diff --git a/.env.example b/.env.example new file mode 100644 index 0000000000000000000000000000000000000000..aa96e1820a29689a9163b6a202ac15bf5d81fd8e --- /dev/null +++ b/.env.example @@ -0,0 +1,3 @@ +ENVIRONMENT=development +HF_TOKEN=xxx +HF_HOME=.cache diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..5249861514094de77ec82ce85d76674ced85afe3 --- /dev/null +++ b/.gitignore @@ -0,0 +1,58 @@ +# See https://help.github.com/articles/ignoring-files/ for more about ignoring files. + +__pycache__ +.cache/ + +# dependencies + +frontend/node_modules +/.pnp +.pnp.js + +# testing +/coverage + +/backend/uploaded_files +/backend/logs + +.venv/ + +__pycache__/ +*.pkl +model_info_cache.pkl +model_size_cache.pkl +gif.gif +src/assets/scale-hf-logo.png + +.litellm_cache/ + +# production + +/build + +# misc + +.DS_Store +.env.local +.env.development.local +.env.test.local +.env.production.local + +npm-debug.log* +yarn-debug.log* +yarn-error.log\* + +src/dataframe.json + +yarn.lock +package-lock.json + +/public + +.claudesync/ + +# Environment variables +.env +.env.* +!.env.example + diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..3ef172998853baa7028b5bb64cf8018bd7534272 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,61 @@ +# Build frontend +FROM node:18 as frontend-build +WORKDIR /app +COPY frontend/package*.json ./ +RUN npm install +COPY frontend/ ./ + +RUN npm run build + +# Build backend +FROM python:3.12-slim +WORKDIR /app + +# Create non-root user +RUN useradd -m -u 1000 user + +# Install uv instead of poetry +RUN pip install uv + +# Create and configure cache directory +RUN mkdir -p /app/.cache && \ + chown -R user:user /app + +# Copy and install backend dependencies +COPY backend/pyproject.toml ./ +RUN uv pip install -e . --system + +# Copy backend code +COPY backend/ . + +# Install Node.js and npm +RUN apt-get update && apt-get install -y \ + curl \ + netcat-openbsd \ + && curl -fsSL https://deb.nodesource.com/setup_18.x | bash - \ + && apt-get install -y nodejs \ + && rm -rf /var/lib/apt/lists/* + +# Copy frontend server and build +COPY --from=frontend-build /app/build ./frontend/build +COPY --from=frontend-build /app/package*.json ./frontend/ +COPY --from=frontend-build /app/server.js ./frontend/ + +# Install frontend production dependencies +WORKDIR /app/frontend +RUN npm install --production +WORKDIR /app + +# Environment variables +ENV HF_HOME=/app/.cache \ + HF_DATASETS_CACHE=/app/.cache \ + INTERNAL_API_PORT=7861 \ + PORT=7860 \ + NODE_ENV=production + +# Note: HF_TOKEN should be provided at runtime, not build time +USER user +EXPOSE 7860 + +# Start both servers with wait-for +CMD ["sh", "-c", "uvicorn app.asgi:app --host 0.0.0.0 --port 7861 & while ! nc -z localhost 7861; do sleep 1; done && cd frontend && npm run serve"] \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..ef286c6b38dccbf2320f9ce01446a0c0877bfea8 --- /dev/null +++ b/README.md @@ -0,0 +1,11 @@ +--- +title: Yourbench simple demo +emoji: 🏆 +colorFrom: blue +colorTo: red +sdk: docker +hf_oauth: true +pinned: true +license: apache-2.0 +short_description: Yourbench demo +--- diff --git a/backend/Dockerfile.dev b/backend/Dockerfile.dev new file mode 100644 index 0000000000000000000000000000000000000000..f802c87f0d5d730c559b1f21ed715b48cc9ca42a --- /dev/null +++ b/backend/Dockerfile.dev @@ -0,0 +1,25 @@ +FROM python:3.12-slim + +WORKDIR /app + +# Install required system dependencies +RUN apt-get update && apt-get install -y \ + build-essential \ + && rm -rf /var/lib/apt/lists/* + +# Install poetry +RUN pip install poetry + +# Copy Poetry configuration files +COPY pyproject.toml poetry.lock* ./ + +# Install dependencies +RUN poetry config virtualenvs.create false && \ + poetry install --no-interaction --no-ansi --no-root + +# Environment variables configuration for logs +ENV PYTHONUNBUFFERED=1 +ENV LOG_LEVEL=INFO + +# In dev, mount volume directly +CMD ["uvicorn", "app.asgi:app", "--host", "0.0.0.0", "--port", "7860", "--reload", "--log-level", "warning", "--no-access-log"] \ No newline at end of file diff --git a/backend/README.md b/backend/README.md new file mode 100644 index 0000000000000000000000000000000000000000..4a9c1e60d0c77b9add02f0c9ba25acaabe6ab2a5 --- /dev/null +++ b/backend/README.md @@ -0,0 +1,352 @@ +# Backend - Open LLM Leaderboard 🏆 + +FastAPI backend for the Open LLM Leaderboard. This service is part of a larger architecture that includes a React frontend. For complete project installation, see the [main README](../README.md). + +## ✨ Features + +- 📊 REST API for LLM models leaderboard management +- 🗳️ Voting and ranking system +- 🔄 HuggingFace Hub integration +- 🚀 Caching and performance optimizations + +## 🏗 Architecture + +```mermaid +flowchart TD + Client(["**Frontend**

React Application"]) --> API["**API Server**

FastAPI REST Endpoints"] + + subgraph Backend + API --> Core["**Core Layer**

• Middleware
• Cache
• Rate Limiting"] + Core --> Services["**Services Layer**

• Business Logic
• Data Processing"] + + subgraph Services Layer + Services --> Models["**Model Service**

• Model Submission
• Evaluation Pipeline"] + Services --> Votes["**Vote Service**

• Vote Management
• Data Synchronization"] + Services --> Board["**Leaderboard Service**

• Rankings
• Performance Metrics"] + end + + Models --> Cache["**Cache Layer**

• In-Memory Store
• Auto Invalidation"] + Votes --> Cache + Board --> Cache + + Models --> HF["**HuggingFace Hub**

• Models Repository
• Datasets Access"] + Votes --> HF + Board --> HF + end + + style Client fill:#f9f,stroke:#333,stroke-width:2px + style Models fill:#bbf,stroke:#333,stroke-width:2px + style Votes fill:#bbf,stroke:#333,stroke-width:2px + style Board fill:#bbf,stroke:#333,stroke-width:2px + style HF fill:#bfb,stroke:#333,stroke-width:2px +``` + +## 🛠️ HuggingFace Datasets + +The application uses several datasets on the HuggingFace Hub: + +### 1. Requests Dataset (`{HF_ORGANIZATION}/requests`) + +- **Operations**: + - 📤 `POST /api/models/submit`: Adds a JSON file for each new model submission + - 📥 `GET /api/models/status`: Reads files to get models status +- **Format**: One JSON file per model with submission details +- **Updates**: On each new model submission + +### 2. Votes Dataset (`{HF_ORGANIZATION}/votes`) + +- **Operations**: + - 📤 `POST /api/votes/{model_id}`: Adds a new vote + - 📥 `GET /api/votes/model/{provider}/{model}`: Reads model votes + - 📥 `GET /api/votes/user/{user_id}`: Reads user votes +- **Format**: JSONL with one vote per line +- **Sync**: Bidirectional between local cache and Hub + +### 3. Contents Dataset (`{HF_ORGANIZATION}/contents`) + +- **Operations**: + - 📥 `GET /api/leaderboard`: Reads raw data + - 📥 `GET /api/leaderboard/formatted`: Reads and formats data +- **Format**: Main dataset containing all scores and metrics +- **Updates**: Automatic after model evaluations + +### 4. Official Providers Dataset (`{HF_ORGANIZATION}/official-providers`) + +- **Operations**: + - 📥 Read-only access for highlighted models +- **Format**: List of models selected by maintainers +- **Updates**: Manual by maintainers + +## 🛠 Local Development + +### Prerequisites + +- Python 3.9+ +- [Poetry](https://python-poetry.org/docs/#installation) + +### Standalone Installation (without Docker) + +```bash +# Install dependencies +poetry install + +# Setup configuration +cp .env.example .env + +# Start development server +poetry run uvicorn app.asgi:app --host 0.0.0.0 --port 7860 --reload +``` + +Server will be available at http://localhost:7860 + +## ⚙️ Configuration + +| Variable | Description | Default | +| ------------ | ------------------------------------ | ----------- | +| ENVIRONMENT | Environment (development/production) | development | +| HF_TOKEN | HuggingFace authentication token | - | +| PORT | Server port | 7860 | +| LOG_LEVEL | Logging level (INFO/DEBUG/WARNING) | INFO | +| CORS_ORIGINS | Allowed CORS origins | ["*"] | +| CACHE_TTL | Cache Time To Live in seconds | 300 | + +## 🔧 Middleware + +The backend uses several middleware layers for optimal performance and security: + +- **CORS Middleware**: Handles Cross-Origin Resource Sharing +- **GZIP Middleware**: Compresses responses > 500 bytes +- **Rate Limiting**: Prevents API abuse +- **Caching**: In-memory caching with automatic invalidation + +## 📝 Logging + +The application uses a structured logging system with: + +- Formatted console output +- Different log levels per component +- Request/Response logging +- Performance metrics +- Error tracking + +## 📁 File Structure + +``` +backend/ +├── app/ # Source code +│ ├── api/ # Routes and endpoints +│ │ └── endpoints/ # Endpoint handlers +│ ├── core/ # Configurations +│ ├── services/ # Business logic +│ └── utils/ # Utilities +└── tests/ # Tests +``` + +## 📚 API + +Swagger documentation available at http://localhost:7860/docs + +### Main Endpoints & Data Structures + +#### Leaderboard + +- `GET /api/leaderboard/formatted` - Formatted data with computed fields and metadata + + ```typescript + Response { + models: [{ + id: string, // eval_name + model: { + name: string, // fullname + sha: string, // Model sha + precision: string, // e.g. "fp16", "int8" + type: string, // e.g. "fined-tuned-on-domain-specific-dataset" + weight_type: string, + architecture: string, + average_score: number, + has_chat_template: boolean + }, + evaluations: { + ifeval: { + name: "IFEval", + value: number, // Raw score + normalized_score: number + }, + bbh: { + name: "BBH", + value: number, + normalized_score: number + }, + math: { + name: "MATH Level 5", + value: number, + normalized_score: number + }, + gpqa: { + name: "GPQA", + value: number, + normalized_score: number + }, + musr: { + name: "MUSR", + value: number, + normalized_score: number + }, + mmlu_pro: { + name: "MMLU-PRO", + value: number, + normalized_score: number + } + }, + features: { + is_not_available_on_hub: boolean, + is_merged: boolean, + is_moe: boolean, + is_flagged: boolean, + is_official_provider: boolean + }, + metadata: { + upload_date: string, + submission_date: string, + generation: string, + base_model: string, + hub_license: string, + hub_hearts: number, + params_billions: