif windows == "Model D" and btn == "4":
+ rank[3] = 3
+ if btn == "1":
+ vote_level = 0
+ elif btn == "2":
+ vote_level = 1
+ elif btn == "3":
+ vote_level = 2
+ elif btn == "4":
+ vote_level = 3
+ return (rank, vote_level)
+
+def reset_vote_text(rank):
+ rank_str = ""
+ for i in range(len(rank)):
+ if rank[i] == None:
+ rank_str = rank_str + str(rank[i])
+ else:
+ rank_str = rank_str + str(rank[i]+1)
+ rank_str = rank_str + " "
+ return rank_str
+
+def clear_rank(rank, vote_level):
+ for i in range(len(rank)):
+ rank[i] = None
+ vote_level = 0
+ return rank, vote_level
+
+def revote_windows(generate_ig0, generate_ig1, generate_ig2, generate_ig3, rank, vote_level):
+ for i in range(len(rank)):
+ rank[i] = None
+ vote_level = 0
+ return generate_ig0, generate_ig1, generate_ig2, generate_ig3, rank, vote_level
+
+def reset_submit(rank):
+ for i in range(len(rank)):
+ if rank[i] == None:
+ return disable_btn
+ return enable_btn
+
+def reset_mode(mode):
+
+ if mode == "Best":
+ return (gr.update(visible=False, interactive=False),) * 6 + \
+ (gr.update(visible=True, interactive=True),) * 16 + \
+ (gr.update(visible=True, interactive=True),) * 3 + \
+ (gr.Textbox(value="Rank", visible=False, interactive=False),)
+ elif mode == "Rank":
+ return (gr.update(visible=True, interactive=True),) * 6 + \
+ (gr.update(visible=False, interactive=False),) * 16 + \
+ (gr.update(visible=True, interactive=False),) * 2 + \
+ (gr.update(visible=True, interactive=True),) + \
+ (gr.Textbox(value="Best", visible=False, interactive=False),)
+ else:
+ raise ValueError("Undefined mode")
+
+def get_json_filename(conv_id):
+ output_dir = f'{KSORT_IMAGE_DIR}/{conv_id}/json/'
+ if not os.path.exists(output_dir):
+ os.makedirs(output_dir)
+ output_file = os.path.join(output_dir, "information.json")
+ # name = os.path.join(KSORT_IMAGE_DIR, f"{conv_id}/json/information.json")
+ print(output_file)
+ return output_file
+
+def get_img_filename(conv_id, i):
+ output_dir = f'{KSORT_IMAGE_DIR}/{conv_id}/image/'
+ if not os.path.exists(output_dir):
+ os.makedirs(output_dir)
+ output_file = os.path.join(output_dir, f"{i}.jpg")
+ print(output_file)
+ return output_file
+
+def vote_submit(states, rank, request: gr.Request):
+ conv_id = states[0].conv_id
+
+ for i in range(len(states)):
+ output_file = get_img_filename(conv_id, i)
+ save_any_image(states[i].output, output_file)
+ with open(get_json_filename(conv_id), "a") as fout:
+ data = {
+ "models_name": [x.model_name for x in states],
+ "img_rank": [x for x in rank],
+ }
+ fout.write(json.dumps(data) + "\n")
+
+def vote_ssh_submit(states, rank):
+ conv_id = states[0].conv_id
+ output_dir = create_remote_directory(conv_id)
+ upload_image(states, output_dir)
+
+ data = {
+ "models_name": [x.model_name for x in states],
+ "img_rank": [x for x in rank],
+ }
+ output_file = os.path.join(output_dir, "informance.json")
+ upload_informance(data, output_file)
+
+ from .update_skill import update_skill
+ update_skill(rank)
+
+
+def submit_response_igm(
+ state0, state1, state2, state3, model_selector0, model_selector1, model_selector2, model_selector3, rank, request: gr.Request
+):
+ vote_submit([state0, state1, state2, state3], rank, request)
+ vote_ssh_submit([state0, state1, state2, state3], rank)
+ if model_selector0 == "":
+ return (disable_btn,) * 7 + (
+ gr.Markdown(f"### Model A: {state0.model_name.split('_')[1]}", visible=True),
+ gr.Markdown(f"### Model B: {state1.model_name.split('_')[1]}", visible=True),
+ gr.Markdown(f"### Model C: {state2.model_name.split('_')[1]}", visible=True),
+ gr.Markdown(f"### Model D: {state3.model_name.split('_')[1]}", visible=True)
+ ) + (disable_btn,)
+ else:
+ return (disable_btn,) * 7 + (
+ gr.Markdown(state0.model_name, visible=True),
+ gr.Markdown(state1.model_name, visible=True),
+ gr.Markdown(state2.model_name, visible=True),
+ gr.Markdown(state3.model_name, visible=True)
+ ) + (disable_btn,)
+
+def submit_response_rank_igm(
+ state0, state1, state2, state3, model_selector0, model_selector1, model_selector2, model_selector3, rank, right_vote_text, request: gr.Request
+):
+ print(rank)
+ if right_vote_text == "right":
+ vote_submit([state0, state1, state2, state3], rank, request)
+ vote_ssh_submit([state0, state1, state2, state3], rank)
+ if model_selector0 == "":
+ return (disable_btn,) * 16 + (disable_btn,) * 3 + ("wrong",) + (
+ gr.Markdown(f"### Model A: {state0.model_name.split('_')[1]}", visible=True),
+ gr.Markdown(f"### Model B: {state1.model_name.split('_')[1]}", visible=True),
+ gr.Markdown(f"### Model C: {state2.model_name.split('_')[1]}", visible=True),
+ gr.Markdown(f"### Model D: {state3.model_name.split('_')[1]}", visible=True)
+ )
+ else:
+ return (disable_btn,) * 16 + (disable_btn,) * 3 + ("wrong",) + (
+ gr.Markdown(state0.model_name, visible=True),
+ gr.Markdown(state1.model_name, visible=True),
+ gr.Markdown(state2.model_name, visible=True),
+ gr.Markdown(state3.model_name, visible=True)
+ )
+ else:
+ return (enable_btn,) * 16 + (enable_btn,) * 3 + ("wrong",) + (gr.Markdown("", visible=False),) * 4
+
+def text_response_rank_igm(generate_ig0, generate_ig1, generate_ig2, generate_ig3, Top1_text, Top2_text, Top3_text, Top4_text, vote_textbox):
+ rank_list = [char for char in vote_textbox if char.isdigit()]
+ generate_ig = [generate_ig0, generate_ig1, generate_ig2, generate_ig3]
+ chatbot = []
+ rank = [None, None, None, None]
+ if len(rank_list) != 4:
+ return generate_ig + ["error rank"] + ["wrong"] + [rank]
+ for num in range(len(rank_list)):
+ if rank_list[num] in ['1', '2', '3', '4']:
+ base_image = Image.fromarray(generate_ig[num]).convert("RGBA")
+ if rank_list[num] == '1':
+ txt_layer = Image.new('RGBA', base_image.size, (0, 255, 0, 64))
+ elif rank_list[num] == '2':
+ txt_layer = Image.new('RGBA', base_image.size, (0, 255, 255, 64))
+ elif rank_list[num] == '3':
+ txt_layer = Image.new('RGBA', base_image.size, (255, 0, 255, 64))
+ elif rank_list[num] == '4':
+ txt_layer = Image.new('RGBA', base_image.size, (255, 0, 0, 64))
+ draw = ImageDraw.Draw(txt_layer)
+ font = ImageFont.truetype("./serve/Arial.ttf", 86)
+ text_position = (156, 212)
+ if rank_list[num] == '1':
+ text_color = (0, 255, 0, 200)
+ draw.text(text_position, Top1_text, font=font, fill=text_color)
+ elif rank_list[num] == '2':
+ text_color = (0, 255, 255, 200)
+ draw.text(text_position, Top2_text, font=font, fill=text_color)
+ elif rank_list[num] == '3':
+ text_color = (255, 0, 255, 200)
+ draw.text(text_position, Top3_text, font=font, fill=text_color)
+ elif rank_list[num] == '4':
+ text_color = (255, 0, 0, 200)
+ draw.text(text_position, Top4_text, font=font, fill=text_color)
+
+ combined = Image.alpha_composite(base_image, txt_layer)
+ combined = combined.convert("RGB")
+ chatbot.append(combined.copy())
+ else:
+ return generate_ig + ["error rank"] + ["wrong"] + [rank]
+ rank_str = ""
+ for str_num in rank_list:
+ rank_str = rank_str + str_num
+ rank_str = rank_str + " "
+ rank = [int(x) for x in rank_list]
+
+ return chatbot + [rank_str] + ["right"] + [rank]
+
+def add_foreground(image, vote_level, Top1_text, Top2_text, Top3_text, Top4_text):
+ base_image = Image.fromarray(image).convert("RGBA")
+ if vote_level == 0:
+ txt_layer = Image.new('RGBA', base_image.size, (0, 255, 0, 64))
+ elif vote_level == 1:
+ txt_layer = Image.new('RGBA', base_image.size, (0, 255, 255, 64))
+ elif vote_level == 2:
+ txt_layer = Image.new('RGBA', base_image.size, (255, 0, 255, 64))
+ elif vote_level == 3:
+ txt_layer = Image.new('RGBA', base_image.size, (255, 0, 0, 64))
+
+ draw = ImageDraw.Draw(txt_layer)
+ font = ImageFont.truetype("./serve/Arial.ttf", 86)
+
+ text_position = (156, 212)
+ if vote_level == 0:
+ text_color = (0, 255, 0, 200)
+ draw.text(text_position, Top1_text, font=font, fill=text_color)
+ elif vote_level == 1:
+ text_color = (0, 255, 255, 200)
+ draw.text(text_position, Top2_text, font=font, fill=text_color)
+ elif vote_level == 2:
+ text_color = (255, 0, 255, 200)
+ draw.text(text_position, Top3_text, font=font, fill=text_color)
+ elif vote_level == 3:
+ text_color = (255, 0, 0, 200)
+ draw.text(text_position, Top4_text, font=font, fill=text_color)
+
+ combined = Image.alpha_composite(base_image, txt_layer)
+ combined = combined.convert("RGB")
+ return combined
+
diff --git a/serve/__init__.py b/serve/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/serve/gradio_web.py b/serve/gradio_web.py
new file mode 100644
index 0000000000000000000000000000000000000000..226768135be9caca980d7fcd5290c0b508e991d8
--- /dev/null
+++ b/serve/gradio_web.py
@@ -0,0 +1,774 @@
+from .utils import *
+from .vote_utils import (
+ upvote_last_response_ig as upvote_last_response,
+ downvote_last_response_ig as downvote_last_response,
+ flag_last_response_ig as flag_last_response,
+ leftvote_last_response_igm as leftvote_last_response,
+ left1vote_last_response_igm as left1vote_last_response,
+ rightvote_last_response_igm as rightvote_last_response,
+ right1vote_last_response_igm as right1vote_last_response,
+ tievote_last_response_igm as tievote_last_response,
+ bothbad_vote_last_response_igm as bothbad_vote_last_response,
+ share_click_igm as share_click,
+ generate_ig,
+ generate_ig_museum,
+ generate_igm,
+ generate_igm_museum,
+ generate_igm_annoy,
+ generate_igm_annoy_museum,
+ share_js
+)
+from .Ksort import (
+ add_foreground,
+ reset_level,
+ reset_rank,
+ revote_windows,
+ submit_response_igm,
+ submit_response_rank_igm,
+ reset_submit,
+ clear_rank,
+ reset_mode,
+ reset_btn_rank,
+ reset_vote_text,
+ text_response_rank_igm,
+)
+from functools import partial
+
+def build_side_by_side_ui_anony(models):
+ notice_markdown = """
+# ⚔️ Text2Image-Arena ⚔️
+## 📜 Rules
+- Input prompt to two anonymous models in same area (e.g., SD, SDXL, OpenJourney in Text-guided Image Generation Model) and vote for the better one!
+- When the results are ready, click the button below to vote.
+- Vote won't be counted if model identity is revealed during conversation.
+- Click "New Round" to start a new round.
+
+## 🏆 Arena Elo
+Find out who is the 🥇conditional image generation models! More models are going to be supported.
+
+## 👇 Generating now!
+
+"""
+
+ model_list = models.model_ig_list
+
+ state0 = gr.State()
+ state1 = gr.State()
+ state2 = gr.State()
+ state3 = gr.State()
+
+ gen_func = partial(generate_igm_annoy, models.generate_image_ig_parallel_anony)
+ gen_func_random = partial(generate_igm_annoy_museum, models.generate_image_ig_museum_parallel_anony)
+
+ gr.Markdown(notice_markdown, elem_id="notice_markdown")
+
+ with gr.Group(elem_id="share-region-anony"):
+ with gr.Accordion("🔍 Expand to see all Arena players", open=False):
+ model_description_md = get_model_description_md(model_list)
+ gr.Markdown(model_description_md, elem_id="model_description_markdown")
+ with gr.Row():
+ with gr.Column():
+ chatbot_left = gr.Image(width=512, label = "Model A")
+ with gr.Column():
+ chatbot_left1 = gr.Image(width=512, label = "Model B")
+ with gr.Column():
+ chatbot_right = gr.Image(width=512, label = "Model C")
+ with gr.Column():
+ chatbot_right1 = gr.Image(width=512, label = "Model D")
+
+ with gr.Row():
+ with gr.Column():
+ model_selector_left = gr.Markdown("", visible=False)
+ with gr.Column():
+ model_selector_left1 = gr.Markdown("", visible=False)
+ with gr.Column():
+ model_selector_right = gr.Markdown("", visible=False)
+ with gr.Column():
+ model_selector_right1 = gr.Markdown("", visible=False)
+ with gr.Row():
+ slow_warning = gr.Markdown("", elem_id="notice_markdown")
+
+ with gr.Row():
+ leftvote_btn = gr.Button(
+ value="A is Best", visible=False, interactive=False
+ )
+ left1vote_btn = gr.Button(
+ value="B is Best", visible=False, interactive=False
+ )
+ rightvote_btn = gr.Button(
+ value="C is Best", visible=False, interactive=False
+ )
+ right1vote_btn = gr.Button(
+ value="D is Best", visible=False, interactive=False
+ )
+ tie_btn = gr.Button(value="🤝 Tie", visible=False, interactive=False)
+ bothbad_btn = gr.Button(
+ value="👎 Both are bad", visible=False, interactive=False
+ )
+
+ with gr.Row():
+ Top1_btn = gr.Button(
+ value="Top1", visible=False, interactive=False
+ )
+ Top2_btn = gr.Button(
+ value="Top2", visible=False, interactive=False
+ )
+ Top3_btn = gr.Button(
+ value="Top3", visible=False, interactive=False
+ )
+ Top4_btn = gr.Button(
+ value="Top4", visible=False, interactive=False
+ )
+ Revote_btn = gr.Button(
+ value="Re-vote", visible=False, interactive=False
+ )
+ Submit_btn = gr.Button(value="🤝 Submit", visible=False, interactive=False)
+ with gr.Row():
+ with gr.Blocks():
+ with gr.Row():
+ with gr.Column(scale=1, min_width=10):
+ A1_btn = gr.Button(
+ value="1", visible=False, interactive=False
+ )
+ with gr.Column(scale=1, min_width=10):
+ A2_btn = gr.Button(
+ value="2", visible=False, interactive=False
+ )
+ with gr.Column(scale=1, min_width=10):
+ A3_btn = gr.Button(
+ value="3", visible=False, interactive=False
+ )
+ with gr.Column(scale=1, min_width=10):
+ A4_btn = gr.Button(
+ value="4", visible=False, interactive=False
+ )
+ with gr.Blocks():
+ with gr.Row():
+ with gr.Column(scale=1, min_width=10):
+ B1_btn = gr.Button(
+ value="1", visible=False, interactive=False
+ )
+ with gr.Column(scale=1, min_width=10):
+ B2_btn = gr.Button(
+ value="2", visible=False, interactive=False
+ )
+ with gr.Column(scale=1, min_width=10):
+ B3_btn = gr.Button(
+ value="3", visible=False, interactive=False
+ )
+ with gr.Column(scale=1, min_width=10):
+ B4_btn = gr.Button(
+ value="4", visible=False, interactive=False
+ )
+ with gr.Blocks():
+ with gr.Row():
+ with gr.Column(scale=1, min_width=10):
+ C1_btn = gr.Button(
+ value="1", visible=False, interactive=False
+ )
+ with gr.Column(scale=1, min_width=10):
+ C2_btn = gr.Button(
+ value="2", visible=False, interactive=False
+ )
+ with gr.Column(scale=1, min_width=10):
+ C3_btn = gr.Button(
+ value="3", visible=False, interactive=False
+ )
+ with gr.Column(scale=1, min_width=10):
+ C4_btn = gr.Button(
+ value="4", visible=False, interactive=False
+ )
+ with gr.Blocks():
+ with gr.Row():
+ with gr.Column(scale=1, min_width=10):
