Basic Gradio setup

CNAME

@@ -1 +0,0 @@
-languagebench.bmz.dev

README.md

@@ -3,13 +3,26 @@ title: AI Language Monitor
 emoji: 🌍
 colorFrom: purple
 colorTo: pink
-sdk: static
-pinned: false
+sdk: gradio
 license: mit
 short_description: Evaluating LLM performance across all human languages.
+tags:
+- leaderboard
+- submission:manual
+- test:public
+- judge:auto
+- modality:text
+- modality:artefacts
+- eval:generation
+- language:English
+- language:German
 ---
 
-<!-- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference -->
+<!--
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference 
+For tag meaning, see https://huggingface.co/spaces/leaderboards/LeaderboardsExplorer
+-->
+
 
 # AI Language Monitor 🌍
 

app.py

@@ -0,0 +1,76 @@
+import gradio as gr
+import json
+import pandas as pd
+import plotly.graph_objects as go
+
+# Load and process results
+with open("results.json") as f:
+    results = json.load(f)
+
+def create_model_comparison_plot(results):
+    # Extract all unique models
+    models = set()
+    for lang in results:
+        for score in lang["scores"]:
+            models.add(score["model"])
+    models = list(models)
+    
+    # Create traces for each model
+    traces = []
+    for model in models:
+        x_vals = []  # languages
+        y_vals = []  # BLEU scores
+        
+        for lang in results:
+            model_score = next((s["bleu"] for s in lang["scores"] if s["model"] == model), None)
+            if model_score is not None:
+                x_vals.append(lang["language_name"])
+                y_vals.append(model_score)
+        
+        traces.append(go.Bar(
+            name=model.split('/')[-1],
+            x=x_vals,
+            y=y_vals,
+        ))
+    
+    fig = go.Figure(data=traces)
+    fig.update_layout(
+        title="BLEU Scores by Model and Language",
+        xaxis_title="Language",
+        yaxis_title="BLEU Score",
+        barmode='group',
+        height=500
+    )
+    return fig
+
+def create_results_df(results):
+    # Create a list to store flattened data
+    flat_data = []
+    
+    for lang in results:
+        row = {
+            "Language": lang["language_name"],
+            "Speakers (M)": round(lang["speakers"] / 1_000_000, 1),
+            "Average BLEU": round(lang["bleu"], 3),
+        }
+        # Add individual model scores
+        for score in lang["scores"]:
+            model_name = score["model"].split('/')[-1]
+            row[f"{model_name} BLEU"] = round(score["bleu"], 3)
+        
+        flat_data.append(row)
+    
+    return pd.DataFrame(flat_data)
+
+# Create the visualization components
+with gr.Blocks(title="AI Language Translation Benchmark") as demo:
+    gr.Markdown("# AI Language Translation Benchmark")
+    gr.Markdown("Comparing translation performance across different AI models and languages")
+    
+    df = create_results_df(results)
+    plot = create_model_comparison_plot(results)
+    
+    gr.DataFrame(value=df, label="Translation Results")
+    gr.Plot(value=plot, label="Model Comparison")
+
+demo.launch()

src/data/languagebench.json.py → evals.py

@@ -1,9 +1,7 @@
 import asyncio
 import json
 import os
-import sys
 from os import getenv
-from pathlib import Path
 
 import evaluate
 import pandas as pd
@@ -21,7 +19,7 @@ models = [
     "anthropic/claude-3.5-haiku",
     # "meta-llama/llama-3.1-405b-instruct",  # lots of slow repetitions for LRLs
     # "mistralai/mistral-large",
-    # "google/gemini-flash-1.5",  # very fast
+    "google/gemini-flash-1.5",  # very fast
     # "qwen/qwen-2.5-72b-instruct",  # somewhat slow
 ]
 fast_model = "anthropic/claude-3.5-haiku"
@@ -46,8 +44,7 @@ def reorder(language_name):
     return language_name
 
 # load benchmark languages and scripts
-data = Path("src/data")
-benchmark_dir = data / "floresp-v2.0-rc.3/dev"
+benchmark_dir = "data/floresp-v2.0-rc.3/dev"
 benchmark_languages = pd.DataFrame(
     [f.split(".")[1].split("_", 1) for f in os.listdir(benchmark_dir)],
     columns=["language_code", "script_code"],
@@ -58,7 +55,7 @@ benchmark_languages["in_benchmark"] = True
 
 # load Ethnologue language names
 language_names = (
-    pd.read_csv(data / "LanguageCodes.tab", sep="\t")
+    pd.read_csv("data/LanguageCodes.tab", sep="\t")
     .rename(columns={"LangID": "language_code", "Name": "language_name"})[
         ["language_code", "language_name"]
     ]
@@ -67,7 +64,7 @@ language_names = (
 