number, + co2_cost: number // CO₂ cost in kg + } + }] + } + ``` + +- `GET /api/leaderboard` - Raw data from the HuggingFace dataset + ```typescript + Response { + models: [{ + eval_name: string, + Precision: string, + Type: string, + "Weight type": string, + Architecture: string, + Model: string, + fullname: string, + "Model sha": string, + "Average ⬆️": number, + "Hub License": string, + "Hub ❤️": number, + "#Params (B)": number, + "Available on the hub": boolean, + Merged: boolean, + MoE: boolean, + Flagged: boolean, + "Chat Template": boolean, + "CO₂ cost (kg)": number, + "IFEval Raw": number, + IFEval: number, + "BBH Raw": number, + BBH: number, + "MATH Lvl 5 Raw": number, + "MATH Lvl 5": number, + "GPQA Raw": number, + GPQA: number, + "MUSR Raw": number, + MUSR: number, + "MMLU-PRO Raw": number, + "MMLU-PRO": number, + "Maintainer's Highlight": boolean, + "Upload To Hub Date": string, + "Submission Date": string, + Generation: string, + "Base Model": string + }] + } + ``` + +#### Models + +- `GET /api/models/status` - Get all models grouped by status + ```typescript + Response { + pending: [{ + name: string, + submitter: string, + revision: string, + wait_time: string, + submission_time: string, + status: "PENDING" | "EVALUATING" | "FINISHED", + precision: string + }], + evaluating: Array, + finished: Array + } + ``` +- `GET /api/models/pending` - Get pending models only +- `POST /api/models/submit` - Submit model + + ```typescript + Request { + user_id: string, + model_id: string, + base_model?: string, + precision?: string, + model_type: string + } + + Response { + status: string, + message: string + } + ``` + +- `GET /api/models/{model_id}/status` - Get model status + +#### Votes + +- `POST /api/votes/{model_id}` - Vote + + ```typescript + Request { + vote_type: "up" | "down", + user_id: string // HuggingFace username + } + + Response { + success: boolean, + message: string + } + ``` + +- `GET /api/votes/model/{provider}/{model}` - Get model votes + ```typescript + Response { + total_votes: number, + up_votes: number, + down_votes: number + } + ``` +- `GET /api/votes/user/{user_id}` - Get user votes + ```typescript + Response Array<{ + model_id: string, + vote_type: string, + timestamp: string + }> + ``` + +## 🔒 Authentication + +The backend uses HuggingFace token-based authentication for secure API access. Make sure to: + +1. Set your HF_TOKEN in the .env file +2. Include the token in API requests via Bearer authentication +3. Keep your token secure and never commit it to version control + +## 🚀 Performance + +The backend implements several optimizations: + +- In-memory caching with configurable TTL (Time To Live) +- Batch processing for model evaluations +- Rate limiting for API endpoints +- Efficient database queries with proper indexing +- Automatic cache invalidation for votes diff --git a/backend/main.py b/backend/main.py new file mode 100644 index 0000000000000000000000000000000000000000..3cb69f023e71e1e83584191006bdcbebb9f968b2 --- /dev/null +++ b/backend/main.py @@ -0,0 +1,38 @@ +from fastapi import FastAPI +from fastapi.middleware.cors import CORSMiddleware +import os +from dotenv import load_dotenv +from routes import routers, session_files, active_bench_tasks + +# Load environment variables from .env file +load_dotenv() + +# Verify environment variables are loaded +hf_token = os.getenv("HF_TOKEN") +if not hf_token: + print("Warning: HF_TOKEN environment variable is not set. Make sure it's defined in your .env file.") + +hf_organization = os.getenv("HF_ORGANIZATION") +if not hf_organization: + print("Warning: HF_ORGANIZATION environment variable is not set. Make sure it's defined in your .env file.") + +app = FastAPI(title="Yourbench API") + +# Activer CORS pour permettre les requêtes depuis le frontend +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], # Dans un environnement de production, spécifiez les origines exactes + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +# Ajouter un gestionnaire d'événements pour afficher les session_files au démarrage +@app.on_event("startup") +async def startup_event(): + print("Application startup") + print(f"Initial session_files: {session_files}") + +# Enregistrer toutes les routes +for router in routers: + app.include_router(router) \ No newline at end of file diff --git a/backend/old-pyproject.toml b/backend/old-pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..4c00db1bdd3982d26f2a2e3730d8df0768a5c120 --- /dev/null +++ b/backend/old-pyproject.toml @@ -0,0 +1,26 @@ +[tool.poetry] +name = "llm-leaderboard-backend" +version = 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active_bench_tasks +from .questions import router as questions_router +from .download import router as download_router +from .evaluation import router as evaluation_router, active_evaluation_tasks + +# Exposer les routeurs +routers = [ + health_router, + upload_router, + benchmark_router, + questions_router, + download_router, + evaluation_router +] + +# Référencer les données partagées entre routes +benchmark_router.session_files = session_files + +# Exposer les variables partagées pour main.py +__all__ = ['routers', 'session_files', 'active_bench_tasks', 'active_evaluation_tasks'] \ No newline at end of file diff --git a/backend/routes/benchmark.py b/backend/routes/benchmark.py new file mode 100644 index 0000000000000000000000000000000000000000..0076f3b2f87a62298ca303347bd86318aff6118f --- /dev/null +++ b/backend/routes/benchmark.py @@ -0,0 +1,132 @@ +from fastapi import APIRouter, HTTPException +from typing import Dict, Any +import os +import time +from tasks.createBenchConfigFile import CreateBenchConfigTask +from tasks.createBench import CreateBenchTask + +router = APIRouter(tags=["benchmark"]) + +# Store active tasks by session_id (importé dans main.py) +active_bench_tasks = {} +active_config_tasks = {} + +# Référence aux session_files (sera fournie par main.py) +# Cette déclaration sera écrasée par l'affectation dans __init__.py +session_files = {} + +@router.post("/generate-benchmark") +async def generate_benchmark(data: Dict[str, Any]): + """ + Generate a benchmark configuration and run the ingestion process + + Args: + data: Dictionary containing session_id + + Returns: + Dictionary with logs and config_path + """ + session_id = data.get("session_id") + + # Débogage pour vérifier les session_files et le session_id reçu + print(f"DEBUG: Session ID reçu: {session_id}") + print(f"DEBUG: Session files disponibles: {list(router.session_files.keys())}") + + if not session_id or session_id not in router.session_files: + return {"error": "Invalid or missing session ID"} + + file_path = router.session_files[session_id] + all_logs = [] + + try: + # Step 1: Generate configuration file + config_task = CreateBenchConfigTask(session_uid=session_id) + # Store the config task for later log retrieval + active_config_tasks[session_id] = config_task + + # Start configuration generation asynchronously + config_path = config_task.run(file_path=file_path) + + # Add initial logs + all_logs.extend(config_task.get_logs()) + + # Step 2: Run the createBench task with the generated config + # Note: This will be started by a separate endpoint once configuration is done + + return { + "status": "running", + "config_path": config_path, + "logs": all_logs + } + except Exception as e: + return { + "status": "error", + "error": str(e), + "logs": all_logs + } + +@router.get("/config-logs/{session_id}") +async def get_config_logs(session_id: str): + """ + Get the logs for a running configuration task + + Args: + session_id: Session ID for the task + + Returns: + Dictionary with logs and completion status + """ + if session_id not in active_config_tasks: + raise HTTPException(status_code=404, detail="Configuration task not found") + + config_task = active_config_tasks[session_id] + logs = config_task.get_logs() + is_completed = config_task.is_task_completed() + + # Si la configuration est terminée et que le benchmark n'est pas encore démarré, + # démarrer automatiquement le benchmark + if is_completed and session_id not in active_bench_tasks: + try: + # Ensure the config_path is a string + config_path_str = f"uploaded_files/{session_id}/config.yml" + bench_task = CreateBenchTask(session_uid=session_id, config_path=config_path_str) + + # Store the bench task for later log retrieval + active_bench_tasks[session_id] = bench_task + + # Add a transition log + logs.append("[INFO] Configuration file generated, starting benchmark creation") + + # Run the task + bench_task.run() + except Exception as bench_error: + error_msg = f"Error starting benchmark creation: {str(bench_error)}" + logs.append(f"[ERROR] {error_msg}") + + return { + "logs": logs, + "is_completed": is_completed + } + +@router.get("/benchmark-logs/{session_id}") +async def get_benchmark_logs(session_id: str): + """ + Get the logs for a running benchmark task + + Args: + session_id: Session ID for the task + + Returns: + Dictionary with logs and completion status + """ + if session_id not in active_bench_tasks: + raise HTTPException(status_code=404, detail="Benchmark task not found") + + bench_task = active_bench_tasks[session_id] + logs = bench_task.get_logs() + is_completed = bench_task.is_task_completed() + + return { + "logs": logs, + "is_completed": is_completed + } \ No newline at end of file diff --git a/backend/routes/download.py b/backend/routes/download.py new file mode 100644 index 0000000000000000000000000000000000000000..753fca403ad8252b01443126479c8a388c8e8181 --- /dev/null +++ b/backend/routes/download.py @@ -0,0 +1,74 @@ +from fastapi import APIRouter, HTTPException +from fastapi.responses import StreamingResponse +from huggingface_hub import hf_hub_download, snapshot_download +import os +import tempfile +import shutil +import zipfile +import io +import logging + +router = APIRouter(tags=["download"]) + +@router.get("/download-dataset/{session_id}") +async def download_dataset(session_id: str): + """ + Télécharge le dataset HuggingFace associé à une session et le renvoie au client + + Args: + session_id: Identifiant de la session + + Returns: + Fichier ZIP contenant le dataset + """ + try: + # Créer un répertoire temporaire pour stocker les fichiers du dataset + with tempfile.TemporaryDirectory() as temp_dir: + # Identifiant du repo HuggingFace + repo_id = f"yourbench/yourbench_{session_id}" + + try: + # Télécharger le snapshot du dataset depuis HuggingFace + logging.info(f"Téléchargement du dataset {repo_id}") + snapshot_path = snapshot_download( + repo_id=repo_id, + repo_type="dataset", + local_dir=temp_dir, + token=os.environ.get("HF_TOKEN") + ) + + logging.info(f"Dataset téléchargé dans {snapshot_path}") + + # Créer un fichier ZIP en mémoire + zip_io = io.BytesIO() + with zipfile.ZipFile(zip_io, 'w', zipfile.ZIP_DEFLATED) as zip_file: + # Parcourir tous les fichiers du dataset et les ajouter au ZIP + for root, _, files in os.walk(snapshot_path): + for file in files: + file_path = os.path.join(root, file) + arc_name = os.path.relpath(file_path, snapshot_path) + zip_file.write(file_path, arcname=arc_name) + + # Remettre le curseur au début du stream + zip_io.seek(0) + + # Renvoyer le ZIP au client + filename = f"yourbench_{session_id}_dataset.zip" + return StreamingResponse( + zip_io, + media_type="application/zip", + headers={"Content-Disposition": f"attachment; filename={filename}"} + ) + + except Exception as e: + logging.error(f"Erreur lors du téléchargement du dataset: {str(e)}") + raise HTTPException( + status_code=500, + detail=f"Erreur lors du téléchargement du dataset: {str(e)}" + ) + except Exception as e: + logging.error(f"Erreur générale: {str(e)}") + raise HTTPException( + status_code=500, + detail=f"Erreur lors du téléchargement: {str(e)}" + ) \ No newline at end of file diff --git a/backend/routes/evaluation.py b/backend/routes/evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..e6db78af70fb2eb3131bcfffac7cdc1829c46a33 --- /dev/null +++ b/backend/routes/evaluation.py @@ -0,0 +1,150 @@ +from fastapi import APIRouter, HTTPException +from typing import Dict, Any +import os +from tasks.evaluationTask import EvaluationTask + +router = APIRouter(tags=["evaluation"]) + +# Store active evaluation tasks by session_id +active_evaluation_tasks = {} + +@router.post("/evaluate-benchmark") +async def evaluate_benchmark(data: Dict[str, Any]): + """ + Lancer l'évaluation d'un benchmark pour une session donnée + + Args: + data: Dictionary contenant session_id + + Returns: + Dictionary avec statut et logs initiaux + """ + session_id = data.get("session_id") + + if not session_id: + return {"error": "Session ID manquant ou invalide"} + + # Vérifier si une évaluation est déjà en cours pour cette session + if session_id in active_evaluation_tasks: + evaluation_task = active_evaluation_tasks[session_id] + # Si l'évaluation est déjà terminée, on peut en lancer une nouvelle + if evaluation_task.is_task_completed(): + # Suppression de l'ancienne tâche + del active_evaluation_tasks[session_id] + else: + # Une évaluation est déjà en cours + return { + "status": "already_running", + "message": "Une évaluation est déjà en cours pour cette session", + "logs": evaluation_task.get_logs() + } + + try: + # Nom du dataset basé sur l'ID de session + dataset_name = f"yourbench_{session_id}" + + # Créer et démarrer une nouvelle tâche d'évaluation + evaluation_task = EvaluationTask(session_uid=session_id, dataset_name=dataset_name) + active_evaluation_tasks[session_id] = evaluation_task + + # Démarrer l'évaluation de manière asynchrone + evaluation_task.run() + + # Récupérer les logs initiaux + initial_logs = evaluation_task.get_logs() + + return { + "status": "started", + "message": f"Évaluation démarrée pour le benchmark {dataset_name}", + "logs": initial_logs + } + except Exception as e: + return { + "status": "error", + "error": str(e), + "message": f"Erreur lors du démarrage de l'évaluation: {str(e)}" + } + +@router.get("/evaluation-logs/{session_id}") +async def get_evaluation_logs(session_id: str): + """ + Récupérer les logs d'une évaluation en cours + + Args: + session_id: ID de la session pour laquelle récupérer les logs + + Returns: + Dictionary avec logs et statut de complétion + """ + if session_id not in active_evaluation_tasks: + raise HTTPException(status_code=404, detail="Tâche d'évaluation non trouvée") + + evaluation_task = active_evaluation_tasks[session_id] + logs = evaluation_task.get_logs() + is_completed = evaluation_task.is_task_completed() + + # Récupérer les résultats si disponibles et l'évaluation est terminée + results = None + if is_completed and hasattr(evaluation_task, 'results') and evaluation_task.results: + results = evaluation_task.results + + return { + "logs": logs, + "is_completed": is_completed, + "results": results + } + +@router.get("/evaluation-results/{session_id}") +async def get_evaluation_results(session_id: str): + """ + Retrieve results of a completed evaluation + + Args: + session_id: Session ID to retrieve results for + + Returns: + Dictionary with evaluation results + """ + # First, check if the task is in memory + if session_id in active_evaluation_tasks: + evaluation_task = active_evaluation_tasks[session_id] + + if not evaluation_task.is_task_completed(): + return { + "success": False, + "message": "Evaluation is still in progress" + } + + if hasattr(evaluation_task, 'results') and evaluation_task.results: + return { + "success": True, + "results": evaluation_task.results + } + + # If we get here, either the task is not in memory or it doesn't have results + # Try to load results from file + try: + # Construct the path to the results file + results_path = f"uploaded_files/{session_id}/lighteval_results/models_comparison.json" + + # Check if the file exists + if not os.path.exists(results_path): + return { + "success": False, + "message": "No evaluation results found for this session" + } + + # Read the file + import json + with open(results_path, 'r') as f: + results = json.load(f) + + return { + "success": True, + "results": results + } + except Exception as e: + return { + "success": False, + "message": f"Error retrieving evaluation results: {str(e)}" + } \ No newline at end of file diff --git a/backend/routes/health.py b/backend/routes/health.py new file mode 100644 index 0000000000000000000000000000000000000000..3a1b1c96252baacff78b34da8ad35d58a3893b23 --- /dev/null +++ b/backend/routes/health.py @@ -0,0 +1,13 @@ +from fastapi import APIRouter + +router = APIRouter(tags=["health"]) + +@router.get("/health") +async def health_check(): + """ + Check if the API is running properly + + Returns: + Dictionary with status + """ + return {"status": "ok"} \ No newline at end of file diff --git a/backend/routes/questions.py b/backend/routes/questions.py new file mode 100644 index 0000000000000000000000000000000000000000..3a3fb32e3f0d259d73e231c0d68627c7552b95c7 --- /dev/null +++ b/backend/routes/questions.py @@ -0,0 +1,87 @@ +from fastapi import APIRouter, HTTPException +import random +from datasets import load_dataset +from huggingface_hub import HfApi, dataset_info +import os + +router = APIRouter(tags=["benchmark"]) + +@router.get("/benchmark-questions/{session_id}") +async def get_benchmark_questions(session_id: str): + """ + Get example questions from the generated benchmark + + Args: + session_id: Session ID for the benchmark + + Returns: + Dictionary with sample questions from the dataset + """ + try: + # Dataset path on Hugging Face + dataset_repo_id = f"yourbench/yourbench_{session_id}" + + # Initialize response + response = { + "success": False, + "questions": [], + "dataset_url": f"https://huggingface.co/datasets/{dataset_repo_id}" + } + + # Try to load the dataset + questions = [] + + try: + # Essayer de charger les questions single-shot directement avec le nom de config + single_dataset = load_dataset(dataset_repo_id, 'single_shot_questions') + if single_dataset and len(single_dataset['train']) > 0: + # Get a random sample (up to 2) from single-shot questions + sample_indices = random.sample(range(len(single_dataset['train'])), min(2, len(single_dataset['train']))) + for idx in sample_indices: + questions.append({ + "id": str(idx), + "question": single_dataset['train'][idx].get("question", ""), + "type": "single_shot" + }) + print(f"Loaded {len(questions)} single-shot questions") + except Exception as e: + print(f"Error loading single-shot questions: {str(e)}") + + try: + # Essayer de charger les questions multi-hop si nécessaire + if len(questions) < 2: + multi_dataset = load_dataset(dataset_repo_id, 'multi_hop_questions') + if multi_dataset and len(multi_dataset['train']) > 0: + # Get remaining questions from multi-hop questions + remaining = 2 - len(questions) + sample_indices = random.sample(range(len(multi_dataset['train'])), min(remaining, len(multi_dataset['train']))) + for idx in sample_indices: + questions.append({ + "id": str(idx), + "question": multi_dataset['train'][idx].get("question", ""), + "type": "multi_hop" + }) + print(f"Loaded {len(questions)} multi-hop questions") + except Exception as e: + print(f"Error loading multi-hop questions: {str(e)}") + + # If we couldn't load any questions, the dataset might not exist + if len(questions) == 0: + # Check if we have a directory for this session locally as fallback + session_dir = os.path.join("uploaded_files", session_id) + if not os.path.exists(session_dir): + raise HTTPException(status_code=404, detail="Dataset not found") + + # Update the response + response["success"] = len(questions) > 0 + response["questions"] = questions + return response + + except HTTPException: + # Re-raise HTTP exceptions + raise + except Exception as e: + return { + "success": False, + "error": f"Error retrieving benchmark questions: {str(e)}" + } \ No newline at end of file diff --git a/backend/routes/upload.py b/backend/routes/upload.py new file mode 100644 index 0000000000000000000000000000000000000000..8de863c55c9e22f661c682a16a2ec93143e69819 --- /dev/null +++ b/backend/routes/upload.py @@ -0,0 +1,52 @@ +from fastapi import APIRouter, UploadFile, File +import os +import shutil +import uuid + +router = APIRouter(tags=["files"]) + +# Définir le stockage des fichiers par session (importé dans main.py) +session_files = {} + +# Dossier racine pour les uploads +UPLOAD_ROOT = "uploaded_files" +os.makedirs(UPLOAD_ROOT, exist_ok=True) + +@router.post("/upload") +async def upload_file(file: UploadFile = File(...)): + """ + Upload a file to the server and generate a session ID + + Args: + file: The file to upload + + Returns: + Dictionary with filename, status and session_id + """ + # Vérifier si le fichier est un PDF, TXT, HTML ou MD + if not file.filename.endswith(('.pdf', '.txt', '.html', '.md')): + return {"error": "Only PDF, TXT, HTML and MD files are accepted"} + + # Generate a session ID for this file + session_id = str(uuid.uuid4()) + + # Create the session directory structure + session_dir = os.path.join(UPLOAD_ROOT, session_id) + uploaded_files_dir = os.path.join(session_dir, "uploaded_files") + os.makedirs(uploaded_files_dir, exist_ok=True) + + # Create the full path to save the file + file_path = os.path.join(uploaded_files_dir, file.filename) + + # Sauvegarder le fichier + with open(file_path, "wb") as buffer: + shutil.copyfileobj(file.file, buffer) + + # Store file path for later use + session_files[session_id] = file_path + + # Débogage pour vérifier l'état des session_files + print(f"DEBUG UPLOAD: File uploaded with session_id: {session_id}") + print(f"DEBUG UPLOAD: Current session_files: {session_files}") + + return {"filename": file.filename, "status": "uploaded", "session_id": session_id} \ No newline at end of file diff --git a/backend/tasks/__init__.py b/backend/tasks/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1be5f30c5341f46dc4c1c3833b4ca62a5c0e2e77 --- /dev/null +++ b/backend/tasks/__init__.py @@ -0,0 +1,3 @@ +""" +Tasks module for YourbenchSimpleDemo +""" \ No newline at end of file diff --git a/backend/tasks/createBench.py b/backend/tasks/createBench.py new file mode 100644 index 0000000000000000000000000000000000000000..43795b6a92b503f643b2baa90768c75370ab84f9 --- /dev/null +++ b/backend/tasks/createBench.py @@ -0,0 +1,317 @@ +""" +Task to ingest and transform documents to markdown using yourbench +""" +import os +import time +import pathlib +import subprocess +import threading +from typing import Optional, List, Tuple, Dict, Any +import yaml + +from loguru import logger + + +class CreateBenchTask: + """ + Task to ingest and transform documents to markdown using yourbench + """ + + def __init__(self, session_uid: str, config_path: Optional[str] = None): + """ + Initialize the ingestion task + + Args: + session_uid: Session ID for this task + config_path: Path to the configuration file, will be generated if None + """ + self.session_uid = session_uid + self.logs: List[str] = [] + self.is_completed = False + self.process = None + self.is_running_flag = threading.Event() + + # Default config path if not provided + if config_path is None: + config_path = f"uploaded_files/{session_uid}/config.yml" + self.config_path = config_path + + # Command to run yourbench - modified to avoid error with uv run + self.command = ["yourbench", "run", "--config", str(self.config_path)] + + self._add_log("[INFO] Initializing ingestion task") + self._add_log(f"[INFO] Using configuration file: {self.config_path}") + + def _add_log(self, message: str) -> None: + """ + Add a log message to the logs list + + Args: + message: Log message to add + """ + if message not in self.logs: # Avoid duplicates + self.logs.append(message) + # Force copy of the list to avoid reference problems + self.logs = self.logs.copy() + # Log to system logs + logger.info(f"[{self.session_uid}] {message}") + + def get_logs(self) -> List[str]: + """ + Get all logs for this task + + Returns: + List of log messages + """ + return self.logs.copy() # Return a copy to avoid reference problems + + def is_task_completed(self) -> bool: + """ + Check if the task is completed + + Returns: + True if completed, False otherwise + """ + return self.is_completed + + def is_running(self) -> bool: + """ + Check if the process is running + + Returns: + True if running, False otherwise + """ + return self.is_running_flag.is_set() + + def stop(self) -> None: + """ + Stop the process if it's running + """ + if self.process and self.is_running(): + self._add_log("[INFO] Stopping ingestion process") + try: + self.process.terminate() + # Wait 5 seconds for termination + self.process.wait(timeout=5) + except subprocess.TimeoutExpired: + self._add_log("[WARN] Process not responding, forcing termination") + self.process.kill() + finally: + self.is_running_flag.clear() + self.is_completed = True + self._add_log("[INFO] Ingestion process stopped") + + def _capture_output(self) -> None: + """ + Capture and process the output from the yourbench process + """ + self._add_log("[INFO] Starting output capture") + + try: + while self.is_running() and self.process: + line = self.process.stdout.readline() + if not line: + # If no line is read and the process is no longer running + if self.process.poll() is not None: + self.is_running_flag.clear() + break + # Otherwise, wait a bit and continue + time.sleep(0.1) + continue + + # Process the output line + line = line.strip() + if line: + # Filter and format the line as needed + if "ERROR" in line: + self._add_log(f"[ERROR] {line}") + elif "WARNING" in line: + self._add_log(f"[WARN] {line}") + else: + # Detect completed stages + if "Completed stage:" in line: + stage = line.split("'")[1] if "'" in line else line + self._add_log(f"[SUCCESS] Stage completed: {stage}") + else: + self._add_log(f"[INFO] {line}") + + # Check exit code once the process is finished + if self.process: + exit_code = self.process.poll() + if exit_code == 0: + self._add_log("[SUCCESS] Ingestion process completed successfully") + else: + self._add_log(f"[ERROR] Ingestion process terminated with error code: {exit_code}") + except Exception as e: + self._add_log(f"[ERROR] Error during output capture: {str(e)}") + finally: + self.is_completed = True + self.is_running_flag.clear() + self._add_log("[INFO] Output capture completed") + + def run(self, token: Optional[str] = None) -> None: + """ + Run the ingestion task + + Args: + token: Hugging Face token + """ + try: + self._add_log("[INFO] Starting ingestion process") + + # Check if the configuration file exists + if not os.path.exists(self.config_path): + raise FileNotFoundError(f"Configuration file does not exist: {self.config_path}") + + # Examine the configuration to get information + try: + with open(self.config_path, 'r') as f: + config_yaml = yaml.safe_load(f) + + # Get source and destination paths + source_dir = config_yaml.get("pipeline", {}).get("ingestion", {}).get("source_documents_dir", "") + output_dir = config_yaml.get("pipeline", {}).get("ingestion", {}).get("output_dir", "") + + if source_dir: + self._add_log(f"[INFO] Source directory: {source_dir}") + if output_dir: + self._add_log(f"[INFO] Output directory: {output_dir}") + + # List files to process if the directory exists + if source_dir and os.path.exists(source_dir): + files = os.listdir(source_dir) + if files: + self._add_log(f"[INFO] Files to process: {', '.join(files)}") + else: + self._add_log("[WARN] No files found in source directory") + + except Exception as e: + self._add_log(f"[WARN] Unable to read configuration: {str(e)}") + + # Environment preparation + env = os.environ.copy() + + # Explicitly define environment variables for authentication + hf_token = os.getenv("HF_TOKEN") + if hf_token: + # Explicitly export these variables for yourbench + env["HF_TOKEN"] = hf_token + env["HUGGING_FACE_HUB_TOKEN"] = hf_token + env["HF_ORGANIZATION"] = os.getenv("HF_ORGANIZATION", "yourbench") + self._add_log("[INFO] Environment variables HF_TOKEN, HUGGING_FACE_HUB_TOKEN and HF_ORGANIZATION exported") + + # In development mode, only simulate ingestion + if os.environ.get("DEVELOPMENT_MODE", "").lower() == "true": + self._add_log("[INFO] Development mode enabled, simulating ingestion") + self._simulate_ingestion_process() + return + + # Start the process + self._add_log(f"[INFO] Executing command: {' '.join(self.command)}") + + self.process = subprocess.Popen( + self.command, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + text=True, + bufsize=1, + universal_newlines=True, + env=env + ) + + # Mark the process as running + self.is_running_flag.set() + + # Start a thread to capture output + output_thread = threading.Thread(target=self._capture_output) + output_thread.daemon = True + output_thread.start() + + self._add_log(f"[INFO] Process started with PID: {self.process.pid}") + + except Exception as e: + self._add_log(f"[ERROR] Error starting ingestion process: {str(e)}") + self.is_completed = True + + def _simulate_ingestion_process(self) -> None: + """ + Simulate the ingestion process for testing/development + This will be removed in production + """ + # This method is just to simulate logs during development + # It will be removed in production + + threading.Thread(target=self._simulate_logs).start() + + def _simulate_logs(self) -> None: + """ + Simulate logs for testing/development + This will be used when yourbench isn't installed or in development mode + """ + # Log simulation (used when yourbench is not available) + self._add_log("[INFO] Simulation mode enabled (yourbench is not actually running)") + + # Get filenames from source directory + source_files = [] + try: + with open(self.config_path, 'r') as f: + config_yaml = yaml.safe_load(f) + + source_dir = config_yaml.get("pipeline", {}).get("ingestion", {}).get("source_documents_dir", "") + if source_dir and os.path.exists(source_dir): + source_files = [f for f in os.listdir(source_dir) + if os.path.isfile(os.path.join(source_dir, f))] + except Exception: + source_files = ["document.pdf", "document.txt"] # Fallback + + # Create output directory if it doesn't exist + output_dir = "" + try: + output_dir = config_yaml.get("pipeline", {}).get("ingestion", {}).get("output_dir", "") + if output_dir: + os.makedirs(output_dir, exist_ok=True) + except Exception: + pass + + # Simulate file processing + time.sleep(1) + self._add_log("[INFO] Initializing document ingestion") + time.sleep(1.5) + self._add_log("[INFO] Loading configuration parameters") + time.sleep(1) + self._add_log("[INFO] Verifying source files") + + # Process each file + for file in source_files: + time.sleep(1.5) + self._add_log(f"[INFO] Processing file: {file}") + time.sleep(2) + self._add_log(f"[INFO] Extracting content from {file}") + time.sleep(1.5) + self._add_log(f"[INFO] Converting to markdown: {file}") + + # Create a simulated markdown file if an output directory is defined + if output_dir: + base_name = os.path.splitext(file)[0] + output_file = os.path.join(output_dir, f"{base_name}.md") + try: + with open(output_file, 'w') as f: + f.write(f"# {base_name}\n\n") + f.write("This is a markdown document automatically generated by the simulation.