+ D1_btn = gr.Button(
+ value="1", visible=False, interactive=False
+ )
+ with gr.Column(scale=1, min_width=10):
+ D2_btn = gr.Button(
+ value="2", visible=False, interactive=False
+ )
+ with gr.Column(scale=1, min_width=10):
+ D3_btn = gr.Button(
+ value="3", visible=False, interactive=False
+ )
+ with gr.Column(scale=1, min_width=10):
+ D4_btn = gr.Button(
+ value="4", visible=False, interactive=False
+ )
+
+ with gr.Row():
+ vote_textbox = gr.Textbox(
+ show_label=False,
+ placeholder="👉 Enter your rank",
+ container=True,
+ elem_id="input_box",
+ visible=False,
+ )
+ vote_submit_btn = gr.Button(value="Submit", visible=False, interactive=False, variant="primary", scale=0)
+ vote_mode_btn = gr.Button(value="🔄 Mode", visible=False, interactive=False, variant="primary", scale=0)
+
+ with gr.Row():
+ textbox = gr.Textbox(
+ show_label=False,
+ placeholder="👉 Enter your prompt and press ENTER",
+ container=True,
+ elem_id="input_box",
+ )
+ send_btn = gr.Button(value="Send", variant="primary", scale=0)
+ draw_btn = gr.Button(value="🎲 Random sample", variant="primary", scale=0)
+
+ with gr.Row():
+ clear_btn = gr.Button(value="🎲 New Round", interactive=False)
+ regenerate_btn = gr.Button(value="🔄 Regenerate", interactive=False)
+ # share_btn = gr.Button(value="📷 Share")
+
+ #gr.Markdown(acknowledgment_md, elem_id="ack_markdown")
+
+ dummy_img_output = gr.Image(width=512, visible=False)
+ gr.Examples(
+ examples=[["a cute dog is playing a ball", os.path.join("./examples", "dog.jpg")],
+ ["Buildings on fire, old film still", os.path.join("./examples", "fire.jpg")],
+ ["Lonely evil bananas on a table, hard light chiaroscuro, realistic",os.path.join("./examples", "banana.jpg")],
+ ["A futuristic hopeful busy city, purple and green color scheme", os.path.join("./examples", "city.jpg")]],
+ inputs = [textbox, dummy_img_output])
+
+ btn_list = [leftvote_btn, left1vote_btn, rightvote_btn, right1vote_btn, tie_btn, bothbad_btn]
+ vote_list = [Top1_btn, Top2_btn, Top3_btn, Top4_btn, Revote_btn, Submit_btn]
+ order_btn_list = [send_btn, draw_btn, clear_btn, regenerate_btn]
+ vote_order_list = [leftvote_btn, left1vote_btn, rightvote_btn, right1vote_btn, tie_btn, bothbad_btn, \
+ A1_btn, A2_btn, A3_btn, A4_btn, B1_btn, B2_btn, B3_btn, B4_btn, C1_btn, C2_btn, C3_btn, C4_btn, D1_btn, D2_btn, D3_btn, D4_btn, \
+ vote_textbox, vote_submit_btn, vote_mode_btn]
+ # vote_rank_list = [A1_btn, A2_btn, A3_btn, A4_btn, B1_btn, B2_btn, B3_btn, B4_btn, C1_btn, C2_btn, C3_btn, C4_btn, D1_btn, D2_btn, D3_btn, D4_btn]
+
+ generate_ig0 = gr.Image(width=512, label = "generate A", visible=False, interactive=False)
+ generate_ig1 = gr.Image(width=512, label = "generate B", visible=False, interactive=False)
+ generate_ig2 = gr.Image(width=512, label = "generate C", visible=False, interactive=False)
+ generate_ig3 = gr.Image(width=512, label = "generate D", visible=False, interactive=False)
+ dummy_left_model = gr.State("")
+ dummy_left1_model = gr.State("")
+ dummy_right_model = gr.State("")
+ dummy_right1_model = gr.State("")
+
+ ig_rank = [None, None, None, None]
+ bastA_rank = [0, 3, 3, 3]
+ bastB_rank = [3, 0, 3, 3]
+ bastC_rank = [3, 3, 0, 3]
+ bastD_rank = [3, 3, 3, 0]
+ tie_rank = [0, 0, 0, 0]
+ bad_rank = [3, 3, 3, 3]
+ rank = gr.State(ig_rank)
+ rankA = gr.State(bastA_rank)
+ rankB = gr.State(bastB_rank)
+ rankC = gr.State(bastC_rank)
+ rankD = gr.State(bastD_rank)
+ rankTie = gr.State(tie_rank)
+ rankBad = gr.State(bad_rank)
+ Top1_text = gr.Textbox(value="Top 1", visible=False, interactive=False)
+ Top2_text = gr.Textbox(value="Top 2", visible=False, interactive=False)
+ Top3_text = gr.Textbox(value="Top 3", visible=False, interactive=False)
+ Top4_text = gr.Textbox(value="Top 4", visible=False, interactive=False)
+ window1_text = gr.Textbox(value="Model A", visible=False, interactive=False)
+ window2_text = gr.Textbox(value="Model B", visible=False, interactive=False)
+ window3_text = gr.Textbox(value="Model C", visible=False, interactive=False)
+ window4_text = gr.Textbox(value="Model D", visible=False, interactive=False)
+ vote_level = gr.Number(value=0, visible=False, interactive=False)
+ Top1_btn.click(reset_level, inputs=[Top1_text], outputs=[vote_level])
+ Top2_btn.click(reset_level, inputs=[Top2_text], outputs=[vote_level])
+ Top3_btn.click(reset_level, inputs=[Top3_text], outputs=[vote_level])
+ Top4_btn.click(reset_level, inputs=[Top4_text], outputs=[vote_level])
+ vote_mode = gr.Textbox(value="Best", visible=False, interactive=False)
+ right_vote_text = gr.Textbox(value="wrong", visible=False, interactive=False)
+
+ textbox.submit(
+ gen_func,
+ inputs=[state0, state1, state2, state3, textbox, model_selector_left, model_selector_left1, model_selector_right, model_selector_right1],
+ outputs=[state0, state1, state2, state3, generate_ig0, generate_ig1, generate_ig2, generate_ig3, chatbot_left, chatbot_left1, chatbot_right, chatbot_right1, \
+ model_selector_left, model_selector_left1, model_selector_right, model_selector_right1],
+ api_name="submit_btn_annony"
+ ).then(
+ disable_order_buttons,
+ inputs=None,
+ outputs=order_btn_list
+ ).then(
+ enable_vote_mode_buttons,
+ inputs=[vote_mode],
+ outputs=vote_order_list
+ )
+
+ send_btn.click(
+ gen_func,
+ inputs=[state0, state1, state2, state3, textbox, model_selector_left, model_selector_left1, model_selector_right, model_selector_right1],
+ outputs=[state0, state1, state2, state3, generate_ig0, generate_ig1, generate_ig2, generate_ig3, chatbot_left, chatbot_left1, chatbot_right, chatbot_right1, \
+ model_selector_left, model_selector_left1, model_selector_right, model_selector_right1],
+ api_name="send_btn_annony"
+ ).then(
+ disable_order_buttons,
+ inputs=None,
+ outputs=order_btn_list
+ ).then(
+ enable_vote_mode_buttons,
+ inputs=[vote_mode],
+ outputs=vote_order_list
+ )
+
+ draw_btn.click(
+ gen_func_random,
+ inputs=[state0, state1, state2, state3, model_selector_left, model_selector_left1, model_selector_right, model_selector_right1],
+ outputs=[state0, state1, state2, state3, generate_ig0, generate_ig1, generate_ig2, generate_ig3, chatbot_left, chatbot_left1, chatbot_right, chatbot_right1, \
+ textbox, model_selector_left, model_selector_left1, model_selector_right, model_selector_right1],
+ api_name="draw_btn_annony"
+ ).then(
+ disable_order_buttons,
+ inputs=None,
+ outputs=order_btn_list
+ ).then(
+ enable_vote_mode_buttons,
+ inputs=[vote_mode],
+ outputs=vote_order_list
+ )
+ # .then(
+ # enable_vote_buttons,
+ # inputs=None,
+ # outputs=vote_list
+ # )
+
+ clear_btn.click(
+ clear_history_side_by_side_anony,
+ inputs=None,
+ outputs=[state0, state1, state2, state3, textbox, vote_textbox, chatbot_left, chatbot_left1, chatbot_right, chatbot_right1, \
+ model_selector_left, model_selector_left1, model_selector_right, model_selector_right1],
+ api_name="clear_btn_annony"
+ ).then(
+ enable_order_buttons,
+ inputs=None,
+ outputs=order_btn_list
+ ).then(
+ disable_vote_buttons,
+ inputs=None,
+ outputs=vote_list
+ ).then(
+ clear_rank,
+ inputs=[rank, vote_level],
+ outputs=[rank, vote_level]
+ ).then(
+ disable_vote_mode_buttons,
+ inputs=None,
+ outputs=vote_order_list
+ )
+
+ # regenerate_btn.click(
+ # gen_func,
+ # inputs=[state0, state1, state2, state3, textbox, model_selector_left, model_selector_left1, model_selector_right, model_selector_right1],
+ # outputs=[state0, state1, state2, state3, chatbot_left, chatbot_left1, chatbot_right, chatbot_right1, \
+ # model_selector_left, model_selector_left1, model_selector_right, model_selector_right1],
+ # api_name="regenerate_btn_annony"
+ # ).then(
+ # enable_best_buttons,
+ # inputs=None,
+ # outputs=btn_list
+ # )
+ vote_mode_btn.click(
+ reset_mode,
+ inputs=[vote_mode],
+ outputs=[leftvote_btn, left1vote_btn, rightvote_btn, right1vote_btn, tie_btn, bothbad_btn, \
+ A1_btn, A2_btn, A3_btn, A4_btn, B1_btn, B2_btn, B3_btn, B4_btn, C1_btn, C2_btn, C3_btn, C4_btn, D1_btn, D2_btn, D3_btn, D4_btn, \
+ vote_textbox, vote_submit_btn, vote_mode_btn, vote_mode]
+ )
+ vote_textbox.submit(
+ text_response_rank_igm,
+ inputs=[generate_ig0, generate_ig1, generate_ig2, generate_ig3, Top1_text, Top2_text, Top3_text, Top4_text, vote_textbox],
+ outputs=[chatbot_left, chatbot_left1, chatbot_right, chatbot_right1, vote_textbox, right_vote_text, rank]
+ ).then(
+ submit_response_rank_igm,
+ inputs=[state0, state1, state2, state3, dummy_left_model, dummy_left1_model, dummy_right_model, dummy_right1_model, rank, right_vote_text],
+ outputs=[A1_btn, A2_btn, A3_btn, A4_btn, B1_btn, B2_btn, B3_btn, B4_btn, C1_btn, C2_btn, C3_btn, C4_btn, D1_btn, D2_btn, D3_btn, D4_btn, \
+ vote_textbox, vote_submit_btn, vote_mode_btn, right_vote_text, \
+ model_selector_left, model_selector_left1, model_selector_right, model_selector_right1],
+ api_name="submit_btn_annony"
+ )
+ vote_submit_btn.click(
+ text_response_rank_igm,
+ inputs=[generate_ig0, generate_ig1, generate_ig2, generate_ig3, Top1_text, Top2_text, Top3_text, Top4_text, vote_textbox],
+ outputs=[chatbot_left, chatbot_left1, chatbot_right, chatbot_right1, vote_textbox, right_vote_text, rank]
+ ).then(
+ submit_response_rank_igm,
+ inputs=[state0, state1, state2, state3, dummy_left_model, dummy_left1_model, dummy_right_model, dummy_right1_model, rank, right_vote_text],
+ outputs=[A1_btn, A2_btn, A3_btn, A4_btn, B1_btn, B2_btn, B3_btn, B4_btn, C1_btn, C2_btn, C3_btn, C4_btn, D1_btn, D2_btn, D3_btn, D4_btn, \
+ vote_textbox, vote_submit_btn, vote_mode_btn, right_vote_text, \
+ model_selector_left, model_selector_left1, model_selector_right, model_selector_right1],
+ api_name="submit_btn_annony"
+ )
+
+ # Revote_btn.click(
+ # revote_windows,
+ # inputs=[generate_ig0, generate_ig1, generate_ig2, generate_ig3, rank, vote_level],
+ # outputs=[chatbot_left, chatbot_left1, chatbot_right, chatbot_right1, rank, vote_level]
+ # ).then(
+ # reset_submit,
+ # inputs = [rank],
+ # outputs = [Submit_btn]
+ # )
+ # Submit_btn.click(
+ # submit_response_igm,
+ # inputs=[state0, state1, state2, state3, dummy_left_model, dummy_left1_model, dummy_right_model, dummy_right1_model, rank],
+ # outputs=[textbox, Top1_btn, Top2_btn, Top3_btn, Top4_btn, Revote_btn, Submit_btn, \
+ # model_selector_left, model_selector_left1, model_selector_right, model_selector_right1]
+ # )
+
+ # chatbot_left.select(add_foreground, inputs=[generate_ig0, vote_level, Top1_text, Top2_text, Top3_text, Top4_text], outputs=[chatbot_left]).then(
+ # reset_rank,
+ # inputs = [window1_text, rank, vote_level],
+ # outputs = [rank]
+ # ).then(
+ # reset_submit,
+ # inputs = [rank],
+ # outputs = [Submit_btn]
+ # )
+ # chatbot_left1.select(add_foreground, inputs=[generate_ig1, vote_level, Top1_text, Top2_text, Top3_text, Top4_text], outputs=[chatbot_left1]).then(
+ # reset_rank,
+ # inputs = [window2_text, rank, vote_level],
+ # outputs = [rank]
+ # ).then(
+ # reset_submit,
+ # inputs = [rank],
+ # outputs = [Submit_btn]
+ # )
+ # chatbot_right.select(add_foreground, inputs=[generate_ig2, vote_level, Top1_text, Top2_text, Top3_text, Top4_text], outputs=[chatbot_right]).then(
+ # reset_rank,
+ # inputs = [window3_text, rank, vote_level],
+ # outputs = [rank]
+ # ).then(
+ # reset_submit,
+ # inputs = [rank],
+ # outputs = [Submit_btn]
+ # )
+ # chatbot_right1.select(add_foreground, inputs=[generate_ig3, vote_level, Top1_text, Top2_text, Top3_text, Top4_text], outputs=[chatbot_right1]).then(
+ # reset_rank,
+ # inputs = [window4_text, rank, vote_level],
+ # outputs = [rank]
+ # ).then(
+ # reset_submit,
+ # inputs = [rank],
+ # outputs = [Submit_btn]
+ # )
+
+
+ leftvote_btn.click(
+ submit_response_igm,
+ inputs=[state0, state1, state2, state3, dummy_left_model, dummy_left1_model, dummy_right_model, dummy_right1_model, rankA],
+ outputs=[textbox, leftvote_btn, left1vote_btn, rightvote_btn, right1vote_btn, tie_btn, bothbad_btn, \
+ model_selector_left, model_selector_left1, model_selector_right, model_selector_right1, \
+ vote_mode_btn]
+ )
+ left1vote_btn.click(
+ submit_response_igm,
+ inputs=[state0, state1, state2, state3, dummy_left_model, dummy_left1_model, dummy_right_model, dummy_right1_model, rankB],
+ outputs=[textbox, leftvote_btn, left1vote_btn, rightvote_btn, right1vote_btn, tie_btn, bothbad_btn, \
+ model_selector_left, model_selector_left1, model_selector_right, model_selector_right1, \
+ vote_mode_btn]
+ )
+ rightvote_btn.click(
+ submit_response_igm,
+ inputs=[state0, state1, state2, state3, dummy_left_model, dummy_left1_model, dummy_right_model, dummy_right1_model, rankC],
+ outputs=[textbox, leftvote_btn, left1vote_btn, rightvote_btn, right1vote_btn, tie_btn, bothbad_btn, \
+ model_selector_left, model_selector_left1, model_selector_right, model_selector_right1, \
+ vote_mode_btn]
+ )
+ right1vote_btn.click(
+ submit_response_igm,
+ inputs=[state0, state1, state2, state3, dummy_left_model, dummy_left1_model, dummy_right_model, dummy_right1_model, rankD],
+ outputs=[textbox, leftvote_btn, left1vote_btn, rightvote_btn, right1vote_btn, tie_btn, bothbad_btn, \
+ model_selector_left, model_selector_left1, model_selector_right, model_selector_right1, \
+ vote_mode_btn]
+ )
+ tie_btn.click(
+ submit_response_igm,
+ inputs=[state0, state1, state2, state3, dummy_left_model, dummy_left1_model, dummy_right_model, dummy_right1_model, rankTie],
+ outputs=[textbox, leftvote_btn, left1vote_btn, rightvote_btn, right1vote_btn, tie_btn, bothbad_btn, \
+ model_selector_left, model_selector_left1, model_selector_right, model_selector_right1, \
+ vote_mode_btn]
+ )
+ bothbad_btn.click(
+ submit_response_igm,
+ inputs=[state0, state1, state2, state3, dummy_left_model, dummy_left1_model, dummy_right_model, dummy_right1_model, rankBad],
+ outputs=[textbox, leftvote_btn, left1vote_btn, rightvote_btn, right1vote_btn, tie_btn, bothbad_btn, \
+ model_selector_left, model_selector_left1, model_selector_right, model_selector_right1, \
+ vote_mode_btn]
+ )
+
+ A1_btn.click(
+ reset_btn_rank,
+ inputs=[window1_text, rank, A1_btn, vote_level],
+ outputs=[rank, vote_level]
+ ).then(
+ add_foreground,