 # load Wikidata speaker stats
 language_stats = (
-    pd.read_csv(data / "languages.tsv", sep="\t")
+    pd.read_csv("data/languages.tsv", sep="\t")
     .rename(columns={"iso639_3": "language_code", "maxSpeakers": "speakers"})[
         ["language_code", "speakers"]
     ]
@@ -86,7 +83,7 @@ language_stats = language_stats[
 ]
 
 # load unicode script names
-script_names = pd.read_csv(data / "ScriptCodes.csv").rename(
+script_names = pd.read_csv("data/ScriptCodes.csv").rename(
     columns={"Code": "script_code", "English Name": "script_name"}
 )[["script_code", "script_name"]]
 
@@ -96,15 +93,15 @@ languages = pd.merge(benchmark_languages, languages, on="language_code", how="ou
 languages = pd.merge(languages, script_names, on="script_code", how="left")
 languages["in_benchmark"] = languages["in_benchmark"].fillna(False)
 languages = languages.sort_values(by="speakers", ascending=False)
+languages = languages.iloc[:5]
 
-# sample languages to translate from
-# when translating e.g. to Mandarin, we drop Mandarin from the sample and use the next samples from the list instead; therefore we need to sample more than n_sentences
-original_languages = languages[languages["in_benchmark"]].sample(
-    n=n_sentences * 2, weights="speakers", replace=True, random_state=42
+# sample languages to translate to
+target_languages_NEW = languages[languages["in_benchmark"]].sample(
+    n=n_sentences, weights="speakers", replace=True, random_state=42
 )
 # sample languages to analyze with all models
-detailed_target_languages = languages[languages["in_benchmark"]].sample(
-    n=3, random_state=42
+detailed_languages = languages[languages["in_benchmark"]].sample(
+    n=2, random_state=42
 )
 
 
@@ -162,42 +159,39 @@ def load_sentences(language):
 # evaluation!
 async def main():
     results = []
-    for language in list(languages.itertuples())[:5]:
+    for language in list(languages.itertuples()):
         name = (
             language.language_name
             if not pd.isna(language.language_name)
             else language.language_code
         )
-        print(name, file=sys.stderr)
+        print(name)
         scores = []
         if language.in_benchmark:
-            target_sentences = load_sentences(language)[:n_sentences]
+            original_sentences_NEW = load_sentences(language)[:n_sentences]
             for model in models:
                 if (
                     model != fast_model
                     and language.language_code
-                    not in detailed_target_languages.language_code.values
+                    not in detailed_languages.language_code.values
                 ):
                     continue
-                # drop the target language from the original languages sample
-                _original_languages = original_languages[
-                    original_languages.language_code != language.language_code
-                ].iloc[:n_sentences]
-                original_sentences = [
-                    load_sentences(lang)[i]
-                    for i, lang in enumerate(_original_languages.itertuples())
-                ]
-                print(model, file=sys.stderr)
+                
+                print(model)
                 predictions = [
                     translate(
                         model, language.language_name, language.script_name, sentence
                     )
-                    for sentence in original_sentences
+                    for sentence, language in zip(original_sentences_NEW, target_languages_NEW.itertuples())
                 ]
                 predictions = await tqdm_asyncio.gather(*predictions, miniters=1)
+                target_sentences_NEW = [
+                    load_sentences(lang)[i]
+                    for i, lang in enumerate(target_languages_NEW.itertuples())
+                ]
                 metrics_bleu = bleu.compute(
                     predictions=predictions,
-                    references=target_sentences,
+                    references=target_sentences_NEW,
                     tokenizer=tokenizer.tokenize,
                 )
                 # metrics_bert = bertscore.compute(
@@ -222,7 +216,8 @@ async def main():
                 # "bert_score": mean([s["bert_score"] for s in scores]),
             }
         )
-    print(json.dumps(results, indent=2, ensure_ascii=False))
+    with open("results.json", "w") as f:
+        json.dump(results, f, indent=2, ensure_ascii=False)
 
 
 if __name__ == "__main__":