\n\n") + f.write("## Section 1\n\n") + f.write("Content of section 1...\n\n") + f.write("## Section 2\n\n") + f.write("Content of section 2...\n\n") + self._add_log(f"[INFO] Markdown file created: {output_file}") + except Exception as e: + self._add_log(f"[ERROR] Error creating markdown file: {str(e)}") + + time.sleep(2) + self._add_log("[INFO] Finalizing processing") + time.sleep(1) + self._add_log("[SUCCESS] Stage completed: ingestion") + time.sleep(0.5) + self._add_log("[SUCCESS] Ingestion completed successfully") + + # Mark task as completed + self.is_completed = True \ No newline at end of file diff --git a/backend/tasks/createBenchConfigFile.py b/backend/tasks/createBenchConfigFile.py new file mode 100644 index 0000000000000000000000000000000000000000..94a7a43b348ebb0636c95948fa4f5fd593eb8ceb --- /dev/null +++ b/backend/tasks/createBenchConfigFile.py @@ -0,0 +1,313 @@ +""" +Task to create and save the configuration file +""" +import os +import pathlib +import uuid +import yaml +import shutil +import time +import threading +from typing import Optional, Dict, Any, List, Tuple + +from loguru import logger +from huggingface_hub import HfApi + + +class CreateBenchConfigTask: + """ + Task to create and save a configuration file for YourbenchSimpleDemo + """ + + def __init__(self, session_uid: Optional[str] = None): + """ + Initialize the task with a session ID + + Args: + session_uid: Optional session ID, will be generated if None + """ + self.session_uid = session_uid or str(uuid.uuid4()) + self.logs: List[str] = [] + self.is_completed = False + self.is_running_flag = threading.Event() + self.thread = None + self._add_log("[INFO] Initializing configuration creation task") + + def _add_log(self, message: str) -> None: + """ + Add a log message to the logs list + + Args: + message: Log message to add + """ + if message not in self.logs: # Avoid duplicates + self.logs.append(message) + # Force a copy of the list to avoid reference issues + self.logs = self.logs.copy() + # Log to system logs + logger.info(f"[{self.session_uid}] {message}") + + def get_logs(self) -> List[str]: + """ + Get all logs for this task + + Returns: + List of log messages + """ + return self.logs.copy() # Retourner une copie pour éviter les problèmes de référence + + def save_uploaded_file(self, file_path: str) -> str: + """ + Process the uploaded file that is already in the correct directory + + Args: + file_path: Path to the uploaded file + + Returns: + Path to the file (same as input) + """ + try: + # The file is already in the correct location: uploaded_files/{session_id}/uploaded_files/ + # Just log that we're processing it and return the path + self._add_log(f"[INFO] Processing file: {os.path.basename(file_path)}") + return file_path + except Exception as e: + error_msg = f"Error processing file: {str(e)}" + self._add_log(f"[ERROR] {error_msg}") + raise RuntimeError(error_msg) + + def generate_base_config(self, hf_org: str, hf_dataset_name: str) -> Dict[str, Any]: + """ + Create the base configuration dictionary + + Args: + hf_org: Hugging Face organization name + hf_dataset_name: Hugging Face dataset name + + Returns: + Configuration dictionary + """ + self._add_log(f"[INFO] Generating base configuration for {hf_dataset_name}") + + # Check if HF token is available + hf_token = os.getenv("HF_TOKEN") + if not hf_token: + raise RuntimeError("HF_TOKEN environment variable is not defined") + + return { + "hf_configuration": { + "token": "$HF_TOKEN", # Utiliser directement le token de l'environnement + "hf_organization": "$HF_ORGANIZATION", + "private": True, + "hf_dataset_name": hf_dataset_name, + "concat_if_exist": False, + }, + "model_list": [ + { + "model_name": "Qwen/Qwen2.5-VL-72B-Instruct", + "provider": "novita", + "api_key": "$HF_TOKEN", + "max_concurrent_requests": 32, + }, + { + "model_name": "Qwen/Qwen2.5-72B-Instruct", + "provider": "novita", + "api_key": "$HF_TOKEN", + "max_concurrent_requests": 32, + }, + ], + + "model_roles": { + "ingestion": ["Qwen/Qwen2.5-VL-72B-Instruct"], + "summarization": ["Qwen/Qwen2.5-72B-Instruct"], + "chunking": ["intfloat/multilingual-e5-large-instruct"], + "single_shot_question_generation": ["Qwen/Qwen2.5-72B-Instruct"], + "multi_hop_question_generation": ["Qwen/Qwen2.5-72B-Instruct"], + }, + "pipeline": { + "ingestion": { + "source_documents_dir": f"uploaded_files/{self.session_uid}/uploaded_files/", + "output_dir": f"uploaded_files/{self.session_uid}/ingested", + "run": True, + }, + "upload_ingest_to_hub": { + "source_documents_dir": f"uploaded_files/{self.session_uid}/ingested", + "run": True, # Réactivé pour l'upload sur le Hub + }, + "summarization": { + "run": True, + }, + "chunking": { + "run": True, + "chunking_configuration": { + "l_min_tokens": 64, + "l_max_tokens": 128, + "tau_threshold": 0.8, + "h_min": 2, + "h_max": 5, + "num_multihops_factor": 2, + }, + }, + "single_shot_question_generation": { + "run": True, + "additional_instructions": "Generate questions to test a curious adult", + "chunk_sampling": { + "mode": "count", + "value": 5, + "random_seed": 123, + }, + }, + "multi_hop_question_generation": { + "run": True, + "additional_instructions": "Generate questions to test a curious adult", + "chunk_sampling": { + "mode": "percentage", + "value": 0.3, + "random_seed": 42, + }, + }, + "lighteval": { + "run": True, + }, + }, + } + + def save_yaml_file(self, config: Dict[str, Any], path: str) -> str: + """ + Save the given configuration dictionary to a YAML file + + Args: + config: Configuration dictionary + path: Path to save the file + + Returns: + Path to the saved file + """ + try: + # Create directory if it doesn't exist + os.makedirs(os.path.dirname(path), exist_ok=True) + + with open(path, "w") as file: + yaml.dump(config, file, default_flow_style=False, sort_keys=False) + + self._add_log(f"[INFO] Configuration saved: {path}") + return path + except Exception as e: + error_msg = f"Error saving configuration: {str(e)}" + self._add_log(f"[ERROR] {error_msg}") + raise RuntimeError(error_msg) + + def _run_task(self, file_path: str) -> str: + """ + Internal method to run the task in a separate thread + + Args: + file_path: Path to the uploaded file + + Returns: + Path to the configuration file + """ + try: + # Use the default yourbench organization + org_name = os.getenv("HF_ORGANIZATION") + + # Check if HF token is available + hf_token = os.getenv("HF_TOKEN") + if not hf_token: + raise RuntimeError("HF_TOKEN environment variable is not defined") + + self._add_log(f"[INFO] Organization: {org_name}") + + time.sleep(0.5) # Simulate delay + + # Save the uploaded file + saved_file_path = self.save_uploaded_file(file_path) + + time.sleep(1) # Simulate delay + + # Path for the config file + config_dir = pathlib.Path(f"uploaded_files/{self.session_uid}") + config_path = config_dir / "config.yml" + + # Generate dataset name based on session ID + dataset_name = f"yourbench_{self.session_uid}" + self._add_log(f"[INFO] Dataset name: {dataset_name}") + + time.sleep(0.8) # Simulate delay + + # Generate and save the configuration + config = self.generate_base_config(org_name, dataset_name) + + time.sleep(1.2) # Simulate delay + + config_file_path = self.save_yaml_file(config, str(config_path)) + + self._add_log(f"[INFO] Configuration generated successfully: {config_file_path}") + + # Simulate additional processing + time.sleep(1.5) # Simulate delay + self._add_log("[INFO] Starting ingestion") + + time.sleep(2) # Simulate delay + self._add_log(f"[INFO] Processing file: {dataset_name}") + + time.sleep(2) # Simulate delay + self._add_log("[SUCCESS] Stage completed: config_generation") + + # Tâche terminée + self.mark_task_completed() + + return str(config_path) + except Exception as e: + error_msg = f"Error generating configuration: {str(e)}" + self._add_log(f"[ERROR] {error_msg}") + self.mark_task_completed() + raise RuntimeError(error_msg) + + def run(self, file_path: str, token: Optional[str] = None) -> str: + """ + Run the task to create and save the configuration file asynchronously + + Args: + file_path: Path to the uploaded file + token: Hugging Face token (not used, using HF_TOKEN from environment) + + Returns: + Path to the configuration file + """ + # Mark the task as running + self.is_running_flag.set() + + # Start the task in a separate thread + self.thread = threading.Thread(target=self._run_task, args=(file_path,)) + self.thread.daemon = True + self.thread.start() + + # Return the expected config path + return f"uploaded_files/{self.session_uid}/config.yml" + + def is_running(self) -> bool: + """ + Check if the task is running + + Returns: + True if running, False otherwise + """ + return self.is_running_flag.is_set() and not self.is_completed + + def is_task_completed(self) -> bool: + """ + Check if the task is completed + + Returns: + True if completed, False otherwise + """ + return self.is_completed + + def mark_task_completed(self) -> None: + """ + Mark the task as completed + """ + self.is_completed = True + self.is_running_flag.clear() + self._add_log("[INFO] Configuration generation task completed") \ No newline at end of file diff --git a/backend/tasks/evaluationTask.py b/backend/tasks/evaluationTask.py new file mode 100644 index 0000000000000000000000000000000000000000..29a3b65ec2898468217927e75a5250cf0603c870 --- /dev/null +++ b/backend/tasks/evaluationTask.py @@ -0,0 +1,471 @@ +""" +Task to evaluate models on a YourbBench dataset using LightEval +""" +import os +import sys +import json +import time +import tempfile +import asyncio +import threading +from pathlib import Path +from typing import Optional, List, Dict, Any, Tuple + +from loguru import logger +from huggingface_hub import HfApi, CommitOperationAdd + +from tasks.yourbench_lighteval_task import create_yourbench_task + + +class EvaluationTask: + """ + Task to evaluate models using LightEval on a YourbBench dataset + """ + + def __init__(self, session_uid: str, dataset_name: str): + """ + Initialize the evaluation task + + Args: + session_uid: Session ID for this task + dataset_name: Name of the dataset to evaluate + """ + self.session_uid = session_uid + self.dataset_name = dataset_name + self.logs: List[str] = [] + self.is_completed = False + self.organization = os.getenv("HF_ORGANIZATION", "yourbench") + self.results: Dict[str, Any] = {} + self.output_dir = f"uploaded_files/{session_uid}/lighteval_results" + + # Models to evaluate - can be modified to allow customization + self.models = [ + ("Qwen/Qwen2.5-72B-Instruct", "novita"), + ("Qwen/QwQ-32B", "novita"), + ] + + self._add_log("[INFO] Initializing evaluation task") + self._add_log(f"[INFO] Dataset to evaluate: {self.organization}/{dataset_name}") + self._add_log(f"[INFO] Output directory: {self.output_dir}") + + def _add_log(self, message: str) -> None: + """ + Add a log message to the logs list + + Args: + message: Log message to add + """ + if message not in self.logs: # Avoid duplicates + self.logs.append(message) + # Force copy of the list to avoid reference problems + self.logs = self.logs.copy() + # Record in system logs + logger.info(f"[{self.session_uid}] {message}") + + def get_logs(self) -> List[str]: + """ + Get all logs for this task + + Returns: + List of log messages + """ + return self.logs.copy() # Retourner une copie pour éviter les problèmes de référence + + def is_task_completed(self) -> bool: + """ + Check if the task is completed + + Returns: + True if completed, False otherwise + """ + return self.is_completed + + async def _evaluate_model(self, model_info: Tuple[str, str]) -> Dict[str, Any]: + """ + Evaluate a specific model + + Args: + model_info: Tuple of (model_name, provider) + + Returns: + Dictionary with evaluation results + """ + model_name, provider = model_info + self._add_log(f"[INFO] Starting evaluation for {model_name} with {provider}") + + # Create output directory + os.makedirs(self.output_dir, exist_ok=True) + + # Define full dataset path + dataset_path = f"{self.organization}/{self.dataset_name}" + + # Create temporary file + temp_file_path = tempfile.mktemp(suffix=".py") + self._add_log(f"[INFO] Creating temporary file for {model_name}: {temp_file_path}") + + with open(temp_file_path, 'w') as temp_file: + temp_file.write(f""" +import os +import sys +sys.path.append("{os.getcwd()}") + +from tasks.yourbench_lighteval_task import create_yourbench_task + +# Create yourbench task +yourbench = create_yourbench_task("{dataset_path}", "lighteval") + +# Define TASKS_TABLE needed by lighteval +TASKS_TABLE = [yourbench] +""") + + # Build lighteval command args + cmd_args = [ + "lighteval", + "endpoint", + "inference-providers", + f"model={model_name},provider={provider}", + "custom|yourbench|0|0", + "--custom-tasks", + temp_file_path, + "--max-samples", "5", + "--output-dir", self.output_dir, + "--save-details", + "--no-push-to-hub" + ] + + self._add_log(f"[INFO] Running command for {model_name}: {' '.join(cmd_args)}") + + results = { + "model_name": model_name, + "provider": provider, + "success": False, + "error": None, + "results": None, + "return_code": None + } + + try: + # Prepare environment with needed tokens + env = os.environ.copy() + hf_token = os.getenv("HF_TOKEN") + if hf_token: + env["HF_TOKEN"] = hf_token + env["HUGGING_FACE_HUB_TOKEN"] = hf_token + env["HF_ORGANIZATION"] = self.organization + + # Run the process asynchronously + process = await asyncio.create_subprocess_exec( + *cmd_args, + stdout=asyncio.subprocess.PIPE, + stderr=asyncio.subprocess.PIPE, + env=env + ) + + # Wait for the process to complete + stdout, stderr = await process.communicate() + + # Store return code + exit_code = process.returncode + results["return_code"] = exit_code + + # Log output + if stdout: + stdout_lines = stdout.decode().strip().split('\n') + for line in stdout_lines[:5]: # Log only first 5 lines + self._add_log(f"[INFO] {model_name} - {line}") + + # Log errors if any + if stderr and exit_code != 0: + stderr_lines = stderr.decode().strip().split('\n') + for line in stderr_lines[:5]: # Log only first 5 lines + self._add_log(f"[ERROR] {model_name} - {line}") + + # Find any JSON result files - LightEval organizes by model name in different ways + result_files = [] + results_dir = Path(self.output_dir) / "results" + if results_dir.exists(): + # Parcourir récursivement tous les répertoires pour trouver des fichiers JSON + for json_file in results_dir.glob("**/*.json"): + # Check if the filename or path contains parts of the model name + model_parts = [ + model_name, # Full name + model_name.replace('/', '_'), # Name with / replaced by _ + model_name.split('/')[-1] # Just the model name without the organization + ] + + if any(part in str(json_file) for part in model_parts): + result_files.append(json_file) + + # Traiter les fichiers de résultats trouvés + if result_files: + # Prendre le fichier le plus récent + result_files.sort(key=lambda x: x.stat().st_mtime, reverse=True) + latest_result = result_files[0] + self._add_log(f"[INFO] {model_name} - Found result file: {latest_result}") + + try: + with open(latest_result, 'r') as f: + test_results = json.load(f) + + # Vérifier si les résultats contiennent les informations essentielles + if (test_results and + isinstance(test_results, dict) and + "results" in test_results and + "all" in test_results["results"]): + + # Enregistrer les résultats + results["results"] = test_results + results["success"] = True + + # Afficher la précision + accuracy = test_results["results"]["all"]["accuracy"] + accuracy_stderr = test_results["results"]["all"]["accuracy_stderr"] + self._add_log(f"[SUCCESS] {model_name} - Accuracy: {accuracy:.4f} ± {accuracy_stderr:.4f}") + else: + results["error"] = "Incomplete or unexpected result format" + self._add_log(f"[WARNING] {model_name} - Unexpected result format") + + except (json.JSONDecodeError, KeyError) as e: + results["error"] = f"Error reading results: {str(e)}" + self._add_log(f"[ERROR] {model_name} - {results['error']}") + + # Si aucun résultat trouvé + if not results["success"]: + if exit_code == 0: + results["error"] = "Execution completed without error but no results found" + self._add_log(f"[WARNING] {model_name} - {results['error']}") + else: + results["error"] = f"Execution error (code: {exit_code})" + self._add_log(f"[ERROR] {model_name} - {results['error']}") + + except Exception as e: + results["error"] = f"Exception: {str(e)}" + self._add_log(f"[ERROR] Exception during evaluation of {model_name}: {str(e)}") + finally: + # Delete temporary file + try: + os.unlink(temp_file_path) + except: + pass + + return results + + async def _run_evaluations(self) -> List[Dict[str, Any]]: + """ + Run evaluations for all models + + Returns: + List of evaluation results + """ + self._add_log(f"[INFO] Starting evaluations for {len(self.models)} models") + + # Create tasks for each model + tasks = [self._evaluate_model(model) for model in self.models] + + # Run all tasks concurrently and gather results + model_results = await asyncio.gather(*tasks, return_exceptions=True) + + # Process results + results = [] + for i, result in enumerate(model_results): + if isinstance(result, Exception): + # Handle exception + model_name, provider = self.models[i] + self._add_log(f"[ERROR] Evaluation failed for {model_name}: {str(result)}") + results.append({ + "model_name": model_name, + "provider": provider, + "success": False, + "error": str(result), + "results": None, + "return_code": None + }) + else: + # Valid result + results.append(result) + + return results + + def _format_comparison_results(self, results: List[Dict[str, Any]]) -> Dict[str, Any]: + """ + Format results for easy comparison between models + + Args: + results: List of evaluation results + + Returns: + Dictionary with formatted comparison results + """ + comparison = { + "metadata": { + "timestamp": time.strftime("%Y-%m-%d %H:%M:%S"), + "dataset": f"{self.organization}/{self.dataset_name}", + "total_models_tested": len(results), + "successful_tests": len([r for r in results if r["success"]]) + }, + "models_comparison": [] + } + + # Liste des modèles réussis et des modèles échoués + successful_models = [r for r in results if r["success"]] + failed_models = [r for r in results if not r["success"]] + + # Trier les modèles réussis par précision (du plus précis au moins précis) + if successful_models: + sorted_successful = sorted( + successful_models, + key=lambda x: x["results"]["results"]["all"]["accuracy"], + reverse=True # Du plus grand au plus petit + ) + else: + sorted_successful = [] + + # Trier les modèles échoués par nom + sorted_failed = sorted(failed_models, key=lambda x: x["model_name"]) + + # Concaténer: d'abord les réussites, puis les échecs + sorted_results = sorted_successful + sorted_failed + + # Créer l'entrée pour chaque modèle + for result in sorted_results: + model_result = { + "model_name": result["model_name"], + "provider": result["provider"], + "success": result["success"] + } + + if result["success"]: + # Ajouter les métriques de précision et temps d'exécution + model_result.update({ + "accuracy": result["results"]["results"]["all"]["accuracy"], + "accuracy_stderr": result["results"]["results"]["all"]["accuracy_stderr"], + "evaluation_time": float(result["results"]["config_general"]["total_evaluation_time_secondes"]) + }) + else: + # Ajouter l'erreur + model_result["error"] = result.get("error", "Unknown reason") + + comparison["models_comparison"].append(model_result) + + return comparison + + async def _upload_results_to_dataset(self, comparison_results: Dict[str, Any]) -> bool: + """ + Upload evaluation results to the HuggingFace dataset + + Args: + comparison_results: The formatted comparison results + + Returns: + bool: True if upload