+ inputs=[generate_ig0, vote_level, Top1_text, Top2_text, Top3_text, Top4_text],
+ outputs=[chatbot_left]
+ ).then(
+ reset_submit,
+ inputs = [rank],
+ outputs = [vote_submit_btn]
+ ).then(
+ reset_vote_text,
+ inputs = [rank],
+ outputs = [vote_textbox]
+ )
+ A2_btn.click(
+ reset_btn_rank,
+ inputs=[window1_text, rank, A2_btn, vote_level],
+ outputs=[rank, vote_level]
+ ).then(
+ add_foreground,
+ inputs=[generate_ig0, vote_level, Top1_text, Top2_text, Top3_text, Top4_text],
+ outputs=[chatbot_left]
+ ).then(
+ reset_submit,
+ inputs = [rank],
+ outputs = [vote_submit_btn]
+ ).then(
+ reset_vote_text,
+ inputs = [rank],
+ outputs = [vote_textbox]
+ )
+ A3_btn.click(
+ reset_btn_rank,
+ inputs=[window1_text, rank, A3_btn, vote_level],
+ outputs=[rank, vote_level]
+ ).then(
+ add_foreground,
+ inputs=[generate_ig0, vote_level, Top1_text, Top2_text, Top3_text, Top4_text],
+ outputs=[chatbot_left]
+ ).then(
+ reset_submit,
+ inputs = [rank],
+ outputs = [vote_submit_btn]
+ ).then(
+ reset_vote_text,
+ inputs = [rank],
+ outputs = [vote_textbox]
+ )
+ A4_btn.click(
+ reset_btn_rank,
+ inputs=[window1_text, rank, A4_btn, vote_level],
+ outputs=[rank, vote_level]
+ ).then(
+ add_foreground,
+ inputs=[generate_ig0, vote_level, Top1_text, Top2_text, Top3_text, Top4_text],
+ outputs=[chatbot_left]
+ ).then(
+ reset_submit,
+ inputs = [rank],
+ outputs = [vote_submit_btn]
+ ).then(
+ reset_vote_text,
+ inputs = [rank],
+ outputs = [vote_textbox]
+ )
+
+ B1_btn.click(
+ reset_btn_rank,
+ inputs=[window2_text, rank, B1_btn, vote_level],
+ outputs=[rank, vote_level]
+ ).then(
+ add_foreground,
+ inputs=[generate_ig1, vote_level, Top1_text, Top2_text, Top3_text, Top4_text],
+ outputs=[chatbot_left1]
+ ).then(
+ reset_submit,
+ inputs = [rank],
+ outputs = [vote_submit_btn]
+ ).then(
+ reset_vote_text,
+ inputs = [rank],
+ outputs = [vote_textbox]
+ )
+ B2_btn.click(
+ reset_btn_rank,
+ inputs=[window2_text, rank, B2_btn, vote_level],
+ outputs=[rank, vote_level]
+ ).then(
+ add_foreground,
+ inputs=[generate_ig1, vote_level, Top1_text, Top2_text, Top3_text, Top4_text],
+ outputs=[chatbot_left1]
+ ).then(
+ reset_submit,
+ inputs = [rank],
+ outputs = [vote_submit_btn]
+ ).then(
+ reset_vote_text,
+ inputs = [rank],
+ outputs = [vote_textbox]
+ )
+ B3_btn.click(
+ reset_btn_rank,
+ inputs=[window2_text, rank, B3_btn, vote_level],
+ outputs=[rank, vote_level]
+ ).then(
+ add_foreground,
+ inputs=[generate_ig1, vote_level, Top1_text, Top2_text, Top3_text, Top4_text],
+ outputs=[chatbot_left1]
+ ).then(
+ reset_submit,
+ inputs = [rank],
+ outputs = [vote_submit_btn]
+ ).then(
+ reset_vote_text,
+ inputs = [rank],
+ outputs = [vote_textbox]
+ )
+ B4_btn.click(
+ reset_btn_rank,
+ inputs=[window2_text, rank, B4_btn, vote_level],
+ outputs=[rank, vote_level]
+ ).then(
+ add_foreground,
+ inputs=[generate_ig1, vote_level, Top1_text, Top2_text, Top3_text, Top4_text],
+ outputs=[chatbot_left1]
+ ).then(
+ reset_submit,
+ inputs = [rank],
+ outputs = [vote_submit_btn]
+ ).then(
+ reset_vote_text,
+ inputs = [rank],
+ outputs = [vote_textbox]
+ )
+
+ C1_btn.click(
+ reset_btn_rank,
+ inputs=[window3_text, rank, C1_btn, vote_level],
+ outputs=[rank, vote_level]
+ ).then(
+ add_foreground,
+ inputs=[generate_ig2, vote_level, Top1_text, Top2_text, Top3_text, Top4_text],
+ outputs=[chatbot_right]
+ ).then(
+ reset_submit,
+ inputs = [rank],
+ outputs = [vote_submit_btn]
+ ).then(
+ reset_vote_text,
+ inputs = [rank],
+ outputs = [vote_textbox]
+ )
+ C2_btn.click(
+ reset_btn_rank,
+ inputs=[window3_text, rank, C2_btn, vote_level],
+ outputs=[rank, vote_level]
+ ).then(
+ add_foreground,
+ inputs=[generate_ig2, vote_level, Top1_text, Top2_text, Top3_text, Top4_text],
+ outputs=[chatbot_right]
+ ).then(
+ reset_submit,
+ inputs = [rank],
+ outputs = [vote_submit_btn]
+ ).then(
+ reset_vote_text,
+ inputs = [rank],
+ outputs = [vote_textbox]
+ )
+ C3_btn.click(
+ reset_btn_rank,
+ inputs=[window3_text, rank, C3_btn, vote_level],
+ outputs=[rank, vote_level]
+ ).then(
+ add_foreground,
+ inputs=[generate_ig2, vote_level, Top1_text, Top2_text, Top3_text, Top4_text],
+ outputs=[chatbot_right]
+ ).then(
+ reset_submit,
+ inputs = [rank],
+ outputs = [vote_submit_btn]
+ ).then(
+ reset_vote_text,
+ inputs = [rank],
+ outputs = [vote_textbox]
+ )
+ C4_btn.click(
+ reset_btn_rank,
+ inputs=[window3_text, rank, C4_btn, vote_level],
+ outputs=[rank, vote_level]
+ ).then(
+ add_foreground,
+ inputs=[generate_ig2, vote_level, Top1_text, Top2_text, Top3_text, Top4_text],
+ outputs=[chatbot_right]
+ ).then(
+ reset_submit,
+ inputs = [rank],
+ outputs = [vote_submit_btn]
+ ).then(
+ reset_vote_text,
+ inputs = [rank],
+ outputs = [vote_textbox]
+ )
+
+ D1_btn.click(
+ reset_btn_rank,
+ inputs=[window4_text, rank, D1_btn, vote_level],
+ outputs=[rank, vote_level]
+ ).then(
+ add_foreground,
+ inputs=[generate_ig3, vote_level, Top1_text, Top2_text, Top3_text, Top4_text],
+ outputs=[chatbot_right1]
+ ).then(
+ reset_submit,
+ inputs = [rank],
+ outputs = [vote_submit_btn]
+ ).then(
+ reset_vote_text,
+ inputs = [rank],
+ outputs = [vote_textbox]
+ )
+ D2_btn.click(
+ reset_btn_rank,
+ inputs=[window4_text, rank, D2_btn, vote_level],
+ outputs=[rank, vote_level]
+ ).then(
+ add_foreground,
+ inputs=[generate_ig3, vote_level, Top1_text, Top2_text, Top3_text, Top4_text],
+ outputs=[chatbot_right1]
+ ).then(
+ reset_submit,
+ inputs = [rank],
+ outputs = [vote_submit_btn]
+ ).then(
+ reset_vote_text,
+ inputs = [rank],
+ outputs = [vote_textbox]
+ )
+ D3_btn.click(
+ reset_btn_rank,
+ inputs=[window4_text, rank, D3_btn, vote_level],
+ outputs=[rank, vote_level]
+ ).then(
+ add_foreground,
+ inputs=[generate_ig3, vote_level, Top1_text, Top2_text, Top3_text, Top4_text],
+ outputs=[chatbot_right1]
+ ).then(
+ reset_submit,
+ inputs = [rank],
+ outputs = [vote_submit_btn]
+ ).then(
+ reset_vote_text,
+ inputs = [rank],
+ outputs = [vote_textbox]
+ )
+ D4_btn.click(
+ reset_btn_rank,
+ inputs=[window4_text, rank, D4_btn, vote_level],
+ outputs=[rank, vote_level]
+ ).then(
+ add_foreground,
+ inputs=[generate_ig3, vote_level, Top1_text, Top2_text, Top3_text, Top4_text],
+ outputs=[chatbot_right1]
+ ).then(
+ reset_submit,
+ inputs = [rank],
+ outputs = [vote_submit_btn]
+ ).then(
+ reset_vote_text,
+ inputs = [rank],
+ outputs = [vote_textbox]
+ )
\ No newline at end of file
diff --git a/serve/leaderboard.py b/serve/leaderboard.py
new file mode 100644
index 0000000000000000000000000000000000000000..375dbd45dbab817159a93f6ad4e5d0c6ec311682
--- /dev/null
+++ b/serve/leaderboard.py
@@ -0,0 +1,215 @@
+"""
+Live monitor of the website statistics and leaderboard.
+
+Dependency:
+sudo apt install pkg-config libicu-dev
+pip install pytz gradio gdown plotly polyglot pyicu pycld2 tabulate
+"""
+
+import argparse
+import ast
+import pickle
+import os
+import threading
+import time
+
+import gradio as gr
+import numpy as np
+import pandas as pd
+
+
+basic_component_values = [None] * 6
+leader_component_values = [None] * 5
+
+
+# def make_leaderboard_md(elo_results):
+# leaderboard_md = f"""
+# # 🏆 Chatbot Arena Leaderboard
+# | [Blog](https://lmsys.org/blog/2023-05-03-arena/) | [GitHub](https://github.com/lm-sys/FastChat) | [Paper](https://arxiv.org/abs/2306.05685) | [Dataset](https://github.com/lm-sys/FastChat/blob/main/docs/dataset_release.md) | [Twitter](https://twitter.com/lmsysorg) | [Discord](https://discord.gg/HSWAKCrnFx) |
+
+# This leaderboard is based on the following three benchmarks.
+# - [Chatbot Arena](https://lmsys.org/blog/2023-05-03-arena/) - a crowdsourced, randomized battle platform. We use 100K+ user votes to compute Elo ratings.
+# - [MT-Bench](https://arxiv.org/abs/2306.05685) - a set of challenging multi-turn questions. We use GPT-4 to grade the model responses.
+# - [MMLU](https://arxiv.org/abs/2009.03300) (5-shot) - a test to measure a model's multitask accuracy on 57 tasks.
+
+# 💻 Code: The Arena Elo ratings are computed by this [notebook]({notebook_url}). The MT-bench scores (single-answer grading on a scale of 10) are computed by [fastchat.llm_judge](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge). The MMLU scores are mostly computed by [InstructEval](https://github.com/declare-lab/instruct-eval). Higher values are better for all benchmarks. Empty cells mean not available. Last updated: November, 2023.
+# """
+# return leaderboard_md
+
+def make_leaderboard_md(elo_results):
+ leaderboard_md = f"""
+# 🏆 Text2Image-Arena Leaderboard
+"""
+
+ return leaderboard_md
+
+
+def make_leaderboard_md_live(elo_results):
+ leaderboard_md = f"""
+# Leaderboard
+Last updated: {elo_results["last_updated_datetime"]}
+{elo_results["leaderboard_table"]}
+"""
+ return leaderboard_md
+
+
+def model_hyperlink(model_name, link):
+ return f'{model_name}'
+
+
+def load_leaderboard_table_csv(filename, add_hyperlink=True):
+ df = pd.read_csv(filename)
+ for col in df.columns:
+ if "Arena Elo rating" in col:
+ df[col] = df[col].apply(lambda x: int(x) if x != "-" else np.nan)
+ elif col == "MMLU":
+ df[col] = df[col].apply(lambda x: round(x * 100, 1) if x != "-" else np.nan)
+ elif col == "MT-bench (win rate %)":
+ df[col] = df[col].apply(lambda x: round(x, 1) if x != "-" else np.nan)
+ elif col == "MT-bench (score)":
+ df[col] = df[col].apply(lambda x: round(x, 2) if x != "-" else np.nan)
+
+ if add_hyperlink and col == "Model":
+ df[col] = df.apply(lambda row: model_hyperlink(row[col], row["Link"]), axis=1)
+ return df
+
+
+def build_basic_stats_tab():
+ empty = "Loading ..."
+ basic_component_values[:] = [empty, None, empty, empty, empty, empty]
+
+ md0 = gr.Markdown(empty)
+ gr.Markdown("#### Figure 1: Number of model calls and votes")
+ plot_1 = gr.Plot(show_label=False)
+ with gr.Row():
+ with gr.Column():
+ md1 = gr.Markdown(empty)
+ with gr.Column():
+ md2 = gr.Markdown(empty)
+ with gr.Row():
+ with gr.Column():
+ md3 = gr.Markdown(empty)
+ with gr.Column():
+ md4 = gr.Markdown(empty)
+ return [md0, plot_1, md1, md2, md3, md4]
+
+def get_arena_table(arena_df, model_table_df):
+ # sort by rating
+ arena_df = arena_df.sort_values(by=["rating"], ascending=False)
+ values = []
+ for i in range(len(arena_df)):
+ row = []
+ model_key = arena_df.index[i]
+ model_name = model_table_df[model_table_df["key"] == model_key]["Model"].values[
+ 0
+ ]
+
+ # rank
+ row.append(i + 1)
+ # model display name
+ row.append(model_name)
+ # elo rating
+ row.append(0), #round(arena_df.iloc[i]["rating"])
+ upper_diff = round(arena_df.iloc[i]["rating_q975"] - arena_df.iloc[i]["rating"])
+ lower_diff = round(arena_df.iloc[i]["rating"] - arena_df.iloc[i]["rating_q025"])
+ row.append(0) #f"+{upper_diff}/-{lower_diff}"
+ # num battles
+ row.append(0) #round(arena_df.iloc[i]["num_battles"])
+ # Organization
+ row.append(
+ model_table_df[model_table_df["key"] == model_key]["Organization"].values[0]
+ )
+ # license
+ row.append(
+ model_table_df[model_table_df["key"] == model_key]["License"].values[0]
+ )
+
+ values.append(row)
+ return values
+
+def make_arena_leaderboard_md(elo_results):
+ arena_df = elo_results["leaderboard_table_df"]
+ last_updated = elo_results["last_updated_datetime"]
+ total_votes = sum(arena_df["num_battles"]) // 2
+ total_models = len(arena_df)
+
+ leaderboard_md = f"""
+Total #models: **{total_models}**(anonymous). Total #votes: **{total_votes}**. Last updated: {last_updated}.
+"""
+
+ return leaderboard_md
+
+
+def build_leaderboard_tab(elo_results_file, leaderboard_table_file, show_plot=False):
+ if elo_results_file is None: # Do live update
+ md = "Loading ..."
+ p1 = p2 = p3 = p4 = None
+ else:
+ with open(elo_results_file, "rb") as fin:
+ elo_results = pickle.load(fin)
+
+ anony_elo_results = elo_results["anony"]
+ full_elo_results = elo_results["full"]
+ anony_arena_df = anony_elo_results["leaderboard_table_df"]
+ full_arena_df = full_elo_results["leaderboard_table_df"]
+ p1 = anony_elo_results["win_fraction_heatmap"]
+ p2 = anony_elo_results["battle_count_heatmap"]
+ p3 = anony_elo_results["bootstrap_elo_rating"]
+ p4 = anony_elo_results["average_win_rate_bar"]
+
+ md = make_leaderboard_md(anony_elo_results)
+
+ md_1 = gr.Markdown(md, elem_id="leaderboard_markdown")
+
+ if leaderboard_table_file:
+ model_table_df = load_leaderboard_table_csv(leaderboard_table_file)
+ with gr.Tabs() as tabs:
+ # arena table
+ arena_table_vals = get_arena_table(anony_arena_df, model_table_df)
+ with gr.Tab("Arena Elo", id=0):
+ md = make_arena_leaderboard_md(anony_elo_results)
+ gr.Markdown(md, elem_id="leaderboard_markdown")
+ gr.Dataframe(
+ headers=[
+ "Rank",
+ "🤖 Model",
+ "⭐ Arena Elo",
+ "📊 95% CI",
+ "🗳️ Votes",
+ "Organization",
+ "License",
+ ],
+ datatype=[
+ "str",
+ "markdown",
+ "number",
+ "str",
+ "number",
+ "str",
+ "str",
+ ],
+ value=arena_table_vals,
+ elem_id="arena_leaderboard_dataframe",
+ height=700,
+ column_widths=[50, 200, 100, 100, 100, 150, 150],
+ wrap=True,
+ )
+
+ if not show_plot:
+ gr.Markdown(
+ """ ## We are still collecting more votes on more models. The ranking will be updated very fruquently. Please stay tuned!
+ """,
+ elem_id="leaderboard_markdown",
+ )
+ else:
+ pass
+
+ leader_component_values[:] = [md, p1, p2, p3, p4]
+
+
+ from .utils import acknowledgment_md
+
+ gr.Markdown(acknowledgment_md)
+
+ # return [md_1, plot_1, plot_2, plot_3, plot_4]
+ return [md_1]
\ No newline at end of file
diff --git a/serve/log_server.py b/serve/log_server.py
new file mode 100644
index 0000000000000000000000000000000000000000..90e040e36ca1a653ac04dc650d1d2f38daaec705
--- /dev/null
+++ b/serve/log_server.py
@@ -0,0 +1,86 @@
+from fastapi import FastAPI, File, UploadFile, Form, APIRouter
+from typing import Optional
+import json
+import os
+import aiofiles
+from .log_utils import build_logger
+from .constants import LOG_SERVER_SUBDOMAIN, APPEND_JSON, SAVE_IMAGE, SAVE_VIDEO, SAVE_LOG
+
+logger = build_logger("log_server", "log_server.log", add_remote_handler=False)
+
+app = APIRouter(prefix=LOG_SERVER_SUBDOMAIN)
+
+@app.post(f"/{APPEND_JSON}")
+async def append_json(json_str: str = Form(...), file_name: str = Form(...)):
+ """
+ Appends a JSON string to a specified file.