observablehq.config.js

@@ -1,34 +0,0 @@
-// See https://observablehq.com/framework/config for documentation.
-export default {
-  // The app’s title; used in the sidebar and webpage titles.
-  title: "AI Language Monitor",
-
-  // The pages and sections in the sidebar. If you don’t specify this option,
-  // all pages will be listed in alphabetical order. Listing pages explicitly
-  // lets you organize them into sections and have unlisted pages.
-  pages: [
-    { name: "Compare Languages", path: "/compare-languages" },
-    { name: "Compare AI Models", path: "/compare-ai-models" },
-    { name: "Methodology", path: "/methodology" },
-  ],
-
-  // Content to add to the head of the page, e.g. for a favicon:
-  head: '<link rel="icon" href="data:image/svg+xml,<svg xmlns=%22http://www.w3.org/2000/svg%22 viewBox=%220 0 100 100%22><text y=%22.9em%22 font-size=%2290%22 fill=%22black%22>🌍</text></svg>">',
-
-  // The path to the source root.
-  root: "src",
-
-  // Some additional configuration options and their defaults:
-  // theme: "default", // try "light", "dark", "slate", etc.
-  // header: "", // what to show in the header (HTML)
-  // footer: "Built with Observable.", // what to show in the footer (HTML)
-  // sidebar: true, // whether to show the sidebar
-  // toc: true, // whether to show the table of contents
-  // pager: true, // whether to show previous & next links in the footer
-  // output: "dist", // path to the output root for build
-  // search: true, // activate search
-  // linkify: true, // convert URLs in Markdown to links
-  // typographer: false, // smart quotes and other typographic improvements
-  // preserveExtension: false, // drop .html from URLs
-  // preserveIndex: false, // drop /index from URLs
-};

package.json

@@ -1,22 +0,0 @@
-{
-  "type": "module",
-  "private": true,
-  "scripts": {
-    "clean": "rimraf src/.observablehq/cache",
-    "build": "observable build",
-    "dev": "observable preview",
-    "deploy": "observable deploy",
-    "observable": "observable"
-  },
-  "dependencies": {
-    "@observablehq/framework": "^1.13.2",
-    "d3-dsv": "^3.0.1",
-    "d3-time-format": "^4.1.0"
-  },
-  "devDependencies": {
-    "rimraf": "^5.0.5"
-  },
-  "engines": {
-    "node": ">=18"
-  }
-}

pyproject.toml

@@ -5,13 +5,18 @@ description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.10"
 dependencies = [
+    "gradio>=5.16.2",
+    "pandas>=2.2.3",
+    "plotly>=6.0.0",
+]
+
+[tool.uv]
+dev-dependencies = [
     "aiolimiter>=1.1.0",
     "bert-score>=0.3.13",
-    "evaluate>=0.4.3",
+    "evaluate==0.4.0",
     "joblib>=1.4.2",
-    "nltk>=3.9.1",
     "openai>=1.52.2",
-    "pandas>=2.2.3",
     "protobuf>=5.28.3",
     "python-dotenv>=1.0.1",
     "sacrebleu>=2.4.3",

requirements.txt

@@ -0,0 +1,156 @@
+# This file was autogenerated by uv via the following command:
+#    uv pip compile pyproject.toml -o requirements.txt
+aiofiles==23.2.1
+    # via gradio
+annotated-types==0.7.0
+    # via pydantic
+anyio==4.8.0
+    # via
+    #   gradio
+    #   httpx
+    #   starlette
+certifi==2025.1.31
+    # via
+    #   httpcore
+    #   httpx
+    #   requests
+charset-normalizer==3.4.1
+    # via requests
+click==8.1.8
+    # via
+    #   typer
+    #   uvicorn
+exceptiongroup==1.2.2
+    # via anyio
+fastapi==0.115.8
+    # via gradio
+ffmpy==0.5.0
+    # via gradio
+filelock==3.17.0
+    # via huggingface-hub
+fsspec==2025.2.0
+    # via
+    #   gradio-client
+    #   huggingface-hub
+gradio==5.16.2
+    # via languagebench (pyproject.toml)
+gradio-client==1.7.1
+    # via gradio
+h11==0.14.0
+    # via
+    #   httpcore
+    #   uvicorn
+httpcore==1.0.7
+    # via httpx
+httpx==0.28.1
+    # via
+    #   gradio
+    #   gradio-client
+    #   safehttpx
+huggingface-hub==0.29.1
+    # via
+    #   gradio
+    #   gradio-client
+idna==3.10
+    # via
+    #   anyio
+    #   httpx
+    #   requests
+jinja2==3.1.5
+    # via gradio
+markdown-it-py==3.0.0
+    # via rich
+markupsafe==2.1.5
+    # via
+    #   gradio
+    #   jinja2
+mdurl==0.1.2
+    # via markdown-it-py
+narwhals==1.27.1
+    # via plotly
+numpy==2.2.3
+    # via
+    #   gradio
+    #   pandas
+orjson==3.10.15
+    # via gradio
+packaging==24.2
+    # via
+    #   gradio
+    #   gradio-client
+    #   huggingface-hub
+    #   plotly
+pandas==2.2.3
+    # via
+    #   languagebench (pyproject.toml)
+    #   gradio
+pillow==11.1.0
+    # via gradio
+plotly==6.0.0
+    # via languagebench (pyproject.toml)
+pydantic==2.10.6
+    # via
+    #   fastapi
+    #   gradio
+pydantic-core==2.27.2
+    # via pydantic
+pydub==0.25.1
+    # via gradio
+pygments==2.19.1
+    # via rich
+python-dateutil==2.9.0.post0
+    # via pandas
+python-multipart==0.0.20
+    # via gradio
+pytz==2025.1
+    # via pandas
+pyyaml==6.0.2
+    # via
+    #   gradio
+    #   huggingface-hub
+requests==2.32.3
+    # via huggingface-hub
+rich==13.9.4
+    # via typer
+ruff==0.9.7
+    # via gradio
+safehttpx==0.1.6
+    # via gradio
+semantic-version==2.10.0
+    # via gradio
+shellingham==1.5.4
+    # via typer
+six==1.17.0
+    # via python-dateutil
+sniffio==1.3.1
+    # via anyio
+starlette==0.45.3
+    # via
+    #   fastapi
+    #   gradio
+tomlkit==0.13.2
+    # via gradio
+tqdm==4.67.1
+    # via huggingface-hub
+typer==0.15.1
+    # via gradio
+typing-extensions==4.12.2
+    # via
+    #   anyio
+    #   fastapi
+    #   gradio
+    #   gradio-client
+    #   huggingface-hub
+    #   pydantic
+    #   pydantic-core
+    #   rich
+    #   typer
+    #   uvicorn
+tzdata==2025.1
+    # via pandas
+urllib3==2.3.0
+    # via requests
+uvicorn==0.34.0
+    # via gradio
+websockets==14.2
+    # via gradio-client