succeeded, False otherwise + """ + try: + # Create a timestamp for the results file + timestamp = time.strftime("%Y%m%d_%H%M%S") + result_filename = f"lighteval_results.json" + + # Create temporary file for upload + temp_file_path = tempfile.mktemp(suffix=".json") + with open(temp_file_path, 'w') as f: + json.dump(comparison_results, f, indent=2) + + # Initialize HF API + hf_token = os.getenv("HF_TOKEN") + if not hf_token: + self._add_log("[ERROR] HF_TOKEN not found, cannot upload results to dataset") + return False + + api = HfApi(token=hf_token) + dataset_id = f"{self.organization}/{self.dataset_name}" + + # Prepare the file operation + operation = CommitOperationAdd( + path_in_repo=f"lighteval_results/{result_filename}", + path_or_fileobj=temp_file_path + ) + + # Upload the file + self._add_log(f"[INFO] Uploading results to dataset {dataset_id}") + api.create_commit( + repo_id=dataset_id, + repo_type="dataset", + operations=[operation], + commit_message=f"Add evaluation results from {timestamp}" + ) + + # Cleanup temporary file + os.unlink(temp_file_path) + + self._add_log(f"[SUCCESS] Results uploaded to dataset {dataset_id} at lighteval_results/{result_filename}") + return True + + except Exception as e: + self._add_log(f"[ERROR] Failed to upload results to dataset: {str(e)}") + return False + + async def _process_evaluation_results(self, results: List[Dict[str, Any]]) -> None: + """ + Process evaluation results, create summaries and save files + + Args: + results: List of evaluation results + """ + if results: + try: + # Save detailed results + detailed_output_file = f"{self.output_dir}/detailed_results.json" + os.makedirs(os.path.dirname(detailed_output_file), exist_ok=True) + with open(detailed_output_file, 'w') as f: + json.dump(results, f, indent=2) + self._add_log(f"[INFO] Detailed results saved in {detailed_output_file}") + + # Generate and save comparison results + comparison = self._format_comparison_results(results) + comparison_file = f"{self.output_dir}/models_comparison.json" + with open(comparison_file, 'w') as f: + json.dump(comparison, f, indent=2) + self._add_log(f"[INFO] Models comparison saved in {comparison_file}") + + # Upload results to the dataset + await self._upload_results_to_dataset(comparison) + + # Store results for later access + self.results = comparison + self._add_log("[SUCCESS] Evaluation completed") + except Exception as e: + self._add_log(f"[ERROR] Error during evaluation execution: {str(e)}") + finally: + self.is_completed = True + + def _async_run(self) -> None: + """ + Run the evaluation asynchronously + """ + async def run_async(): + try: + # Run evaluations + results = await self._run_evaluations() + + # Process evaluation results + await self._process_evaluation_results(results) + except Exception as e: + self._add_log(f"[ERROR] Error during evaluation execution: {str(e)}") + finally: + self.is_completed = True + + # Create and run the asyncio event loop + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + loop.run_until_complete(run_async()) + loop.close() + + def run(self) -> None: + """ + Run the evaluation task in a separate thread + """ + self._add_log("[INFO] Starting evaluation") + + # Run in a separate thread to not block the main thread + thread = threading.Thread(target=self._async_run) + thread.daemon = True + thread.start() diff --git a/backend/tasks/yourbench_lighteval_task.py b/backend/tasks/yourbench_lighteval_task.py new file mode 100644 index 0000000000000000000000000000000000000000..3aa96d7d9a9b0bb580e7c9b982aece4e0b6f7263 --- /dev/null +++ b/backend/tasks/yourbench_lighteval_task.py @@ -0,0 +1,273 @@ +#!/usr/bin/env python3 +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# 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. + +import logging +import re + +import numpy as np +from aenum import extend_enum + +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.metrics_sample import JudgeLLM +from lighteval.metrics.utils.metric_utils import ( + CorpusLevelMetricGrouping, + MetricCategory, + MetricUseCase, +) +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.requests import Doc + + +logger = logging.getLogger(__name__) + +JUDGE_ANSWER_SYSTEM_PROMPT = """You will be provided with the summary of a document, a piece of text, a question generated from that text, and the correct or "gold" answer to the question. Additionally, you will receive a model answer. Your task is to determine wether the model answer is correct using the provided "gold" answer as a reference. +# Steps +1. **Document Understanding**: + - Analyze the provided document summary to grasp the context and main themes. +2. **Chunk Understanding**: + - Examine the provided text (chunk) to understand its content. +3. **Question Understanding**: + - Interpret the given question to fully comprehend what is being asked. +4. **Ground Truth Answer Understanding**: + - Understand the provided ground truth answer, identifying its key points. +6. **Answer Understanding**: + - Examine the Model Answer, identifying key points and assessing accuracy and factuality. +7. **Final Answer**: + - 0 or 1 (0 if the model answer is incorrect, 1 if it is correct). +# Output Format +- Provide your final evaluation of whether the answer is correct within `` XML tags. +- Include a detailed analysis for each part within the designated XML tags: ``, ``, ``, ``, ``, and ``. +# Examples +**Input**: +```xml + +[Summary] + + +[Text] + + +[Question] + + +[Gold Answer] + + +[Model Answer] + +``` +**Output**: +```xml + +Understanding of the summary including key themes + + +Analysis of the piece of text + + +Comprehension of the question being asked + + +Key points from the gold answer + + +Key points and accuracy of Answer A + + +1 or 0 (1 if the model answer is correct, 0 if it is incorrect) + +``` +# Notes +- Always focus on key points and factual correctness as per the ground truth. +- Avoid any biases and rely solely on the evidence presented. +- Enclose all evaluations and analyses in the specified XML tags for clarity and structure.""" + + +JUDGE_ANSWER_USER_PROMPT = """ +{summary} + + +{chunk} + + +{question} + + +{oracle_answer} + + +{model_answer} +""" + + +def get_judge_prompt(question: str, answer: str, gold: str, **kwargs): + chunk = kwargs.get("chunks", "") + summary = kwargs.get("documents", "") + + return [ + {"role": "system", "content": JUDGE_ANSWER_SYSTEM_PROMPT}, + { + "role": "user", + "content": JUDGE_ANSWER_USER_PROMPT.format( + summary=summary, chunk=chunk, question=question, oracle_answer=gold, model_answer=answer + ), + }, + ] + + +def process_judge_response_yourbench(response): + # extract the final answer using regex from the response xml + try: + # Essayer d'abord le format XML + match = re.search(r"(.*?)", response, re.DOTALL) + if match: + answer_text = match.group(1).strip() + # Convertir différents formats possibles en 0 ou 1 + if answer_text in ["1", "correct", "true", "yes", "True", "TRUE"]: + return 1 + elif answer_text in ["0", "incorrect", "false", "no", "False", "FALSE"]: + return 0 + # Essayer de convertir directement en nombre + try: + value = int(answer_text) + return 1 if value > 0 else 0 + except ValueError: + pass + + # Rechercher des mots-clés dans la réponse + if re.search(r"\b(correct|vrai|true|yes)\b", response, re.IGNORECASE): + return 1 + if re.search(r"\b(incorrect|faux|false|no)\b", response, re.IGNORECASE): + return 0 + + logger.warning(f"Réponse du juge non reconnue, retournant 0 par défaut: {response[:100]}...") + except Exception as e: + logger.error(f"Error processing judge response: {e}") + return 0 + + +class JudgeLLMYourBench(JudgeLLM): + def __init__(self): + super().__init__( + judge_model_name="gpt-4o-2024-08-06", + template=get_judge_prompt, + process_judge_response=process_judge_response_yourbench, + judge_backend="openai", + short_judge_name="yourbench_judge", + ) + + def compute(self, sample_ids: list[str], responses: list, formatted_docs: list[Doc]) -> list[dict[str, float]]: + # If we are evaluating a multiturn task, we need to have specific field in the formatted doc + questions = [formatted_doc.specific["question"] for formatted_doc in formatted_docs] + golds = [formatted_doc.get_golds()[0] for formatted_doc in formatted_docs] + predictions = [response[0].result[0] for response in responses] + options = [None] * len(questions) + chunks = [formatted_doc.specific["chunks"][0] for formatted_doc in formatted_docs] + documents = [formatted_doc.specific["document"] for formatted_doc in formatted_docs] + + score, _, _ = self.judge.evaluate_answer_batch( + questions, predictions, options, golds, chunks=chunks, documents=documents + ) + + metrics = [] + for i in range(len(sample_ids)): + metrics.append( + { + "accuracy": score[i], + } + ) + + return metrics + + +ZEROSHOT_QA_USER_PROMPT = """Answer the following question: + +{question} + +Enclose your full answer in XML tags. For example: + +[your answer here] +""" + + +def yourbench_prompt(line, task_name: str = ""): + return Doc( + task_name=task_name, + query=ZEROSHOT_QA_USER_PROMPT.format(question=line["question"]), + choices=[line["ground_truth_answer"]], + gold_index=0, + specific={ + "question_category": line["question_category"], + "kind": line["kind"], + "estimated_difficulty": line["estimated_difficulty"], + "document_id": line["document_id"], + "question_generating_model": line["question_generating_model"], + "chunks": line["chunks"], + "question": line["question"], + "document": line["document"], + }, + ) + + +def create_yourbench_task(hf_dataset_name, subset="lighteval_single_shot_questions"): + """ + Crée une tâche personnalisée yourbench pour lighteval. + + Args: + hf_dataset_name: Nom du dataset sur le Hub HF (format: "org/nom") + subset: Nom du sous-ensemble à utiliser + + Returns: + LightevalTaskConfig: Configuration de la tâche yourbench + """ + yourbench_metrics = CorpusLevelMetricGrouping( + metric_name=["accuracy"], + higher_is_better={"accuracy": True}, + category=MetricCategory.LLM_AS_JUDGE, + use_case=MetricUseCase.ACCURACY, + sample_level_fn=JudgeLLMYourBench().compute, + corpus_level_fn={"accuracy": np.mean}, + ) + + try: + extend_enum(Metrics, "accuracy", yourbench_metrics) + except Exception: + # L'enum a peut-être déjà été ajouté, on ignore l'erreur + pass + + return LightevalTaskConfig( + name="yourbench", + suite=["custom"], + prompt_function=yourbench_prompt, + hf_repo=hf_dataset_name, + hf_subset=subset, + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=8192, + metric=[Metrics.accuracy], + stop_sequence=[], + trust_dataset=True, + version=0, + ) \ No newline at end of file diff --git a/backend/tests/test_evaluation.py b/backend/tests/test_evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..d836631c4f277fa3ffe46dbf257c22ae28a3b8d2 --- /dev/null +++ b/backend/tests/test_evaluation.py @@ -0,0 +1,165 @@ +#!