+ """
+ # Convert the string back to a JSON object (dict)
+ data = json.loads(json_str)
+ # Append the data to the specified file
+ if os.path.dirname(file_name):
+ os.makedirs(os.path.dirname(file_name), exist_ok=True)
+ async with aiofiles.open(file_name, mode='a') as f:
+ await f.write(json.dumps(data) + "\n")
+
+ logger.info(f"Appended 1 JSON object to {file_name}")
+ return {"message": "JSON data appended successfully"}
+
+@app.post(f"/{SAVE_IMAGE}")
+async def save_image(image: UploadFile = File(...), image_path: str = Form(...)):
+ """
+ Saves an uploaded image to the specified path.
+ """
+ # Note: 'image_path' should include the file name and extension for the image to be saved.
+ if os.path.dirname(image_path):
+ os.makedirs(os.path.dirname(image_path), exist_ok=True)
+ async with aiofiles.open(image_path, mode='wb') as f:
+ content = await image.read() # Read the content of the uploaded image
+ await f.write(content) # Write the image content to a file
+ logger.info(f"Image saved successfully at {image_path}")
+ return {"message": f"Image saved successfully at {image_path}"}
+
+@app.post(f"/{SAVE_VIDEO}")
+async def save_video(video: UploadFile = File(...), video_path: str = Form(...)):
+ """
+ Saves an uploaded video to the specified path.
+ """
+ # Note: 'video_path' should include the file name and extension for the video to be saved.
+ if os.path.dirname(video_path):
+ os.makedirs(os.path.dirname(video_path), exist_ok=True)
+ async with aiofiles.open(video_path, mode='wb') as f:
+ content = await video.read() # Read the content of the uploaded video
+ await f.write(content) # Write the video content to a file
+ logger.info(f"Video saved successfully at {video_path}")
+ return {"message": f"Image saved successfully at {video_path}"}
+
+@app.post(f"/{SAVE_LOG}")
+async def save_log(message: str = Form(...), log_path: str = Form(...)):
+ """
+ Save a log message to a specified log file on the server.
+ """
+ # Ensure the directory for the log file exists
+ if os.path.dirname(log_path):
+ os.makedirs(os.path.dirname(log_path), exist_ok=True)
+
+ # Append the log message to the specified log file
+ async with aiofiles.open(log_path, mode='a') as f:
+ await f.write(f"{message}\n")
+
+ logger.info(f"Romote log message saved to {log_path}")
+ return {"message": f"Log message saved successfully to {log_path}"}
+
+
+@app.get(f"/read_file")
+async def read_file(file_name: str):
+ """
+ Reads the content of a specified file and returns it.
+ """
+ if not os.path.exists(file_name):
+ return {"message": f"File {file_name} does not exist."}
+
+ async with aiofiles.open(file_name, mode='r') as f:
+ content = await f.read()
+
+ logger.info(f"Read file {file_name}")
+ return {"file_name": file_name, "content": content}
diff --git a/serve/log_utils.py b/serve/log_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..00594acd7f951b19eb5852bc66df0a0eca40136e
--- /dev/null
+++ b/serve/log_utils.py
@@ -0,0 +1,142 @@
+"""
+Common utilities.
+"""
+from asyncio import AbstractEventLoop
+import json
+import logging
+import logging.handlers
+import os
+import platform
+import sys
+from typing import AsyncGenerator, Generator
+import warnings
+from pathlib import Path
+
+import requests
+
+from .constants import LOGDIR, LOG_SERVER_ADDR, SAVE_LOG
+from .utils import save_log_str_on_log_server
+
+
+handler = None
+visited_loggers = set()
+
+
+# Assuming LOGDIR and other necessary imports and global variables are defined
+
+class APIHandler(logging.Handler):
+ """Custom logging handler that sends logs to an API."""
+
+ def __init__(self, apiUrl, log_path, *args, **kwargs):
+ super(APIHandler, self).__init__(*args, **kwargs)
+ self.apiUrl = apiUrl
+ self.log_path = log_path
+
+ def emit(self, record):
+ log_entry = self.format(record)
+ try:
+ save_log_str_on_log_server(log_entry, self.log_path)
+ except requests.RequestException as e:
+ print(f"Error sending log to API: {e}", file=sys.stderr)
+
+def build_logger(logger_name, logger_filename, add_remote_handler=False):
+ global handler
+
+ formatter = logging.Formatter(
+ fmt="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
+ datefmt="%Y-%m-%d %H:%M:%S",
+ )
+
+ # Set the format of root handlers
+ if not logging.getLogger().handlers:
+ if sys.version_info[1] >= 9:
+ # This is for windows
+ logging.basicConfig(level=logging.INFO, encoding="utf-8")
+ else:
+ if platform.system() == "Windows":
+ warnings.warn(
+ "If you are running on Windows, "
+ "we recommend you use Python >= 3.9 for UTF-8 encoding."
+ )
+ logging.basicConfig(level=logging.INFO)
+ logging.getLogger().handlers[0].setFormatter(formatter)
+
+ # Redirect stdout and stderr to loggers
+ stdout_logger = logging.getLogger("stdout")
+ stdout_logger.setLevel(logging.INFO)
+ sl = StreamToLogger(stdout_logger, logging.INFO)
+ sys.stdout = sl
+
+ stderr_logger = logging.getLogger("stderr")
+ stderr_logger.setLevel(logging.ERROR)
+ sl = StreamToLogger(stderr_logger, logging.ERROR)
+ sys.stderr = sl
+
+ # Get logger
+ logger = logging.getLogger(logger_name)
+ logger.setLevel(logging.INFO)
+
+ if add_remote_handler:
+ # Add APIHandler to send logs to your API
+ api_url = f"{LOG_SERVER_ADDR}/{SAVE_LOG}"
+
+ remote_logger_filename = str(Path(logger_filename).stem + "_remote.log")
+ api_handler = APIHandler(apiUrl=api_url, log_path=f"{LOGDIR}/{remote_logger_filename}")
+ api_handler.setFormatter(formatter)
+ logger.addHandler(api_handler)
+
+ stdout_logger.addHandler(api_handler)
+ stderr_logger.addHandler(api_handler)
+
+ # if LOGDIR is empty, then don't try output log to local file
+ if LOGDIR != "":
+ os.makedirs(LOGDIR, exist_ok=True)
+ filename = os.path.join(LOGDIR, logger_filename)
+ handler = logging.handlers.TimedRotatingFileHandler(
+ filename, when="D", utc=True, encoding="utf-8"
+ )
+ handler.setFormatter(formatter)
+
+ for l in [stdout_logger, stderr_logger, logger]:
+ if l in visited_loggers:
+ continue
+ visited_loggers.add(l)
+ l.addHandler(handler)
+
+ return logger
+
+
+class StreamToLogger(object):
+ """
+ Fake file-like stream object that redirects writes to a logger instance.
+ """
+
+ def __init__(self, logger, log_level=logging.INFO):
+ self.terminal = sys.stdout
+ self.logger = logger
+ self.log_level = log_level
+ self.linebuf = ""
+
+ def __getattr__(self, attr):
+ return getattr(self.terminal, attr)
+
+ def write(self, buf):
+ temp_linebuf = self.linebuf + buf
+ self.linebuf = ""
+ for line in temp_linebuf.splitlines(True):
+ # From the io.TextIOWrapper docs:
+ # On output, if newline is None, any '\n' characters written
+ # are translated to the system default line separator.
+ # By default sys.stdout.write() expects '\n' newlines and then
+ # translates them so this is still cross platform.
+ if line[-1] == "\n":
+ encoded_message = line.encode("utf-8", "ignore").decode("utf-8")
+ self.logger.log(self.log_level, encoded_message.rstrip())
+ else:
+ self.linebuf += line
+
+ def flush(self):
+ if self.linebuf != "":
+ encoded_message = self.linebuf.encode("utf-8", "ignore").decode("utf-8")
+ self.logger.log(self.log_level, encoded_message.rstrip())
+ self.linebuf = ""
\ No newline at end of file
diff --git a/serve/update_skill.py b/serve/update_skill.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7d1dd417c31ccb9919edad0ab6ddce424f68b28
--- /dev/null
+++ b/serve/update_skill.py
@@ -0,0 +1,86 @@
+import numpy as np
+import json
+from trueskill import TrueSkill
+import paramiko
+import io, os
+import sys
+from serve.constants import SSH_SERVER, SSH_PORT, SSH_USER, SSH_PASSWORD, SSH_SKILL, SSH_SKILL_M
+trueskill_env = TrueSkill()
+
+class Model_ID(object):
+ group = []
+
+
+def ucb_score(trueskill_diff, t, n):
+ exploration_term = np.sqrt((2 * np.log(t + 1e-5)) / (n + 1e-5))
+ ucb = -trueskill_diff + 1.0 * exploration_term
+ return ucb
+
+def update_trueskill(ratings, ranks):
+ new_ratings = trueskill_env.rate(ratings, ranks)
+ return new_ratings
+
+def serialize_rating(rating):
+ return {'mu': rating.mu, 'sigma': rating.sigma}
+
+def deserialize_rating(rating_dict):
+ return trueskill_env.Rating(mu=rating_dict['mu'], sigma=rating_dict['sigma'])
+
+def create_ssh_client(server, port, user, password):
+ ssh = paramiko.SSHClient()
+ ssh.load_system_host_keys()
+ ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())
+ ssh.connect(server, port, user, password)
+ return ssh
+
+def save_json_via_sftp(ratings, comparison_counts, total_comparisons):
+ ssh = create_ssh_client(SSH_SERVER, SSH_PORT, SSH_USER, SSH_PASSWORD)
+ data = {
+ 'ratings': [serialize_rating(r) for r in ratings],
+ 'comparison_counts': comparison_counts.tolist(),
+ 'total_comparisons': total_comparisons
+ }
+ json_data = json.dumps(data)
+ sftp = ssh.open_sftp()
+ with sftp.open(SSH_SKILL, 'w') as f:
+ f.write(json_data)
+
+def load_json_via_sftp():
+ ssh = create_ssh_client(SSH_SERVER, SSH_PORT, SSH_USER, SSH_PASSWORD)
+ sftp = ssh.open_sftp()
+ with sftp.open(SSH_SKILL, 'r') as f:
+ data = json.load(f)
+ ratings = [deserialize_rating(r) for r in data['ratings']]
+ comparison_counts = np.array(data['comparison_counts'])
+ total_comparisons = data['total_comparisons']
+ return ratings, comparison_counts, total_comparisons
+
+
+def load_modelid_via_sftp():
+ ssh = create_ssh_client(SSH_SERVER, SSH_PORT, SSH_USER, SSH_PASSWORD)
+ sftp = ssh.open_sftp()
+ with sftp.open(SSH_SKILL_M, 'r') as f:
+ data = json.load(f)
+ model_ids = data['model_ids']
+ return model_ids
+
+
+def update_skill(rank, k_group=4):
+
+ ratings, comparison_counts, total_comparisons = load_json_via_sftp()
+ group = Model_ID.group
+ pairwise_comparisons = [(i, j) for i in range(len(group)) for j in range(i+1, len(group))]
+ for player1, player2 in pairwise_comparisons:
+ if rank[player1] < rank[player2]:
+ ranks = [0, 1]
+ updated_ratings = update_trueskill([[ratings[group[player1]]], [ratings[group[player2]]]], ranks)
+ ratings[group[player1]], ratings[group[player2]] = updated_ratings[0][0], updated_ratings[1][0]
+ elif rank[player1] > rank[player2]:
+ ranks = [1, 0]
+ updated_ratings = update_trueskill([[ratings[group[player1]]], [ratings[group[player2]]]], ranks)
+ ratings[group[player1]], ratings[group[player2]] = updated_ratings[0][0], updated_ratings[1][0]
+
+ comparison_counts[group[player1], group[player2]] += 1
+ comparison_counts[group[player2], group[player1]] += 1
+
+ save_json_via_sftp(ratings, comparison_counts, total_comparisons)
\ No newline at end of file
diff --git a/serve/upload.py b/serve/upload.py
new file mode 100644
index 0000000000000000000000000000000000000000..4459b4df26bd3107c76ad25e43c3d507afe8fc20
--- /dev/null
+++ b/serve/upload.py
@@ -0,0 +1,60 @@
+import paramiko
+import numpy as np
+import io, os
+from PIL import Image
+import requests
+import json
+from .constants import SSH_SERVER, SSH_PORT, SSH_USER, SSH_PASSWORD, SSH_LOG
+
+def create_ssh_client(server, port, user, password):
+ ssh = paramiko.SSHClient()
+ ssh.load_system_host_keys()
+ ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())
+ ssh.connect(server, port, user, password)
+ return ssh
+
+def get_image_from_url(image_url):
+ response = requests.get(image_url)
+ response.raise_for_status() # success
+ return Image.open(io.BytesIO(response.content))
+
+def upload_image_via_sftp(ssh, image, remote_image_path):
+ if isinstance(image, str):
+ print("get url image")
+ image = get_image_from_url(image)
+ with ssh.open_sftp() as sftp:
+ with io.BytesIO() as image_byte_stream:
+ image.save(image_byte_stream, format='JPEG')
+ image_byte_stream.seek(0)
+ sftp.putfo(image_byte_stream, remote_image_path)
+ print(f"Successfully uploaded image to {remote_image_path}")
+
+def upload_json_via_sftp(ssh, data, remote_json_path):
+ json_data = json.dumps(data, indent=4)
+ with ssh.open_sftp() as sftp:
+ with io.BytesIO(json_data.encode('utf-8')) as json_byte_stream:
+ sftp.putfo(json_byte_stream, remote_json_path)
+ print(f"Successfully uploaded JSON data to {remote_json_path}")
+
+def upload_image(states, output_dir):
+ ssh = create_ssh_client(SSH_SERVER, SSH_PORT, SSH_USER, SSH_PASSWORD)
+ for i in range(len(states)):
+ output_file = os.path.join(output_dir, f"{i}.jpg")
+ upload_image_via_sftp(ssh, states[i].output, output_file)
+
+def upload_informance(data, data_path):
+ ssh = create_ssh_client(SSH_SERVER, SSH_PORT, SSH_USER, SSH_PASSWORD)
+ upload_json_via_sftp(ssh, data, data_path)
+
+def create_remote_directory(remote_directory):
+ ssh = create_ssh_client(SSH_SERVER, SSH_PORT, SSH_USER, SSH_PASSWORD)
+
+ stdin, stdout, stderr = ssh.exec_command(f'mkdir -p {SSH_LOG}/{remote_directory}')
+ error = stderr.read().decode('utf-8')
+ if error:
+ print(f"Error: {error}")
+ else:
+ print(f"Directory {remote_directory} created successfully.")
+ # ssh.close()
+ return f'{SSH_LOG}/{remote_directory}'
+
\ No newline at end of file
diff --git a/serve/utils.py b/serve/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..07e810f58762d2697410f0923bde9f87a2e60828
--- /dev/null
+++ b/serve/utils.py
@@ -0,0 +1,204 @@
+import os
+import json
+import datetime
+import requests
+import numpy as np
+import gradio as gr
+from pathlib import Path
+from model.model_registry import *
+from .constants import LOGDIR, LOG_SERVER_ADDR, APPEND_JSON, SAVE_IMAGE, SAVE_VIDEO, SAVE_LOG
+from typing import Union
+
+
+enable_btn = gr.update(interactive=True, visible=True)
+disable_btn = gr.update(interactive=False)
+invisible_btn = gr.update(interactive=False, visible=False)
+no_change_btn = gr.update(value="No Change", interactive=True, visible=True)
+
+
+def build_about():
+ about_markdown = f"""
+# About Us
+This is a project from TIGER Lab at University of Waterloo.
+
+## Contributors:
+[Tianle Li](https://scholar.google.com/citations?user=g213g7YAAAAJ&hl=en), [Dongfu Jiang](https://jdf-prog.github.io/), [Yuansheng Ni](https://yuanshengni.github.io/), [Max Ku](https://kuwingfung.github.io/), [Shizhuo Sun](), [Richard Fan](https://www.linkedin.com/in/richard-fan2020/).
+
+## Contact:
+Email: t29li@uwaterloo.ca (Tianle Li)
+
+## Advisors
+[Wenhu Chen](https://wenhuchen.github.io/)
+
+## Sponsorship
+We are keep looking for sponsorship to support the arena project for the long term. Please contact us if you are interested in supporting this project.
+
+## Acknowledgment
+Our codebase is built upon FastChat, ImagenHub and VideoGenHub.
