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import argparse
import ast
import pickle
import os
import threading
import time
import gradio as gr
import numpy as np
import pandas as pd
from serve.model import model_config
def amend_model_name(config):
if config.page_link:
return '[' + config.model_name + '](' + config.page_link + ')'
elif config.code_link:
return '[' + config.model_name + '](' + config.page_link + ')'
else:
return config.model_name
def amend_rank(rank):
if rank==1:
return "🥇"
elif rank==2:
return "🥈"
elif rank==3:
return '🥉'
else:
return rank
def get_leaderboard_values(leaderboard_df):
leaderboard_vals = []
for i, row in leaderboard_df.iterrows():
rank = i+1
model_name = row["Method"]
task = row["Task"]
if model_name not in model_config.keys() or model_config[model_name].task != task:
continue
cfg = model_config[model_name]
values = [amend_rank(rank), amend_model_name(cfg), task]
values = values + [row.get(dim, np.NaN) for dim in leaderboard_df.columns[2:]]
# values.append(round(np.sum([v for v in values[3:] if pd.notna(v)]), 4))
values.append(cfg.organization if cfg.organization else "N/A")
leaderboard_vals.append(values)
return leaderboard_vals
def get_topk_ranks(df, k=3):
ranks = {}
for col_idx, col in enumerate(df.columns[2:]): # skip "Model" “Task"
topk = df[col].nlargest(k)
for rank, idx in enumerate(topk.index):
if idx not in ranks:
ranks[idx] = {}
ranks[idx][col_idx] = rank + 1 # 1-based rank
for i in range(k): ranks[i][5] = i + 1
return ranks # dict: row -> {col: rank}
def build_leaderboard_tab(leaderboard_file: str, task: str = ""):
if not isinstance(leaderboard_file, str):
leaderboard_file = leaderboard_file.value
if not isinstance(task, str):
task = task.value
df = pd.read_csv(leaderboard_file)
if task in ["Text-to-3D only", "Image-to-3D only"]:
df = df[df["Task"] == task.split()[0]]
# df = df.drop(df[df["Task"]!=task.split()[0]].index)
leaderboard_df = df.drop(df[df["Method"].isnull()].index)
leaderboard_df = leaderboard_df.reset_index(drop=True)
leaderboard_df = leaderboard_df.sort_values(by="Overall", ascending=False).reset_index(drop=True)
leaderboard_vals = get_leaderboard_values(leaderboard_df)
leaderboard = gr.Dataframe(
headers = ['Rank', "🤖 Model", "🪧 Task" ]
+ [f"{dim}" for dim in leaderboard_df.keys()[2:-1]]
+ ["⭐ Overall", "🏛️ Orgnization"], # "🔗 Links",
datatype = ["number", "markdown", "markdown"]
+ ["number"] * (len(leaderboard_df.columns) - 3)
+ ["number", "markdown"],
value = leaderboard_vals,
height = 680,
column_widths = [60, 120, 100]
+ [120] * (len(leaderboard_df.columns) - 3)
+ [120, 180],
wrap = True,
type="pandas",
)
return leaderboard |