results.json

@@ -0,0 +1,78 @@
+[
+  {
+    "language_name": "English",
+    "language_code": "eng",
+    "speakers": 1132366680.0,
+    "scores": [
+      {
+        "model": "anthropic/claude-3.5-haiku",
+        "bleu": 0.5911781645744415
+      }
+    ],
+    "bleu": 0.5911781645744415
+  },
+  {
+    "language_name": "Mandarin Chinese",
+    "language_code": "cmn",
+    "speakers": 1074000000.0,
+    "scores": [
+      {
+        "model": "openai/gpt-4o-mini",
+        "bleu": 0.5423652619241204
+      },
+      {
+        "model": "anthropic/claude-3.5-haiku",
+        "bleu": 0.4734856962747124
+      },
+      {
+        "model": "google/gemini-flash-1.5",
+        "bleu": 0.430570062499388
+      }
+    ],
+    "bleu": 0.4821403402327402
+  },
+  {
+    "language_name": "Spanish",
+    "language_code": "spa",
+    "speakers": 485000000.0,
+    "scores": [
+      {
+        "model": "anthropic/claude-3.5-haiku",
+        "bleu": 0.4131404308980914
+      }
+    ],
+    "bleu": 0.4131404308980914
+  },
+  {
+    "language_name": "Hindi",
+    "language_code": "hin",
+    "speakers": 341000000.0,
+    "scores": [
+      {
+        "model": "anthropic/claude-3.5-haiku",
+        "bleu": 0.3710125447937959
+      }
+    ],
+    "bleu": 0.3710125447937959
+  },
+  {
+    "language_name": "Bengali",
+    "language_code": "ben",
+    "speakers": 300000000.0,
+    "scores": [
+      {
+        "model": "openai/gpt-4o-mini",
+        "bleu": 0.40080430939726097
+      },
+      {
+        "model": "anthropic/claude-3.5-haiku",
+        "bleu": 0.3733558186182232
+      },
+      {
+        "model": "google/gemini-flash-1.5",
+        "bleu": 0.4337794805645439
+      }
+    ],
+    "bleu": 0.4026465361933427
+  }
+]