+"""
+
+ gr.Markdown(about_markdown, elem_id="about_markdown")
+
+
+acknowledgment_md = """
+### Acknowledgment
+
+"""
+
+block_css = """
+#notice_markdown {
+ font-size: 110%
+}
+#notice_markdown th {
+ display: none;
+}
+#notice_markdown td {
+ padding-top: 6px;
+ padding-bottom: 6px;
+}
+#model_description_markdown {
+ font-size: 110%
+}
+#leaderboard_markdown {
+ font-size: 110%
+}
+#leaderboard_markdown td {
+ padding-top: 6px;
+ padding-bottom: 6px;
+}
+#leaderboard_dataframe td {
+ line-height: 0.1em;
+}
+#about_markdown {
+ font-size: 110%
+}
+#ack_markdown {
+ font-size: 110%
+}
+#input_box textarea {
+}
+footer {
+ display:none !important
+}
+.image-about img {
+ margin: 0 30px;
+ margin-top: 30px;
+ height: 60px;
+ max-height: 100%;
+ width: auto;
+ float: left;
+.input-image, .image-preview {
+ margin: 0 30px;
+ height: 30px;
+ max-height: 100%;
+ width: auto;
+ max-width: 30%;}
+}
+"""
+
+def enable_vote_buttons():
+ return tuple(gr.update(visible=True, interactive=i<=4) for i in range(6))
+def disable_vote_buttons():
+ return tuple(gr.update(visible=False, interactive=False) for i in range(6))
+
+def enable_vote_mode_buttons(mode):
+ print(mode)
+ if mode == "Best":
+ return (gr.update(visible=True, interactive=True),) * 6 + \
+ (gr.update(visible=False, interactive=False),) * 16 + \
+ (gr.update(visible=True, interactive=False),) * 2 + \
+ (gr.update(visible=True, interactive=True),)
+ elif mode == "Rank":
+ return (gr.update(visible=False, interactive=False),) * 6 + \
+ (gr.update(visible=True, interactive=True),) * 16 + \
+ (gr.update(visible=True, interactive=True),) * 3
+def disable_vote_mode_buttons():
+ return tuple(gr.update(visible=False, interactive=False) for _ in range(25))
+
+
+def enable_order_buttons():
+ return tuple(gr.update(interactive=True) for _ in range(4))
+def disable_order_buttons():
+ return (gr.update(interactive=False), gr.update(interactive=False), gr.update(interactive=True), gr.update(interactive=False))
+
+def clear_history():
+ return None, "", None
+
+def clear_history_side_by_side():
+ return None, None, "", None, None
+
+def clear_history_side_by_side_anony():
+ return None, None, None, None, gr.update(visible=True, interactive=True, value=""), gr.update(visible=True, interactive=True, value=""), None, None, None, None, \
+ gr.Markdown("", visible=False), gr.Markdown("", visible=False), gr.Markdown("", visible=False), gr.Markdown("", visible=False)
+
+def clear_history_ie():
+ return None, "", "", "", None, None
+
+def clear_history_side_by_side_ie():
+ return None, None, "", "", "", None, None, None
+
+def clear_history_side_by_side_ie_anony():
+ return None, None, "", "", "", None, None, None, gr.Markdown("", visible=False), gr.Markdown("", visible=False)
+
+def get_ip(request: gr.Request):
+ if request:
+ if "cf-connecting-ip" in request.headers:
+ ip = request.headers["cf-connecting-ip"] or request.client.host
+ else:
+ ip = request.client.host
+ else:
+ ip = None
+ return ip
+
+def get_conv_log_filename():
+ t = datetime.datetime.now()
+ name = os.path.join(LOGDIR, f"{t.year}-{t.month:02d}-{t.day:02d}-conv.json")
+ return name
+
+def save_image_file_on_log_server(image_file:str):
+ return 1
+
+ image_file = Path(image_file).absolute().relative_to(os.getcwd())
+ image_file = str(image_file)
+ # Open the image file in binary mode
+ url = f"{LOG_SERVER_ADDR}/{SAVE_IMAGE}"
+ with open(image_file, 'rb') as f:
+ # Make the POST request, sending the image file and the image path
+ response = requests.post(url, files={'image': f}, data={'image_path': image_file})
+ return response
+
+def save_video_file_on_log_server(video_file:str):
+ return 1
+
+ video_file = Path(video_file).absolute().relative_to(os.getcwd())
+ video_file = str(video_file)
+ # Open the video file in binary mode
+ url = f"{LOG_SERVER_ADDR}/{SAVE_VIDEO}"
+ with open(video_file, 'rb') as f:
+ # Make the POST request, sending the video file and the video path
+ response = requests.post(url, files={'video': f}, data={'video_path': video_file})
+ return response
+
+def append_json_item_on_log_server(json_item: Union[dict, str], log_file: str):
+ return 1
+
+ if isinstance(json_item, dict):
+ json_item = json.dumps(json_item)
+ log_file = Path(log_file).absolute().relative_to(os.getcwd())
+ log_file = str(log_file)
+ url = f"{LOG_SERVER_ADDR}/{APPEND_JSON}"
+ # Make the POST request, sending the JSON string and the log file name
+ response = requests.post(url, data={'json_str': json_item, 'file_name': log_file})
+ return response
+
+def save_log_str_on_log_server(log_str: str, log_file: str):
+ return 1
+
+ log_file = Path(log_file).absolute().relative_to(os.getcwd())
+ log_file = str(log_file)
+ url = f"{LOG_SERVER_ADDR}/{SAVE_LOG}"
+ # Make the POST request, sending the log message and the log file name
+ response = requests.post(url, data={'message': log_str, 'log_path': log_file})
+ return response
\ No newline at end of file
diff --git a/serve/vote_utils.py b/serve/vote_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..e81b37f284c592ed6706c7870218f29aa91ab543
--- /dev/null
+++ b/serve/vote_utils.py
@@ -0,0 +1,1560 @@
+import datetime
+import time
+import json
+import uuid
+import gradio as gr
+import regex as re
+from pathlib import Path
+from .utils import *
+from .log_utils import build_logger
+from .constants import IMAGE_DIR, VIDEO_DIR
+import imageio
+from diffusers.utils import load_image
+
+ig_logger = build_logger("gradio_web_server_image_generation", "gr_web_image_generation.log") # ig = image generation, loggers for single model direct chat
+igm_logger = build_logger("gradio_web_server_image_generation_multi", "gr_web_image_generation_multi.log") # igm = image generation multi, loggers for side-by-side and battle
+ie_logger = build_logger("gradio_web_server_image_editing", "gr_web_image_editing.log") # ie = image editing, loggers for single model direct chat
+iem_logger = build_logger("gradio_web_server_image_editing_multi", "gr_web_image_editing_multi.log") # iem = image editing multi, loggers for side-by-side and battle
+vg_logger = build_logger("gradio_web_server_video_generation", "gr_web_video_generation.log") # vg = video generation, loggers for single model direct chat
+vgm_logger = build_logger("gradio_web_server_video_generation_multi", "gr_web_video_generation_multi.log") # vgm = video generation multi, loggers for side-by-side and battle
+
+def save_any_image(image_file, file_path):
+ if isinstance(image_file, str):
+ image = load_image(image_file)
+ image.save(file_path, 'JPEG')
+ else:
+ image_file.save(file_path, 'JPEG')
+
+def vote_last_response_ig(state, vote_type, model_selector, request: gr.Request):
+ with open(get_conv_log_filename(), "a") as fout:
+ data = {
+ "tstamp": round(time.time(), 4),
+ "type": vote_type,
+ "model": model_selector,
+ "state": state.dict(),
+ "ip": get_ip(request),
+ }
+ fout.write(json.dumps(data) + "\n")
+ append_json_item_on_log_server(data, get_conv_log_filename())
+ output_file = f'{IMAGE_DIR}/generation/{state.conv_id}.jpg'
+ with open(output_file, 'w') as f:
+ save_any_image(state.output, f)
+ save_image_file_on_log_server(output_file)
+
+def vote_last_response_igm(states, vote_type, model_selectors, request: gr.Request):
+ with open(get_conv_log_filename(), "a") as fout:
+ data = {
+ "tstamp": round(time.time(), 4),
+ "type": vote_type,
+ "models": [x for x in model_selectors],
+ "states": [x.dict() for x in states],
+ "ip": get_ip(request),
+ }
+ fout.write(json.dumps(data) + "\n")
+ append_json_item_on_log_server(data, get_conv_log_filename())
+ for state in states:
+ print(state.conv_id)
+ output_file = f'{IMAGE_DIR}/generation/{state.conv_id}.jpg'
+ with open(output_file, 'w') as f:
+ save_any_image(state.output, f)
+ save_image_file_on_log_server(output_file)
+
+def vote_last_response_ie(state, vote_type, model_selector, request: gr.Request):
+ with open(get_conv_log_filename(), "a") as fout:
+ data = {
+ "tstamp": round(time.time(), 4),
+ "type": vote_type,
+ "model": model_selector,
+ "state": state.dict(),
+ "ip": get_ip(request),
+ }
+ fout.write(json.dumps(data) + "\n")
+ append_json_item_on_log_server(data, get_conv_log_filename())
+ output_file = f'{IMAGE_DIR}/edition/{state.conv_id}.jpg'
+ source_file = f'{IMAGE_DIR}/edition/{state.conv_id}_source.jpg'
+ with open(output_file, 'w') as f:
+ save_any_image(state.output, f)
+ with open(source_file, 'w') as sf:
+ save_any_image(state.source_image, sf)
+ save_image_file_on_log_server(output_file)
+ save_image_file_on_log_server(source_file)
+
+def vote_last_response_iem(states, vote_type, model_selectors, request: gr.Request):
+ with open(get_conv_log_filename(), "a") as fout:
+ data = {
+ "tstamp": round(time.time(), 4),
+ "type": vote_type,
+ "models": [x for x in model_selectors],
+ "states": [x.dict() for x in states],
+ "ip": get_ip(request),
+ }
+ fout.write(json.dumps(data) + "\n")
+ append_json_item_on_log_server(data, get_conv_log_filename())
+ for state in states:
+ output_file = f'{IMAGE_DIR}/edition/{state.conv_id}.jpg'
+ source_file = f'{IMAGE_DIR}/edition/{state.conv_id}_source.jpg'
+ with open(output_file, 'w') as f:
+ save_any_image(state.output, f)
+ with open(source_file, 'w') as sf:
+ save_any_image(state.source_image, sf)
+ save_image_file_on_log_server(output_file)
+ save_image_file_on_log_server(source_file)
+
+
+def vote_last_response_vg(state, vote_type, model_selector, request: gr.Request):
+ with open(get_conv_log_filename(), "a") as fout:
+ data = {
+ "tstamp": round(time.time(), 4),
+ "type": vote_type,
+ "model": model_selector,
+ "state": state.dict(),
+ "ip": get_ip(request),
+ }
+ fout.write(json.dumps(data) + "\n")
+ append_json_item_on_log_server(data, get_conv_log_filename())
+
+ output_file = f'{VIDEO_DIR}/generation/{state.conv_id}.mp4'
+ os.makedirs(os.path.dirname(output_file), exist_ok=True)
+ if state.model_name.startswith('fal'):
+ r = requests.get(state.output)
+ with open(output_file, 'wb') as outfile:
+ outfile.write(r.content)
+ else:
+ print("======== video shape: ========")
+ print(state.output.shape)
+ imageio.mimwrite(output_file, state.output, fps=8, quality=9)
+ save_video_file_on_log_server(output_file)
+
+
+
+def vote_last_response_vgm(states, vote_type, model_selectors, request: gr.Request):
+ with open(get_conv_log_filename(), "a") as fout:
+ data = {
+ "tstamp": round(time.time(), 4),
+ "type": vote_type,
+ "models": [x for x in model_selectors],
+ "states": [x.dict() for x in states],
+ "ip": get_ip(request),
+ }
+ fout.write(json.dumps(data) + "\n")
+ append_json_item_on_log_server(data, get_conv_log_filename())
+ for state in states:
+ output_file = f'{VIDEO_DIR}/generation/{state.conv_id}.mp4'
+ os.makedirs(os.path.dirname(output_file), exist_ok=True)
+ if state.model_name.startswith('fal'):
+ r = requests.get(state.output)
+ with open(output_file, 'wb') as outfile:
+ outfile.write(r.content)
+ else:
+ print("======== video shape: ========")
+ print(state.output.shape)
+ imageio.mimwrite(output_file, state.output, fps=8, quality=9)
+ save_video_file_on_log_server(output_file)
+
+
+## Image Generation (IG) Single Model Direct Chat
+def upvote_last_response_ig(state, model_selector, request: gr.Request):
+ ip = get_ip(request)
+ ig_logger.info(f"upvote. ip: {ip}")
+ vote_last_response_ig(state, "upvote", model_selector, request)
+ return ("",) + (disable_btn,) * 3
+
+def downvote_last_response_ig(state, model_selector, request: gr.Request):
+ ip = get_ip(request)
+ ig_logger.info(f"downvote. ip: {ip}")
+ vote_last_response_ig(state, "downvote", model_selector, request)
+ return ("",) + (disable_btn,) * 3
+
+
+def flag_last_response_ig(state, model_selector, request: gr.Request):
+ ip = get_ip(request)
+ ig_logger.info(f"flag. ip: {ip}")
+ vote_last_response_ig(state, "flag", model_selector, request)
+ return ("",) + (disable_btn,) * 3
+
+## Image Generation Multi (IGM) Side-by-Side and Battle
+
+def leftvote_last_response_igm(
+ state0, state1, state2, state3, model_selector0, model_selector1, model_selector2, model_selector3, request: gr.Request
+):
+ igm_logger.info(f"leftvote (named). ip: {get_ip(request)}")
+ vote_last_response_igm(
+ [state0, state1, state2, state3], "leftvote", [model_selector0, model_selector1, model_selector2, model_selector3], request
+ )
+ if model_selector0 == "":
+ return ("",) + (disable_btn,) * 6 + (
+ gr.Markdown(f"### Model A: {state0.model_name.split('_')[1]}", visible=True),
+ gr.Markdown(f"### Model B: {state1.model_name.split('_')[1]}", visible=True),
+ gr.Markdown(f"### Model C: {state2.model_name.split('_')[1]}", visible=True),
+ gr.Markdown(f"### Model D: {state3.model_name.split('_')[1]}", visible=True)
+ )
+ else:
+ return ("",) + (disable_btn,) * 6 + (
+ gr.Markdown(state0.model_name, visible=True),
+ gr.Markdown(state1.model_name, visible=True),
+ gr.Markdown(state2.model_name, visible=True),
+ gr.Markdown(state3.model_name, visible=True)
+ )
+def left1vote_last_response_igm(
+ state0, state1, state2, state3, model_selector0, model_selector1, model_selector2, model_selector3, request: gr.Request
+):
+ igm_logger.info(f"left1vote (named). ip: {get_ip(request)}")
+ vote_last_response_igm(
+ [state0, state1, state2, state3], "left1vote", [model_selector0, model_selector1, model_selector2, model_selector3], request
+ )
+ if model_selector0 == "":
+ return ("",) + (disable_btn,) * 6 + (
+ gr.Markdown(f"### Model A: {state0.model_name.split('_')[1]}", visible=True),
+ gr.Markdown(f"### Model B: {state1.model_name.split('_')[1]}", visible=True),
+ gr.Markdown(f"### Model C: {state2.model_name.split('_')[1]}", visible=True),
+ gr.Markdown(f"### Model D: {state3.model_name.split('_')[1]}", visible=True)
+ )
+ else:
+ return ("",) + (disable_btn,) * 6 + (
+ gr.Markdown(state0.model_name, visible=True),
+ gr.Markdown(state1.model_name, visible=True),
+ gr.Markdown(state2.model_name, visible=True),
+ gr.Markdown(state3.model_name, visible=True)
+ )
+def rightvote_last_response_igm(
+ state0, state1, state2, state3, model_selector0, model_selector1, model_selector2, model_selector3, request: gr.Request
+):
+ igm_logger.info(f"rightvote (named). ip: {get_ip(request)}")
+ vote_last_response_igm(
+ [state0, state1, state2, state3], "rightvote", [model_selector0, model_selector1, model_selector2, model_selector3], request
+ )
+ # print(model_selector0)
+ if model_selector0 == "":
+ return ("",) + (disable_btn,) * 6 + (
+ gr.Markdown(f"### Model A: {state0.model_name.split('_')[1]}", visible=True),
+ gr.Markdown(f"### Model B: {state1.model_name.split('_')[1]}", visible=True),
+ gr.Markdown(f"### Model C: {state2.model_name.split('_')[1]}", visible=True),
+ gr.Markdown(f"### Model D: {state3.model_name.split('_')[1]}", visible=True)
+ )
+ else:
+ return ("",) + (disable_btn,) * 6 + (
+ gr.Markdown(state0.model_name, visible=True),
+ gr.Markdown(state1.model_name, visible=True),
+ gr.Markdown(state2.model_name, visible=True),
+ gr.Markdown(state3.model_name, visible=True)
+ )
+def right1vote_last_response_igm(
+ state0, state1, state2, state3, model_selector0, model_selector1, model_selector2, model_selector3, request: gr.Request
+):
+ igm_logger.info(f"right1vote (named). ip: {get_ip(request)}")
+ vote_last_response_igm(
+ [state0, state1, state2, state3], "right1vote", [model_selector0, model_selector1, model_selector2, model_selector3], request
+ )
+ # print(model_selector0)
+ if model_selector0 == "":
+ return ("",) + (disable_btn,) * 6 + (
+ gr.Markdown(f"### Model A: {state0.model_name.split('_')[1]}", visible=True),
+ gr.Markdown(f"### Model B: {state1.model_name.split('_')[1]}", visible=True),
+ gr.Markdown(f"### Model C: {state2.model_name.split('_')[1]}", visible=True),
+ gr.Markdown(f"### Model D: {state3.model_name.split('_')[1]}", visible=True)
+ )
+ else:
+ return ("",) + (disable_btn,) * 6 + (
+ gr.Markdown(state0.model_name, visible=True),
+ gr.Markdown(state1.model_name, visible=True),
+ gr.Markdown(state2.model_name, visible=True),
+ gr.Markdown(state3.model_name, visible=True)
+ )
+
+def tievote_last_response_igm(
+ state0, state1, state2, state3, model_selector0, model_selector1, model_selector2, model_selector3, request: gr.Request
+):
+ igm_logger.info(f"tievote (named). ip: {get_ip(request)}")
+ vote_last_response_igm(
+ [state0, state1, state2, state3], "tievote", [model_selector0, model_selector1, model_selector2, model_selector3], request
+ )
+ if model_selector0 == "":
+ return ("",) + (disable_btn,) * 6 + (
+ gr.Markdown(f"### Model A: {state0.model_name.split('_')[1]}", visible=True),
+ gr.Markdown(f"### Model B: {state1.model_name.split('_')[1]}", visible=True),
+ gr.Markdown(f"### Model C: {state2.model_name.split('_')[1]}", visible=True),
+ gr.Markdown(f"### Model D: {state3.model_name.split('_')[1]}", visible=True)
+ )
+ else:
+ return ("",) + (disable_btn,) * 6 + (
+ gr.Markdown(state0.model_name, visible=True),
+ gr.Markdown(state1.model_name, visible=True),
+ gr.Markdown(state2.model_name, visible=True),
+ gr.Markdown(state3.model_name, visible=True)
+ )
+
+
+def bothbad_vote_last_response_igm(
+ state0, state1, state2, state3, model_selector0, model_selector1, model_selector2, model_selector3, request: gr.Request
+):
+ igm_logger.info(f"bothbad_vote (named). ip: {get_ip(request)}")
+ vote_last_response_igm(
+ [state0, state1, state2, state3], "bothbad_vote", [model_selector0, model_selector1, model_selector2, model_selector3], request
+ )
+ if model_selector0 == "":
+ return ("",) + (disable_btn,) * 6 + (
+ gr.Markdown(f"### Model A: {state0.model_name.split('_')[1]}", visible=True),
+ gr.Markdown(f"### Model B: {state1.model_name.split('_')[1]}", visible=True),
+ gr.Markdown(f"### Model C: {state2.model_name.split('_')[1]}", visible=True),
+ gr.Markdown(f"### Model D: {state3.model_name.split('_')[1]}", visible=True)
+ )
+ else:
+ return ("",) + (disable_btn,) * 6 + (
+ gr.Markdown(state0.model_name, visible=True),
+ gr.Markdown(state1.model_name, visible=True),
+ gr.Markdown(state2.model_name, visible=True),
+ gr.Markdown(state3.model_name, visible=True)
+ )
+
+## Image Editing (IE) Single Model Direct Chat
+
+def upvote_last_response_ie(state, model_selector, request: gr.Request):
+ ip = get_ip(request)
+ ie_logger.info(f"upvote. ip: {ip}")
+ vote_last_response_ie(state, "upvote", model_selector, request)
+ return ("", "", gr.Image(height=512, width=512, type="pil"), "",) + (disable_btn,) * 3
+
+def downvote_last_response_ie(state, model_selector, request: gr.Request):
+ ip = get_ip(request)
+ ie_logger.info(f"downvote. ip: {ip}")
+ vote_last_response_ie(state, "downvote", model_selector, request)
+ return ("", "", gr.Image(height=512, width=512, type="pil"), "",) + (disable_btn,) * 3
+
+def flag_last_response_ie(state, model_selector, request: gr.Request):
+ ip = get_ip(request)
+ ie_logger.info(f"flag. ip: {ip}")
+ vote_last_response_ie(state, "flag", model_selector, request)
+ return ("", "", gr.Image(height=512, width=512, type="pil"), "",) + (disable_btn,) * 3
+
+## Image Editing Multi (IEM) Side-by-Side and Battle
+def leftvote_last_response_iem(
+ state0, state1, model_selector0, model_selector1, request: gr.Request
+):
+ iem_logger.info(f"leftvote (anony). ip: {get_ip(request)}")
+ vote_last_response_iem(
+ [state0, state1], "leftvote", [model_selector0, model_selector1], request
+ )
+ # names = (
+ # "### Model A: " + state0.model_name,
+ # "### Model B: " + state1.model_name,
+ # )
+ # names = (state0.model_name, state1.model_name)
+ if model_selector0 == "":
+ names = (gr.Markdown(f"### Model A: {state0.model_name.split('_')[1]}", visible=True), gr.Markdown(f"### Model B: {state1.model_name.split('_')[1]}", visible=True))
+ else:
+ names = (gr.Markdown(state0.model_name, visible=False), gr.Markdown(state1.model_name, visible=False))
+ return names + ("", "", gr.Image(height=512, width=512, type="pil"), "") + (disable_btn,) * 4
+
+def rightvote_last_response_iem(
+ state0, state1, model_selector0, model_selector1, request: gr.Request
+):
+ iem_logger.info(f"rightvote (anony). ip: {get_ip(request)}")
+ vote_last_response_iem(
+ [state0, state1], "rightvote", [model_selector0, model_selector1], request
+ )
+ # names = (
+ # "### Model A: " + state0.model_name,
+ # "### Model B: " + state1.model_name,
+ # )
+ if model_selector0 == "":
+ names = (gr.Markdown(f"### Model A: {state0.model_name.split('_')[1]}", visible=True),
+ gr.Markdown(f"### Model B: {state1.model_name.split('_')[1]}", visible=True))
+ else:
+ names = (gr.Markdown(state0.model_name, visible=False), gr.Markdown(state1.model_name, visible=False))
+ return names + ("", "", gr.Image(height=512, width=512, type="pil"), "") + (disable_btn,) * 4
+
+def tievote_last_response_iem(
+ state0, state1, model_selector0, model_selector1, request: gr.Request
+):
+ iem_logger.info(f"tievote (anony). ip: {get_ip(request)}")
+ vote_last_response_iem(
+ [state0, state1], "tievote", [model_selector0, model_selector1], request
+ )
+ if model_selector0 == "":
+ names = (gr.Markdown(f"### Model A: {state0.model_name.split('_')[1]}", visible=True),
+ gr.Markdown(f"### Model B: {state1.model_name.split('_')[1]}", visible=True))
+ else:
+ names = (gr.Markdown(state0.model_name, visible=False), gr.Markdown(state1.model_name, visible=False))
+ return names + ("", "", gr.Image(height=512, width=512, type="pil"), "") + (disable_btn,) * 4
+
+def bothbad_vote_last_response_iem(
+ state0, state1, model_selector0, model_selector1, request: gr.Request
+):
+ iem_logger.info(f"bothbad_vote (anony). ip: {get_ip(request)}")
+ vote_last_response_iem(
+ [state0, state1], "bothbad_vote", [model_selector0, model_selector1], request
+ )
+ if model_selector0 == "":
+ names = (gr.Markdown(f"### Model A: {state0.model_name.split('_')[1]}", visible=True),
+ gr.Markdown(f"### Model B: {state1.model_name.split('_')[1]}", visible=True))
+ else:
+ names = (gr.Markdown(state0.model_name, visible=False), gr.Markdown(state1.model_name, visible=False))
+ return names + ("", "", gr.Image(height=512, width=512, type="pil"), "") + (disable_btn,) * 4
+
+
+## Video Generation (VG) Single Model Direct Chat
+def upvote_last_response_vg(state, model_selector, request: gr.Request):
+ ip = get_ip(request)
+ vg_logger.info(f"upvote. ip: {ip}")
+ vote_last_response_vg(state, "upvote", model_selector, request)
+ return ("",) + (disable_btn,) * 3
+
+def downvote_last_response_vg(state, model_selector, request: gr.Request):
+ ip = get_ip(request)
+ vg_logger.info(f"downvote. ip: {ip}")
+ vote_last_response_vg(state, "downvote", model_selector, request)
+ return ("",) + (disable_btn,) * 3
+
+
+def flag_last_response_vg(state, model_selector, request: gr.Request):
+ ip = get_ip(request)
+ vg_logger.info(f"flag. ip: {ip}")
+ vote_last_response_vg(state, "flag", model_selector, request)
+ return ("",) + (disable_btn,) * 3
+
+## Image Generation Multi (IGM) Side-by-Side and Battle
+
+def leftvote_last_response_vgm(
+ state0, state1, model_selector0, model_selector1, request: gr.Request
+):
+ vgm_logger.info(f"leftvote (named). ip: {get_ip(request)}")
+ vote_last_response_vgm(
+ [state0, state1], "leftvote", [model_selector0, model_selector1], request
+ )
+ if model_selector0 == "":
+ return ("",) + (disable_btn,) * 4 + (gr.Markdown(f"### Model A: {state0.model_name.split('_')[1]}", visible=True), gr.Markdown(f"### Model B: {state1.model_name.split('_')[1]}", visible=True))
+ else:
+ return ("",) + (disable_btn,) * 4 + (
+ gr.Markdown(state0.model_name, visible=False),
+ gr.Markdown(state1.model_name, visible=False))
+
+
+def rightvote_last_response_vgm(
+ state0, state1, model_selector0, model_selector1, request: gr.Request
+):
+ vgm_logger.info(f"rightvote (named). ip: {get_ip(request)}")
+ vote_last_response_vgm(
+ [state0, state1], "rightvote", [model_selector0, model_selector1], request
+ )
+ if model_selector0 == "":
+ return ("",) + (disable_btn,) * 4 + (
+ gr.Markdown(f"### Model A: {state0.model_name.split('_')[1]}", visible=True),
+ gr.Markdown(f"### Model B: {state1.model_name.split('_')[1]}", visible=True))
+ else:
+ return ("",) + (disable_btn,) * 4 + (
+ gr.Markdown(state0.model_name, visible=False),
+ gr.Markdown(state1.model_name, visible=False))
+
+def tievote_last_response_vgm(
+ state0, state1, model_selector0, model_selector1, request: gr.Request
+):
+ vgm_logger.info(f"tievote (named). ip: {get_ip(request)}")
+ vote_last_response_vgm(
+ [state0, state1], "tievote", [model_selector0, model_selector1], request
+ )
+ if model_selector0 == "":
+ return ("",) + (disable_btn,) * 4 + (
+ gr.Markdown(f"### Model A: {state0.model_name.split('_')[1]}", visible=True),
+ gr.Markdown(f"### Model B: {state1.model_name.split('_')[1]}", visible=True))
+ else:
+ return ("",) + (disable_btn,) * 4 + (
+ gr.Markdown(state0.model_name, visible=False),
+ gr.Markdown(state1.model_name, visible=False))
+
+
+def bothbad_vote_last_response_vgm(
+ state0, state1, model_selector0, model_selector1, request: gr.Request
+):
+ vgm_logger.info(f"bothbad_vote (named). ip: {get_ip(request)}")
+ vote_last_response_vgm(
+ [state0, state1], "bothbad_vote", [model_selector0, model_selector1], request
+ )
+ if model_selector0 == "":
+ return ("",) + (disable_btn,) * 4 + (
+ gr.Markdown(f"### Model A: {state0.model_name.split('_')[1]}", visible=True),
+ gr.Markdown(f"### Model B: {state1.model_name.split('_')[1]}", visible=True))
+ else:
+ return ("",) + (disable_btn,) * 4 + (
+ gr.Markdown(state0.model_name, visible=False),
+ gr.Markdown(state1.model_name, visible=False))
+
+share_js = """
+function (a, b, c, d) {
+ const captureElement = document.querySelector('#share-region-named');
+ html2canvas(captureElement)
+ .then(canvas => {
+ canvas.style.display = 'none'
+ document.body.appendChild(canvas)
+ return canvas
+ })
+ .then(canvas => {
+ const image = canvas.toDataURL('image/png')
+ const a = document.createElement('a')
+ a.setAttribute('download', 'chatbot-arena.png')
+ a.setAttribute('href', image)
+ a.click()
+ canvas.remove()
+ });
+ return [a, b, c, d];
+}
+"""
+def share_click_igm(state0, state1, model_selector0, model_selector1, request: gr.Request):
+ igm_logger.info(f"share (anony). ip: {get_ip(request)}")
+ if state0 is not None and state1 is not None:
+ vote_last_response_igm(
+ [state0, state1], "share", [model_selector0, model_selector1], request
+ )
+
+def share_click_iem(state0, state1, model_selector0, model_selector1, request: gr.Request):
+ iem_logger.info(f"share (anony). ip: {get_ip(request)}")
+ if state0 is not None and state1 is not None:
+ vote_last_response_iem(
+ [state0, state1], "share", [model_selector0, model_selector1], request
+ )
+
+## All Generation Gradio Interface
+
+class ImageStateIG:
+ def __init__(self, model_name):
+ self.conv_id = uuid.uuid4().hex
+ self.model_name = model_name
+ self.prompt = None
+ self.output = None
+
+ def dict(self):
+ base = {
+ "conv_id": self.conv_id,
+ "model_name": self.model_name,
+ "prompt": self.prompt
+ }
+ return base
+
+class ImageStateIE:
+ def __init__(self, model_name):
+ self.conv_id = uuid.uuid4().hex
+ self.model_name = model_name
+ self.source_prompt = None
+ self.target_prompt = None
+ self.instruct_prompt = None
+ self.source_image = None
+ self.output = None
+
+ def dict(self):
+ base = {
+ "conv_id": self.conv_id,
+ "model_name": self.model_name,
+ "source_prompt": self.source_prompt,
+ "target_prompt": self.target_prompt,
+ "instruct_prompt": self.instruct_prompt
+ }
+ return base
+
+class VideoStateVG:
+ def __init__(self, model_name):
+ self.conv_id = uuid.uuid4().hex
+ self.model_name = model_name
+ self.prompt = None
+ self.output = None
+
+ def dict(self):
+ base = {
+ "conv_id": self.conv_id,
+ "model_name": self.model_name,
+ "prompt": self.prompt
+ }
+ return base
+
+
+def generate_ig(gen_func, state, text, model_name, request: gr.Request):
+ if not text:
+ raise gr.Warning("Prompt cannot be empty.")
+ if not model_name:
+ raise gr.Warning("Model name cannot be empty.")
+ if state is None:
+ state = ImageStateIG(model_name)
+ ip = get_ip(request)
+ ig_logger.info(f"generate. ip: {ip}")
+ start_tstamp = time.time()
+ generated_image = gen_func(text, model_name)
+ state.prompt = text
+ state.output = generated_image
+ state.model_name = model_name
+
+ yield state, generated_image
+
+ finish_tstamp = time.time()
+ # logger.info(f"===output===: {output}")
+
+ with open(get_conv_log_filename(), "a") as fout:
+ data = {
+ "tstamp": round(finish_tstamp, 4),
+ "type": "chat",
+ "model": model_name,
+ "gen_params": {},
+ "start": round(start_tstamp, 4),
+ "finish": round(finish_tstamp, 4),
+ "state": state.dict(),
+ "ip": get_ip(request),
+ }
+ fout.write(json.dumps(data) + "\n")
+ append_json_item_on_log_server(data, get_conv_log_filename())
+
+ output_file = f'{IMAGE_DIR}/generation/{state.conv_id}.jpg'
+ os.makedirs(os.path.dirname(output_file), exist_ok=True)
+ with open(output_file, 'w') as f:
+ save_any_image(state.output, f)
+ save_image_file_on_log_server(output_file)
+
+def generate_ig_museum(gen_func, state, model_name, request: gr.Request):
+ if not model_name:
+ raise gr.Warning("Model name cannot be empty.")
+ if state is None:
+ state = ImageStateIG(model_name)
+ ip = get_ip(request)
+ ig_logger.info(f"generate. ip: {ip}")
+ start_tstamp = time.time()
+ generated_image, text = gen_func(model_name)
+ state.prompt = text
+ state.output = generated_image
+ state.model_name = model_name
+
+ yield state, generated_image, text
+
+ finish_tstamp = time.time()
+ # logger.info(f"===output===: {output}")
+
+ with open(get_conv_log_filename(), "a") as fout:
+ data = {
+ "tstamp": round(finish_tstamp, 4),
+ "type": "chat",
+ "model": model_name,
+ "gen_params": {},
+ "start": round(start_tstamp, 4),
+ "finish": round(finish_tstamp, 4),
+ "state": state.dict(),
+ "ip": get_ip(request),
+ }
+ fout.write(json.dumps(data) + "\n")
+ append_json_item_on_log_server(data, get_conv_log_filename())
+
+ output_file = f'{IMAGE_DIR}/generation/{state.conv_id}.jpg'
+ os.makedirs(os.path.dirname(output_file), exist_ok=True)
+ with open(output_file, 'w') as f:
+ save_any_image(state.output, f)
+ save_image_file_on_log_server(output_file)
+
+def generate_igm(gen_func, state0, state1, text, model_name0, model_name1, request: gr.Request):
+ if not text:
+ raise gr.Warning("Prompt cannot be empty.")
+ if not model_name0:
+ raise gr.Warning("Model name A cannot be empty.")
+ if not model_name1:
+ raise gr.Warning("Model name B cannot be empty.")
+ if state0 is None:
+ state0 = ImageStateIG(model_name0)
+ if state1 is None:
+ state1 = ImageStateIG(model_name1)
+ ip = get_ip(request)
+ igm_logger.info(f"generate. ip: {ip}")
+ start_tstamp = time.time()
+ # Remove ### Model (A|B): from model name
+ model_name0 = re.sub(r"### Model A: ", "", model_name0)
+ model_name1 = re.sub(r"### Model B: ", "", model_name1)
+ generated_image0, generated_image1 = gen_func(text, model_name0, model_name1)
+ state0.prompt = text
+ state1.prompt = text
+ state0.output = generated_image0
+ state1.output = generated_image1
+ state0.model_name = model_name0
+ state1.model_name = model_name1
+
+ yield state0, state1, generated_image0, generated_image1
+
+ finish_tstamp = time.time()
+ # logger.info(f"===output===: {output}")
+
+ with open(get_conv_log_filename(), "a") as fout:
+ data = {
+ "tstamp": round(finish_tstamp, 4),
+ "type": "chat",
+ "model": model_name0,
+ "gen_params": {},
+ "start": round(start_tstamp, 4),
+ "finish": round(finish_tstamp, 4),
+ "state": state0.dict(),
+ "ip": get_ip(request),
+ }
+ fout.write(json.dumps(data) + "\n")
+ append_json_item_on_log_server(data, get_conv_log_filename())
+ data = {
+ "tstamp": round(finish_tstamp, 4),
+ "type": "chat",
+ "model": model_name1,
+ "gen_params": {},
+ "start": round(start_tstamp, 4),
+ "finish": round(finish_tstamp, 4),
+ "state": state1.dict(),
+ "ip": get_ip(request),
+ }
+ fout.write(json.dumps(data) + "\n")
+ append_json_item_on_log_server(data, get_conv_log_filename())
+
+ for i, state in enumerate([state0, state1]):
+ output_file = f'{IMAGE_DIR}/generation/{state.conv_id}.jpg'
+ os.makedirs(os.path.dirname(output_file), exist_ok=True)
+ with open(output_file, 'w') as f:
+ save_any_image(state.output, f)
+ save_image_file_on_log_server(output_file)
+
+def generate_igm_museum(gen_func, state0, state1, model_name0, model_name1, request: gr.Request):
+ if not model_name0:
+ raise gr.Warning("Model name A cannot be empty.")