src/.gitignore

@@ -1 +0,0 @@
-/.observablehq/cache/

src/compare-ai-models.md

@@ -1,65 +0,0 @@
----
-theme: dashboard
-title: Compare AI models
----
-
-# Compare AI models
-
-```js
-const data = FileAttachment("data/languagebench.json").json();
-```
-
-```js
-const scoreKey = "bleu"
-const scoreName = "BLEU Score"
-
-// Get unique languages with their speaker counts
-const languageMap = new Map();
-data.forEach(r => {
-    if (!languageMap.has(r.language_name)) {
-        languageMap.set(r.language_name, r.speakers);
-    }
-});
-
-// Sort languages by speaker count (descending)
-const languages = [...languageMap.entries()]
-    .sort((a, b) => b[1] - a[1])
-    .map(([lang]) => lang);
-
-// Section for each language
-languages.forEach(language => {
-    display(html`<h2 class="language-header">${language}</h2>`)
-
-    const speakerCount = (languageMap.get(language) / 1_000_000).toFixed(1);
-    display(html`${speakerCount}M speakers`);
-
-    const languageData = data.filter(r => r.language_name === language)[0]["scores"];
-    console.log(languageData)
-
-    const descriptor = code => {
-        let [org, model] = code.split("/")
-        return model.split("-")[0]
-    }
-
-    // Plot for how well the models perform on this language
-    if (languageData && languageData.length >= 1) {
-        console.log("yes")
-        const chart = Plot.plot({
-            width: 400,
-            height: 200,
-            margin: 30,
-            y: {
-                domain: [0, 1],
-                label: scoreName
-            },
-            marks: [
-                Plot.barY(languageData, {
-                    x: d => descriptor(d.model),
-                    y: scoreKey
-                })
-            ]
-        });
-        display(chart)
-    }
-});
-```

src/compare-languages.md

@@ -1,20 +0,0 @@
----
-theme: dashboard
-title: Compare languages
----
-
-# Compare languages
-
-```js
-import { languageChart } from "./components/language-chart.js";
-
-const data = FileAttachment("data/languagebench.json").json();
-```
-
-```js
-const scoreKey = "bleu"
-const scoreName = "BLEU Score"
-
-// Create summary plot
-display(languageChart(data, {width: 1000, height: 400, scoreKey: scoreKey, scoreName: scoreName}))
-```

src/index.md

@@ -1,53 +0,0 @@
----
-toc: false
----
-
-<div class="hero">
-  <h1>AI Language Monitor</h1>
-  <h2>Benchmarking all big AI models on all benchmarkable languages.</h2>
-</div>
-
-```js
-import { languageChart } from "./components/language-chart.js";
-
-const data = FileAttachment("data/languagebench.json").json();
-```
-
-
-<div class="grid grid-cols-2" style="grid-auto-rows: 504px;">
-  <div class="card">
-    <h2 class="hero">Compare languages</h2>
-    ${resize((width) => languageChart(data, {width: 1000, height: 400, scoreKey: "bleu", scoreName: "BLEU Score"}))}
-  </div>
-  <div class="card">
-    <h2 class="hero">Compare AI models</h2>
-    ...
-  </div>
-</div>
-
-<style>
-
-.hero {
-  display: flex;
-  flex-direction: column;
-  align-items: center;
-  font-family: var(--sans-serif);
-  margin: 4rem 0 8rem;
-  text-wrap: balance;
-  text-align: center;
-}
-
-.hero h1 {
-  margin: 1rem 0;
-  padding: 1rem 0;
-  max-width: none;
-  font-size: 90px;
-  font-weight: 900;
-  line-height: 1;
-  background: linear-gradient(30deg, var(--theme-foreground-focus), currentColor);
-  -webkit-background-clip: text;
-  -webkit-text-fill-color: transparent;
-  background-clip: text;
-}
-
-</style>

src/methodology.md

@@ -1,12 +0,0 @@
----
-title: Methodology
----
-
-# Methodology
-
-Sources:
-
-1. For AI models: [OpenRouter](https://openrouter.ai/)
-2. For language benchmarks: [FLORES+](https://github.com/openlanguagedata/flores)
-3. For language statistics: [Wikidata](https://gist.github.com/unhammer/3e8f2e0f79972bf5008a4c970081502d), [Ethnologue](https://www.ethnologue.com/browse/names/)
-

upload.py

@@ -1,21 +0,0 @@
-from huggingface_hub import upload_file, upload_folder
-import os
-from dotenv import load_dotenv
-
-load_dotenv()
-
-args = dict(
-    repo_id="datenlabor-bmz/ai-language-monitor",
-    repo_type="space",
-    token=os.getenv("HUGGINGFACE_ACCESS_TOKEN"),
-)
-upload_folder(
-    folder_path="dist",
-    path_in_repo="/",
-    **args,
-)
-upload_file(
-    path_or_fileobj="README.md",
-    path_in_repo="README.md",
-    **args,
-)

uv.lock

@@ -7,6 +7,15 @@ resolution-markers = [
     "python_full_version >= '3.13'",
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+]
+
 [[package]]
 name = "aiohappyeyeballs"
 version = "2.4.3"
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