+ if not model_name1:
+ raise gr.Warning("Model name B cannot be empty.")
+ if state0 is None:
+ state0 = ImageStateIG(model_name0)
+ if state1 is None:
+ state1 = ImageStateIG(model_name1)
+ ip = get_ip(request)
+ igm_logger.info(f"generate. ip: {ip}")
+ start_tstamp = time.time()
+ # Remove ### Model (A|B): from model name
+ model_name0 = re.sub(r"### Model A: ", "", model_name0)
+ model_name1 = re.sub(r"### Model B: ", "", model_name1)
+ generated_image0, generated_image1, text = gen_func(model_name0, model_name1)
+ state0.prompt = text
+ state1.prompt = text
+ state0.output = generated_image0
+ state1.output = generated_image1
+ state0.model_name = model_name0
+ state1.model_name = model_name1
+
+ yield state0, state1, generated_image0, generated_image1, text
+
+ finish_tstamp = time.time()
+ # logger.info(f"===output===: {output}")
+
+ with open(get_conv_log_filename(), "a") as fout:
+ data = {
+ "tstamp": round(finish_tstamp, 4),
+ "type": "chat",
+ "model": model_name0,
+ "gen_params": {},
+ "start": round(start_tstamp, 4),
+ "finish": round(finish_tstamp, 4),
+ "state": state0.dict(),
+ "ip": get_ip(request),
+ }
+ fout.write(json.dumps(data) + "\n")
+ append_json_item_on_log_server(data, get_conv_log_filename())
+ data = {
+ "tstamp": round(finish_tstamp, 4),
+ "type": "chat",
+ "model": model_name1,
+ "gen_params": {},
+ "start": round(start_tstamp, 4),
+ "finish": round(finish_tstamp, 4),
+ "state": state1.dict(),
+ "ip": get_ip(request),
+ }
+ fout.write(json.dumps(data) + "\n")
+ append_json_item_on_log_server(data, get_conv_log_filename())
+
+ for i, state in enumerate([state0, state1]):
+ output_file = f'{IMAGE_DIR}/generation/{state.conv_id}.jpg'
+ os.makedirs(os.path.dirname(output_file), exist_ok=True)
+ with open(output_file, 'w') as f:
+ save_any_image(state.output, f)
+ save_image_file_on_log_server(output_file)
+
+
+def generate_igm_annoy(gen_func, state0, state1, state2, state3, text, model_name0, model_name1, model_name2, model_name3, request: gr.Request):
+ if not text:
+ raise gr.Warning("Prompt cannot be empty.")
+ if state0 is None:
+ state0 = ImageStateIG(model_name0)
+ if state1 is None:
+ state1 = ImageStateIG(model_name1)
+ if state2 is None:
+ state2 = ImageStateIG(model_name2)
+ if state3 is None:
+ state3 = ImageStateIG(model_name3)
+
+
+ ip = get_ip(request)
+ igm_logger.info(f"generate. ip: {ip}")
+ start_tstamp = time.time()
+ model_name0 = ""
+ model_name1 = ""
+ model_name2 = ""
+ model_name3 = ""
+
+ generated_image0, generated_image1, generated_image2, generated_image3, model_name0, model_name1, model_name2, model_name3 \
+ = gen_func(text, model_name0, model_name1, model_name2, model_name3)
+ state0.prompt = text
+ state1.prompt = text
+ state2.prompt = text
+ state3.prompt = text
+
+ state0.output = generated_image0
+ state1.output = generated_image1
+ state2.output = generated_image2
+ state3.output = generated_image3
+
+ state0.model_name = model_name0
+ state1.model_name = model_name1
+ state2.model_name = model_name2
+ state3.model_name = model_name3
+
+
+
+ yield state0, state1, state2, state3, generated_image0, generated_image1, generated_image2, generated_image3, \
+ generated_image0, generated_image1, generated_image2, generated_image3, \
+ gr.Markdown(f"### Model A: {model_name0}", visible=False), gr.Markdown(f"### Model B: {model_name1}", visible=False), \
+ gr.Markdown(f"### Model C: {model_name2}", visible=False), gr.Markdown(f"### Model D: {model_name3}", visible=False)
+
+ # finish_tstamp = time.time()
+ # # logger.info(f"===output===: {output}")
+
+ # with open(get_conv_log_filename(), "a") as fout:
+ # data = {
+ # "tstamp": round(finish_tstamp, 4),
+ # "type": "chat",
+ # "model": model_name0,
+ # "gen_params": {},
+ # "start": round(start_tstamp, 4),
+ # "finish": round(finish_tstamp, 4),
+ # "state": state0.dict(),
+ # "ip": get_ip(request),
+ # }
+ # fout.write(json.dumps(data) + "\n")
+ # append_json_item_on_log_server(data, get_conv_log_filename())
+ # data = {
+ # "tstamp": round(finish_tstamp, 4),
+ # "type": "chat",
+ # "model": model_name1,
+ # "gen_params": {},
+ # "start": round(start_tstamp, 4),
+ # "finish": round(finish_tstamp, 4),
+ # "state": state1.dict(),
+ # "ip": get_ip(request),
+ # }
+ # fout.write(json.dumps(data) + "\n")
+ # append_json_item_on_log_server(data, get_conv_log_filename())
+ # data = {
+ # "tstamp": round(finish_tstamp, 4),
+ # "type": "chat",
+ # "model": model_name2,
+ # "gen_params": {},
+ # "start": round(start_tstamp, 4),
+ # "finish": round(finish_tstamp, 4),
+ # "state": state2.dict(),
+ # "ip": get_ip(request),
+ # }
+ # fout.write(json.dumps(data) + "\n")
+ # append_json_item_on_log_server(data, get_conv_log_filename())
+ # data = {
+ # "tstamp": round(finish_tstamp, 4),
+ # "type": "chat",
+ # "model": model_name3,
+ # "gen_params": {},
+ # "start": round(start_tstamp, 4),
+ # "finish": round(finish_tstamp, 4),
+ # "state": state3.dict(),
+ # "ip": get_ip(request),
+ # }
+ # fout.write(json.dumps(data) + "\n")
+ # append_json_item_on_log_server(data, get_conv_log_filename())
+
+ # for i, state in enumerate([state0, state1, state2, state3]):
+ # output_file = f'{IMAGE_DIR}/generation/{state.conv_id}.jpg'
+ # os.makedirs(os.path.dirname(output_file), exist_ok=True)
+ # with open(output_file, 'w') as f:
+ # save_any_image(state.output, f)
+ # save_image_file_on_log_server(output_file)
+
+def generate_igm_annoy_museum(gen_func, state0, state1, state2, state3, model_name0, model_name1, model_name2, model_name3, request: gr.Request):
+ if state0 is None:
+ state0 = ImageStateIG(model_name0)
+ if state1 is None:
+ state1 = ImageStateIG(model_name1)
+ if state2 is None:
+ state2 = ImageStateIG(model_name2)
+ if state3 is None:
+ state3 = ImageStateIG(model_name3)
+
+ ip = get_ip(request)
+ igm_logger.info(f"generate. ip: {ip}")
+ start_tstamp = time.time()
+ model_name0 = re.sub(r"### Model A: ", "", model_name0)
+ model_name1 = re.sub(r"### Model B: ", "", model_name1)
+ model_name2 = re.sub(r"### Model C: ", "", model_name2)
+ model_name3 = re.sub(r"### Model D: ", "", model_name3)
+
+ generated_image0, generated_image1, generated_image2, generated_image3, model_name0, model_name1, model_name2, model_name3, text \
+ = gen_func(model_name0, model_name1, model_name2, model_name3)
+
+ state0.prompt = text
+ state1.prompt = text
+ state2.prompt = text
+ state3.prompt = text
+
+ state0.output = generated_image0
+ state1.output = generated_image1
+ state2.output = generated_image2
+ state3.output = generated_image3
+
+ state0.model_name = model_name0
+ state1.model_name = model_name1
+ state2.model_name = model_name2
+ state3.model_name = model_name3
+
+
+ yield state0, state1, state2, state3, generated_image0, generated_image1, generated_image2, generated_image3, \
+ generated_image0, generated_image1, generated_image2, generated_image3, text, \
+ gr.Markdown(f"### Model A: {model_name0}"), gr.Markdown(f"### Model B: {model_name1}"), \
+ gr.Markdown(f"### Model C: {model_name2}"), gr.Markdown(f"### Model D: {model_name3}")
+
+ finish_tstamp = time.time()
+ # logger.info(f"===output===: {output}")
+
+ # with open(get_conv_log_filename(), "a") as fout:
+ # data = {
+ # "tstamp": round(finish_tstamp, 4),
+ # "type": "chat",
+ # "model": model_name0,
+ # "gen_params": {},
+ # "start": round(start_tstamp, 4),
+ # "finish": round(finish_tstamp, 4),
+ # "state": state0.dict(),
+ # "ip": get_ip(request),
+ # }
+ # fout.write(json.dumps(data) + "\n")
+ # append_json_item_on_log_server(data, get_conv_log_filename())
+ # data = {
+ # "tstamp": round(finish_tstamp, 4),
+ # "type": "chat",
+ # "model": model_name1,
+ # "gen_params": {},
+ # "start": round(start_tstamp, 4),
+ # "finish": round(finish_tstamp, 4),
+ # "state": state1.dict(),
+ # "ip": get_ip(request),
+ # }
+ # fout.write(json.dumps(data) + "\n")
+ # append_json_item_on_log_server(data, get_conv_log_filename())
+ # data = {
+ # "tstamp": round(finish_tstamp, 4),
+ # "type": "chat",
+ # "model": model_name2,
+ # "gen_params": {},
+ # "start": round(start_tstamp, 4),
+ # "finish": round(finish_tstamp, 4),
+ # "state": state2.dict(),
+ # "ip": get_ip(request),
+ # }
+ # fout.write(json.dumps(data) + "\n")
+ # append_json_item_on_log_server(data, get_conv_log_filename())
+ # data = {
+ # "tstamp": round(finish_tstamp, 4),
+ # "type": "chat",
+ # "model": model_name3,
+ # "gen_params": {},
+ # "start": round(start_tstamp, 4),
+ # "finish": round(finish_tstamp, 4),
+ # "state": state3.dict(),
+ # "ip": get_ip(request),
+ # }
+ # fout.write(json.dumps(data) + "\n")
+ # append_json_item_on_log_server(data, get_conv_log_filename())
+
+
+ # for i, state in enumerate([state0, state1, state2, state3]):
+ # output_file = f'{IMAGE_DIR}/generation/{state.conv_id}.jpg'
+ # os.makedirs(os.path.dirname(output_file), exist_ok=True)
+ # with open(output_file, 'w') as f:
+ # save_any_image(state.output, f)
+ # save_image_file_on_log_server(output_file)
+
+def generate_ie(gen_func, state, source_text, target_text, instruct_text, source_image, model_name, request: gr.Request):
+ if not source_text:
+ raise gr.Warning("Source prompt cannot be empty.")
+ if not target_text:
+ raise gr.Warning("Target prompt cannot be empty.")
+ if not instruct_text:
+ raise gr.Warning("Instruction prompt cannot be empty.")
+ if not source_image:
+ raise gr.Warning("Source image cannot be empty.")
+ if not model_name:
+ raise gr.Warning("Model name cannot be empty.")
+ if state is None:
+ state = ImageStateIE(model_name)
+ ip = get_ip(request)
+ ig_logger.info(f"generate. ip: {ip}")
+ start_tstamp = time.time()
+ generated_image = gen_func(source_text, target_text, instruct_text, source_image, model_name)
+ state.source_prompt = source_text
+ state.target_prompt = target_text
+ state.instruct_prompt = instruct_text
+ state.source_image = source_image
+ state.output = generated_image
+ state.model_name = model_name
+
+ yield state, generated_image
+
+ finish_tstamp = time.time()
+ # logger.info(f"===output===: {output}")
+
+ with open(get_conv_log_filename(), "a") as fout:
+ data = {
+ "tstamp": round(finish_tstamp, 4),
+ "type": "chat",
+ "model": model_name,
+ "gen_params": {},
+ "start": round(start_tstamp, 4),
+ "finish": round(finish_tstamp, 4),
+ "state": state.dict(),
+ "ip": get_ip(request),
+ }
+ fout.write(json.dumps(data) + "\n")
+ append_json_item_on_log_server(data, get_conv_log_filename())
+
+ src_img_file = f'{IMAGE_DIR}/edition/{state.conv_id}_src.jpg'
+ os.makedirs(os.path.dirname(src_img_file), exist_ok=True)
+ with open(src_img_file, 'w') as f:
+ save_any_image(state.source_image, f)
+ output_file = f'{IMAGE_DIR}/edition/{state.conv_id}_out.jpg'
+ with open(output_file, 'w') as f:
+ save_any_image(state.output, f)
+ save_image_file_on_log_server(src_img_file)
+ save_image_file_on_log_server(output_file)
+
+def generate_ie_museum(gen_func, state, model_name, request: gr.Request):
+ if not model_name:
+ raise gr.Warning("Model name cannot be empty.")
+ if state is None:
+ state = ImageStateIE(model_name)
+ ip = get_ip(request)
+ ig_logger.info(f"generate. ip: {ip}")
+ start_tstamp = time.time()
+ source_image, generated_image, source_text, target_text, instruct_text = gen_func(model_name)
+ state.source_prompt = source_text
+ state.target_prompt = target_text
+ state.instruct_prompt = instruct_text
+ state.source_image = source_image
+ state.output = generated_image
+ state.model_name = model_name
+
+ yield state, generated_image, source_image, source_text, target_text, instruct_text
+
+ finish_tstamp = time.time()
+ # logger.info(f"===output===: {output}")
+
+ with open(get_conv_log_filename(), "a") as fout:
+ data = {
+ "tstamp": round(finish_tstamp, 4),
+ "type": "chat",
+ "model": model_name,
+ "gen_params": {},
+ "start": round(start_tstamp, 4),
+ "finish": round(finish_tstamp, 4),
+ "state": state.dict(),
+ "ip": get_ip(request),
+ }
+ fout.write(json.dumps(data) + "\n")
+ append_json_item_on_log_server(data, get_conv_log_filename())
+
+ src_img_file = f'{IMAGE_DIR}/edition/{state.conv_id}_src.jpg'
+ os.makedirs(os.path.dirname(src_img_file), exist_ok=True)
+ with open(src_img_file, 'w') as f:
+ save_any_image(state.source_image, f)
+ output_file = f'{IMAGE_DIR}/edition/{state.conv_id}_out.jpg'
+ with open(output_file, 'w') as f:
+ save_any_image(state.output, f)
+ save_image_file_on_log_server(src_img_file)
+ save_image_file_on_log_server(output_file)
+
+
+def generate_iem(gen_func, state0, state1, source_text, target_text, instruct_text, source_image, model_name0, model_name1, request: gr.Request):
+ if not source_text:
+ raise gr.Warning("Source prompt cannot be empty.")
+ if not target_text:
+ raise gr.Warning("Target prompt cannot be empty.")
+ if not instruct_text:
+ raise gr.Warning("Instruction prompt cannot be empty.")
+ if not source_image:
+ raise gr.Warning("Source image cannot be empty.")
+ if not model_name0:
+ raise gr.Warning("Model name A cannot be empty.")
+ if not model_name1:
+ raise gr.Warning("Model name B cannot be empty.")
+ if state0 is None:
+ state0 = ImageStateIE(model_name0)
+ if state1 is None:
+ state1 = ImageStateIE(model_name1)
+ ip = get_ip(request)
+ igm_logger.info(f"generate. ip: {ip}")
+ start_tstamp = time.time()
+ model_name0 = re.sub(r"### Model A: ", "", model_name0)
+ model_name1 = re.sub(r"### Model B: ", "", model_name1)
+ generated_image0, generated_image1 = gen_func(source_text, target_text, instruct_text, source_image, model_name0, model_name1)
+ state0.source_prompt = source_text
+ state0.target_prompt = target_text
+ state0.instruct_prompt = instruct_text
+ state0.source_image = source_image
+ state0.output = generated_image0
+ state0.model_name = model_name0
+ state1.source_prompt = source_text
+ state1.target_prompt = target_text
+ state1.instruct_prompt = instruct_text
+ state1.source_image = source_image
+ state1.output = generated_image1
+ state1.model_name = model_name1
+
+ yield state0, state1, generated_image0, generated_image1
+
+ finish_tstamp = time.time()
+ # logger.info(f"===output===: {output}")
+
+ with open(get_conv_log_filename(), "a") as fout:
+ data = {
+ "tstamp": round(finish_tstamp, 4),
+ "type": "chat",
+ "model": model_name0,
+ "gen_params": {},
+ "start": round(start_tstamp, 4),
+ "finish": round(finish_tstamp, 4),
+ "state": state0.dict(),
+ "ip": get_ip(request),
+ }
+ fout.write(json.dumps(data) + "\n")
+ append_json_item_on_log_server(data, get_conv_log_filename())
+ data = {
+ "tstamp": round(finish_tstamp, 4),
+ "type": "chat",
+ "model": model_name1,
+ "gen_params": {},
+ "start": round(start_tstamp, 4),
+ "finish": round(finish_tstamp, 4),
+ "state": state1.dict(),
+ "ip": get_ip(request),
+ }
+ fout.write(json.dumps(data) + "\n")
+ append_json_item_on_log_server(data, get_conv_log_filename())
+
+ for i, state in enumerate([state0, state1]):
+ src_img_file = f'{IMAGE_DIR}/edition/{state.conv_id}_src.jpg'
+ os.makedirs(os.path.dirname(src_img_file), exist_ok=True)
+ with open(src_img_file, 'w') as f:
+ save_any_image(state.source_image, f)
+ output_file = f'{IMAGE_DIR}/edition/{state.conv_id}_out.jpg'
+ with open(output_file, 'w') as f:
+ save_any_image(state.output, f)
+ save_image_file_on_log_server(src_img_file)
+ save_image_file_on_log_server(output_file)
+
+def generate_iem_museum(gen_func, state0, state1, model_name0, model_name1, request: gr.Request):
+ if not model_name0:
+ raise gr.Warning("Model name A cannot be empty.")
+ if not model_name1:
+ raise gr.Warning("Model name B cannot be empty.")
+ if state0 is None:
+ state0 = ImageStateIE(model_name0)
+ if state1 is None:
+ state1 = ImageStateIE(model_name1)
+ ip = get_ip(request)
+ igm_logger.info(f"generate. ip: {ip}")
+ start_tstamp = time.time()
+ model_name0 = re.sub(r"### Model A: ", "", model_name0)
+ model_name1 = re.sub(r"### Model B: ", "", model_name1)
+ source_image, generated_image0, generated_image1, source_text, target_text, instruct_text = gen_func(model_name0, model_name1)
+ state0.source_prompt = source_text
+ state0.target_prompt = target_text
+ state0.instruct_prompt = instruct_text
+ state0.source_image = source_image
+ state0.output = generated_image0
+ state0.model_name = model_name0
+ state1.source_prompt = source_text
+ state1.target_prompt = target_text
+ state1.instruct_prompt = instruct_text
+ state1.source_image = source_image
+ state1.output = generated_image1
+ state1.model_name = model_name1
+
+ yield state0, state1, generated_image0, generated_image1, source_image, source_text, target_text, instruct_text
+
+ finish_tstamp = time.time()
+ # logger.info(f"===output===: {output}")
+
+ with open(get_conv_log_filename(), "a") as fout:
+ data = {
+ "tstamp": round(finish_tstamp, 4),
+ "type": "chat",
+ "model": model_name0,
+ "gen_params": {},
+ "start": round(start_tstamp, 4),
+ "finish": round(finish_tstamp, 4),
+ "state": state0.dict(),
+ "ip": get_ip(request),
+ }
+ fout.write(json.dumps(data) + "\n")
+ append_json_item_on_log_server(data, get_conv_log_filename())
+ data = {
+ "tstamp": round(finish_tstamp, 4),
+ "type": "chat",
+ "model": model_name1,
+ "gen_params": {},
+ "start": round(start_tstamp, 4),
+ "finish": round(finish_tstamp, 4),
+ "state": state1.dict(),
+ "ip": get_ip(request),
+ }
+ fout.write(json.dumps(data) + "\n")
+ append_json_item_on_log_server(data, get_conv_log_filename())
+
+ for i, state in enumerate([state0, state1]):
+ src_img_file = f'{IMAGE_DIR}/edition/{state.conv_id}_src.jpg'
+ os.makedirs(os.path.dirname(src_img_file), exist_ok=True)
+ with open(src_img_file, 'w') as f:
+ save_any_image(state.source_image, f)
+ output_file = f'{IMAGE_DIR}/edition/{state.conv_id}_out.jpg'
+ with open(output_file, 'w') as f:
+ save_any_image(state.output, f)
+ save_image_file_on_log_server(src_img_file)
+ save_image_file_on_log_server(output_file)
+
+
+def generate_iem_annoy(gen_func, state0, state1, source_text, target_text, instruct_text, source_image, model_name0, model_name1, request: gr.Request):
+ if not source_text:
+ raise gr.Warning("Source prompt cannot be empty.")
+ if not target_text:
+ raise gr.Warning("Target prompt cannot be empty.")
+ if not instruct_text:
+ raise gr.Warning("Instruction prompt cannot be empty.")
+ if not source_image:
+ raise gr.Warning("Source image cannot be empty.")
+ if state0 is None:
+ state0 = ImageStateIE(model_name0)
+ if state1 is None:
+ state1 = ImageStateIE(model_name1)
+ ip = get_ip(request)
+ igm_logger.info(f"generate. ip: {ip}")
+ start_tstamp = time.time()
+ model_name0 = ""
+ model_name1 = ""
+ generated_image0, generated_image1, model_name0, model_name1 = gen_func(source_text, target_text, instruct_text, source_image, model_name0, model_name1)
+ state0.source_prompt = source_text
+ state0.target_prompt = target_text
+ state0.instruct_prompt = instruct_text
+ state0.source_image = source_image
+ state0.output = generated_image0
+ state0.model_name = model_name0
+ state1.source_prompt = source_text
+ state1.target_prompt = target_text
+ state1.instruct_prompt = instruct_text
+ state1.source_image = source_image
+ state1.output = generated_image1
+ state1.model_name = model_name1
+
+ yield state0, state1, generated_image0, generated_image1, \
+ gr.Markdown(f"### Model A: {model_name0}", visible=False), gr.Markdown(f"### Model B: {model_name1}", visible=False)
+
+ finish_tstamp = time.time()
+ # logger.info(f"===output===: {output}")
+
+ with open(get_conv_log_filename(), "a") as fout:
+ data = {
+ "tstamp": round(finish_tstamp, 4),
+ "type": "chat",
+ "model": model_name0,
+ "gen_params": {},
+ "start": round(start_tstamp, 4),
+ "finish": round(finish_tstamp, 4),
+ "state": state0.dict(),
+ "ip": get_ip(request),
+ }
+ fout.write(json.dumps(data) + "\n")
+ append_json_item_on_log_server(data, get_conv_log_filename())
+ data = {
+ "tstamp": round(finish_tstamp, 4),
+ "type": "chat",
+ "model": model_name1,
+ "gen_params": {},
+ "start": round(start_tstamp, 4),
+ "finish": round(finish_tstamp, 4),
+ "state": state1.dict(),
+ "ip": get_ip(request),
+ }
+ fout.write(json.dumps(data) + "\n")
+ append_json_item_on_log_server(data, get_conv_log_filename())
+
+ for i, state in enumerate([state0, state1]):
+ src_img_file = f'{IMAGE_DIR}/edition/{state.conv_id}_src.jpg'
+ os.makedirs(os.path.dirname(src_img_file), exist_ok=True)
+ with open(src_img_file, 'w') as f:
+ save_any_image(state.source_image, f)
+ output_file = f'{IMAGE_DIR}/edition/{state.conv_id}_out.jpg'
+ with open(output_file, 'w') as f:
+ save_any_image(state.output, f)
+ save_image_file_on_log_server(src_img_file)
+ save_image_file_on_log_server(output_file)
+
+def generate_iem_annoy_museum(gen_func, state0, state1, model_name0, model_name1, request: gr.Request):
+ if state0 is None:
+ state0 = ImageStateIE(model_name0)
+ if state1 is None:
+ state1 = ImageStateIE(model_name1)
+ ip = get_ip(request)
+ igm_logger.info(f"generate. ip: {ip}")
+ start_tstamp = time.time()
+ model_name0 = ""
+ model_name1 = ""
+ source_image, generated_image0, generated_image1, source_text, target_text, instruct_text, model_name0, model_name1 = gen_func(model_name0, model_name1)
+ state0.source_prompt = source_text
+ state0.target_prompt = target_text
+ state0.instruct_prompt = instruct_text
+ state0.source_image = source_image
+ state0.output = generated_image0
+ state0.model_name = model_name0
+ state1.source_prompt = source_text
+ state1.target_prompt = target_text
+ state1.instruct_prompt = instruct_text
+ state1.source_image = source_image
+ state1.output = generated_image1
+ state1.model_name = model_name1
+
+ yield state0, state1, generated_image0, generated_image1, source_image, source_text, target_text, instruct_text, \
+ gr.Markdown(f"### Model A: {model_name0}", visible=False), gr.Markdown(f"### Model B: {model_name1}", visible=False)
+
+ finish_tstamp = time.time()
+ # logger.info(f"===output===: {output}")
+
+ with open(get_conv_log_filename(), "a") as fout:
+ data = {
+ "tstamp": round(finish_tstamp, 4),
+ "type": "chat",
+ "model": model_name0,
+ "gen_params": {},
+ "start": round(start_tstamp, 4),
+ "finish": round(finish_tstamp, 4),
+ "state": state0.dict(),
+ "ip": get_ip(request),
+ }
+ fout.write(json.dumps(data) + "\n")
+ append_json_item_on_log_server(data, get_conv_log_filename())
+ data = {
+ "tstamp": round(finish_tstamp, 4),
+ "type": "chat",
+ "model": model_name1,
+ "gen_params": {},
+ "start": round(start_tstamp, 4),
+ "finish": round(finish_tstamp, 4),
+ "state": state1.dict(),
+ "ip": get_ip(request),
+ }
+ fout.write(json.dumps(data) + "\n")
+ append_json_item_on_log_server(data, get_conv_log_filename())
+
+ for i, state in enumerate([state0, state1]):
+ src_img_file = f'{IMAGE_DIR}/edition/{state.conv_id}_src.jpg'
+ os.makedirs(os.path.dirname(src_img_file), exist_ok=True)
+ with open(src_img_file, 'w') as f:
+ save_any_image(state.source_image, f)
+ output_file = f'{IMAGE_DIR}/edition/{state.conv_id}_out.jpg'
+ with open(output_file, 'w') as f:
+ save_any_image(state.output, f)
+ save_image_file_on_log_server(src_img_file)
+ save_image_file_on_log_server(output_file)
+
+def generate_vg(gen_func, state, text, model_name, request: gr.Request):
+ if not text:
+ raise gr.Warning("Prompt cannot be empty.")
+ if not model_name:
+ raise gr.Warning("Model name cannot be empty.")
+ if state is None:
+ state = VideoStateVG(model_name)
+ ip = get_ip(request)
+ vg_logger.info(f"generate. ip: {ip}")
+ start_tstamp = time.time()
+ generated_video = gen_func(text, model_name)
+ state.prompt = text
+ state.output = generated_video
+ state.model_name = model_name
+
+ # yield state, generated_video
+
+ finish_tstamp = time.time()
+
+ with open(get_conv_log_filename(), "a") as fout:
+ data = {
+ "tstamp": round(finish_tstamp, 4),
+ "type": "chat",
+ "model": model_name,
+ "gen_params": {},
+ "start": round(start_tstamp, 4),
+ "finish": round(finish_tstamp, 4),
+ "state": state.dict(),
+ "ip": get_ip(request),
+ }
+ fout.write(json.dumps(data) + "\n")
+ append_json_item_on_log_server(data, get_conv_log_filename())
+
+ output_file = f'{VIDEO_DIR}/generation/{state.conv_id}.mp4'
+ os.makedirs(os.path.dirname(output_file), exist_ok=True)
+ if model_name.startswith('fal'):
+ r = requests.get(state.output)
+ with open(output_file, 'wb') as outfile:
+ outfile.write(r.content)
+ else:
+ print("======== video shape: ========")
+ print(state.output.shape)
+ imageio.mimwrite(output_file, state.output, fps=8, quality=9)
+
+ save_video_file_on_log_server(output_file)
+ yield state, output_file
+
+def generate_vgm(gen_func, state0, state1, text, model_name0, model_name1, request: gr.Request):
+ if not text:
+ raise gr.Warning("Prompt cannot be empty.")
+ if not model_name0:
+ raise gr.Warning("Model name A cannot be empty.")
+ if not model_name1:
+ raise gr.Warning("Model name B cannot be empty.")
+ if state0 is None:
+ state0 = VideoStateVG(model_name0)
+ if state1 is None:
+ state1 = VideoStateVG(model_name1)
+ ip = get_ip(request)
+ igm_logger.info(f"generate. ip: {ip}")
+ start_tstamp = time.time()
+ # Remove ### Model (A|B): from model name
+ model_name0 = re.sub(r"### Model A: ", "", model_name0)
+ model_name1 = re.sub(r"### Model B: ", "", model_name1)
+ generated_video0, generated_video1 = gen_func(text, model_name0, model_name1)
+ state0.prompt = text
+ state1.prompt = text
+ state0.output = generated_video0
+ state1.output = generated_video1
+ state0.model_name = model_name0
+ state1.model_name = model_name1
+
+ # yield state0, state1, generated_video0, generated_video1
+ print("====== model name =========")
+ print(state0.model_name)
+ print(state1.model_name)
+
+
+ finish_tstamp = time.time()
+
+
+ with open(get_conv_log_filename(), "a") as fout:
+ data = {
+ "tstamp": round(finish_tstamp, 4),
+ "type": "chat",
+ "model": model_name0,
+ "gen_params": {},
+ "start": round(start_tstamp, 4),
+ "finish": round(finish_tstamp, 4),
+ "state": state0.dict(),
+ "ip": get_ip(request),
+ }
+ fout.write(json.dumps(data) + "\n")
+ append_json_item_on_log_server(data, get_conv_log_filename())
+ data = {
+ "tstamp": round(finish_tstamp, 4),
+ "type": "chat",
+ "model": model_name1,
+ "gen_params": {},
+ "start": round(start_tstamp, 4),
+ "finish": round(finish_tstamp, 4),
+ "state": state1.dict(),
+ "ip": get_ip(request),
+ }
+ fout.write(json.dumps(data) + "\n")
+ append_json_item_on_log_server(data, get_conv_log_filename())
+
+ for i, state in enumerate([state0, state1]):
+ output_file = f'{VIDEO_DIR}/generation/{state.conv_id}.mp4'
+ os.makedirs(os.path.dirname(output_file), exist_ok=True)
+ print(state.model_name)
+
+ if state.model_name.startswith('fal'):
+ r = requests.get(state.output)
+ with open(output_file, 'wb') as outfile:
+ outfile.write(r.content)
+ else:
+ print("======== video shape: ========")
+ print(state.output)
+ print(state.output.shape)
+ imageio.mimwrite(output_file, state.output, fps=8, quality=9)
+ save_video_file_on_log_server(output_file)
+ yield state0, state1, f'{VIDEO_DIR}/generation/{state0.conv_id}.mp4', f'{VIDEO_DIR}/generation/{state1.conv_id}.mp4'
+
+
+def generate_vgm_annoy(gen_func, state0, state1, text, model_name0, model_name1, request: gr.Request):
+ if not text:
+ raise gr.Warning("Prompt cannot be empty.")
+ if state0 is None:
+ state0 = VideoStateVG(model_name0)
+ if state1 is None:
+ state1 = VideoStateVG(model_name1)
+ ip = get_ip(request)
+ vgm_logger.info(f"generate. ip: {ip}")
+ start_tstamp = time.time()
+ model_name0 = ""
+ model_name1 = ""
+ generated_video0, generated_video1, model_name0, model_name1 = gen_func(text, model_name0, model_name1)
+ state0.prompt = text
+ state1.prompt = text
+ state0.output = generated_video0
+ state1.output = generated_video1
+ state0.model_name = model_name0
+ state1.model_name = model_name1
+
+ # yield state0, state1, generated_video0, generated_video1, \
+ # gr.Markdown(f"### Model A: {model_name0}"), gr.Markdown(f"### Model B: {model_name1}")
+
+ finish_tstamp = time.time()
+ # logger.info(f"===output===: {output}")
+
+ with open(get_conv_log_filename(), "a") as fout:
+ data = {
+ "tstamp": round(finish_tstamp, 4),
+ "type": "chat",
+ "model": model_name0,
+ "gen_params": {},
+ "start": round(start_tstamp, 4),
+ "finish": round(finish_tstamp, 4),
+ "state": state0.dict(),
+ "ip": get_ip(request),
+ }
+ fout.write(json.dumps(data) + "\n")
+ append_json_item_on_log_server(data, get_conv_log_filename())
+ data = {
+ "tstamp": round(finish_tstamp, 4),
+ "type": "chat",
+ "model": model_name1,
+ "gen_params": {},
+ "start": round(start_tstamp, 4),
+ "finish": round(finish_tstamp, 4),
+ "state": state1.dict(),
+ "ip": get_ip(request),
+ }
+ fout.write(json.dumps(data) + "\n")
+ append_json_item_on_log_server(data, get_conv_log_filename())
+
+ for i, state in enumerate([state0, state1]):
+ output_file = f'{VIDEO_DIR}/generation/{state.conv_id}.mp4'
+ os.makedirs(os.path.dirname(output_file), exist_ok=True)
+ if state.model_name.startswith('fal'):
+ r = requests.get(state.output)
+ with open(output_file, 'wb') as outfile:
+ outfile.write(r.content)
+ else:
+ print("======== video shape: ========")
+ print(state.output.shape)
+ imageio.mimwrite(output_file, state.output, fps=8, quality=9)
+ save_video_file_on_log_server(output_file)
+
+ yield state0, state1, f'{VIDEO_DIR}/generation/{state0.conv_id}.mp4', f'{VIDEO_DIR}/generation/{state1.conv_id}.mp4', \
+ gr.Markdown(f"### Model A: {model_name0}", visible=False), gr.Markdown(f"### Model B: {model_name1}", visible=False)
\ No newline at end of file