/usr/bin/env python3 +""" +Script to test the evaluation task in standalone mode +""" +import os +import sys +import uuid +import json +import time +import argparse +from dotenv import load_dotenv +from pathlib import Path +import traceback + +# Ensure the environment is properly configured +load_dotenv() + +# Add the current directory to the path to import modules +sys.path.append(os.getcwd()) +from tasks.evaluationTask import EvaluationTask + + +def setup_environment(): + """ + Configure the environment for testing + """ + # Check if the HF token is defined + hf_token = os.getenv("HF_TOKEN") + if not hf_token: + print("⚠️ The HF_TOKEN is not defined in the environment or .env file") + print(" Please define this variable before continuing.") + sys.exit(1) + + # Set the default organization if not defined + if not os.getenv("HF_ORGANIZATION"): + os.environ["HF_ORGANIZATION"] = "yourbench" + print("ℹ️ The HF_ORGANIZATION variable is not defined, using 'yourbench' as default") + + +def run_standalone_evaluation(dataset_name, models=None, max_wait_time=3600): + """ + Run the evaluation task in standalone mode + + Args: + dataset_name: Name of the dataset to evaluate + models: List of models to evaluate (optional) + max_wait_time: Maximum waiting time in seconds + """ + # Generate a unique session ID + session_uid = str(uuid.uuid4()) + print(f"🔧 Session ID: {session_uid}") + + # Create the evaluation task instance + evaluation_task = EvaluationTask(session_uid, dataset_name) + + # If specific models are provided, use them + if models: + evaluation_task.models = models + print(f"🤖 Using custom models: {models}") + + # Display dataset information + organization = os.getenv("HF_ORGANIZATION", "yourbench") + print(f"📊 Evaluating dataset: {organization}/{dataset_name}") + print(f"💾 Results saved in: {evaluation_task.output_dir}") + + # Start the evaluation task + print("🚀 Starting evaluation...") + evaluation_task.run() + + # Wait for the task to complete while displaying logs + start_time = time.time() + last_log_count = 0 + + while not evaluation_task.is_task_completed(): + current_logs = evaluation_task.get_logs() + + # Display only new logs + if len(current_logs) > last_log_count: + for log in current_logs[last_log_count:]: + print(f" {log}") + last_log_count = len(current_logs) + + # Check if the maximum time is reached + elapsed_time = time.time() - start_time + if elapsed_time > max_wait_time: + print("⚠️ Maximum waiting time reached, forced stop") + break + + time.sleep(1) + + # Check if results are available + results_file = Path(f"{evaluation_task.output_dir}/models_comparison.json") + if results_file.exists(): + try: + with open(results_file, 'r') as f: + results = json.load(f) + + print("\n📈 Evaluation Results:") + print(f" Dataset: {results['metadata']['dataset']}") + print(f" Models tested: {results['metadata']['total_models_tested']}") + print(f" Successful tests: {results['metadata']['successful_tests']}") + print(f" Timestamp: {results['metadata']['timestamp']}") + + if results['metadata']['successful_tests'] > 0: + print("\n📊 Model ranking by accuracy:") + successful_models = [m for m in results['models_comparison'] if m['success']] + for i, model in enumerate(successful_models): + print(f" {i+1}. ✅ {model['model_name']} ({model['provider']})") + print(f" Accuracy: {model['accuracy']:.4f} ± {model['accuracy_stderr']:.4f}") + print(f" Evaluation time: {model['evaluation_time']:.2f}s") + + failed_models = [m for m in results['models_comparison'] if not m['success']] + if failed_models: + print("\n❌ Unevaluated models:") + for i, model in enumerate(failed_models): + print(f" {i+1}. {model['model_name']} ({model['provider']})") + error_msg = model.get('error', 'Unknown reason') + print(f" Reason: {error_msg}") + + # Check detailed results files + detailed_file = Path(f"{evaluation_task.output_dir}/detailed_results.json") + if detailed_file.exists(): + print(f"\n📄 Detailed results available in: {detailed_file}") + + # Check raw files + raw_results = list(Path(f"{evaluation_task.output_dir}/results").glob("**/*.json")) + if raw_results: + print(f"\n📁 {len(raw_results)} raw result files available in: {evaluation_task.output_dir}/results") + + print(f"\n✅ Evaluation completed!") + except Exception as e: + print(f"❌ Error reading results: {str(e)}") + print(f" Details: {traceback.format_exc()}") + else: + print(f"❌ No evaluation results found in {results_file}") + + +if __name__ == "__main__": + # Configure the argument parser + parser = argparse.ArgumentParser(description="Test the evaluation task in standalone mode") + parser.add_argument("dataset_name", type=str, help="Name of the dataset to evaluate (without the organization)") + parser.add_argument("--model", action="append", dest="models", + help="Model to evaluate in the format 'name/model,provider'. Can be used multiple times.") + parser.add_argument("--timeout", type=int, default=3600, + help="Maximum waiting time in seconds (default: 3600)") + + args = parser.parse_args() + + # Configure the environment + setup_environment() + + # Transform models into tuples if specified + models_to_evaluate = None + if args.models: + models_to_evaluate = [] + for model_spec in args.models: + try: + model_name, provider = model_spec.split(",") + models_to_evaluate.append((model_name, provider)) + except ValueError: + print(f"⚠️ Invalid model format: {model_spec}. Use 'name/model,provider'") + sys.exit(1) + + # Run the evaluation + run_standalone_evaluation(args.dataset_name, models_to_evaluate, args.timeout) diff --git a/backend/tests/test_hf_upload.py b/backend/tests/test_hf_upload.py new file mode 100644 index 0000000000000000000000000000000000000000..1e5b87a62c9188d843dc4a926e4b72c4e66c9c73 --- /dev/null +++ b/backend/tests/test_hf_upload.py @@ -0,0 +1,78 @@ +""" +Script de test pour vérifier l'upload vers Hugging Face Hub +Ce script crée un dataset simple et tente de l'uploader vers le Hub +en utilisant le token et l'organisation définis dans les variables d'environnement. +""" +import os +import sys +import datasets +from huggingface_hub import HfApi, login +from datasets import Dataset +from dotenv import load_dotenv +from pathlib import Path + +def test_hf_upload(): + # Charger les variables d'environnement depuis le fichier .env + dotenv_path = Path('.env') + load_dotenv(dotenv_path=dotenv_path) + + # Récupérer le token et l'organisation des variables d'environnement + hf_token = os.getenv("HF_TOKEN") + org_name = os.getenv("HF_ORGANIZATION", "yourbench") + + if not hf_token: + print("Erreur: La variable HF_TOKEN n'est pas définie dans le fichier .env.") + sys.exit(1) + + dataset_name = "test_dataset_upload" + repo_id = f"{org_name}/{dataset_name}" + + print(f"Tentative d'upload vers {repo_id} avec le token {hf_token[:5]}... (token tronqué pour la sécurité)") + + try: + # Se connecter à l'API Hugging Face + print("Connexion à l'API Hugging Face...") + login(token=hf_token) + api = HfApi(token=hf_token) + + # Créer un dataset simple + print("Création d'un dataset de test...") + data = { + "text": ["Ceci est un test", "Un autre exemple", "Troisième exemple"], + "label": [1, 0, 1] + } + dataset = Dataset.from_dict(data) + + # Vérifier si le repo existe déjà et le supprimer si nécessaire + try: + api.delete_repo(repo_id=repo_id, repo_type="dataset") + print(f"Repo existant {repo_id} supprimé.") + except Exception: + print(f"Le repo {repo_id} n'existait pas encore.") + + # Uploader le dataset + print(f"Upload du dataset vers {repo_id}...") + dataset.push_to_hub( + repo_id=repo_id, + token=hf_token, + private=True, + commit_message="Test d'upload de dataset" + ) + + print(f"Succès! Dataset uploadé vers https://huggingface.co/datasets/{repo_id}") + return True + + except Exception as e: + print(f"Erreur lors de l'upload: {str(e)}") + print("\nTraceback complet:") + import traceback + traceback.print_exc() + return False + +if __name__ == "__main__": + print("=== Test d'upload vers Hugging Face Hub ===") + success = test_hf_upload() + if success: + print("\n✅ Le test a réussi! L'upload fonctionne correctement.") + else: + print("\n❌ Le test a échoué. Vérifiez les erreurs ci-dessus.") \ No newline at end of file diff --git a/backend/tests/test_inference.py b/backend/tests/test_inference.py new file mode 100644 index 0000000000000000000000000000000000000000..8f92d91f8f649d2004aadbf9ab015e5f0f24dd14 --- /dev/null +++ b/backend/tests/test_inference.py @@ -0,0 +1,84 @@ +import time +import signal +from huggingface_hub import InferenceClient + +# Configuration - Modèles et leurs providers +MODELS = [ + ("Qwen/Qwen2.5-72B-Instruct", "sambanova"), + ("meta-llama/Llama-3.3-70B-Instruct", "sambanova"), + ("deepseek-ai/DeepSeek-R1-Distill-Llama-70B", "sambanova"), + ("Qwen/QwQ-32B", "novita"), + # ("mistralai/Mistral-Small-24B-Instruct-2501", "novita") +] +QUESTION = "What is the capital of France?" +TIMEOUT = 10 # secondes + + +class TimeoutException(Exception): + pass + + +def timeout_handler(signum, frame): + raise TimeoutException("Timeout") + + +def test_model(model, provider): + client = InferenceClient(provider=provider) + + # Configure le timeout + signal.signal(signal.SIGALRM, timeout_handler) + signal.alarm(TIMEOUT) + + start_time = time.time() + try: + response = client.chat_completion( + model=model, + messages=[{"role": "user", "content": QUESTION}] + ) + result = response.choices[0].message.content + success = True + except TimeoutException: + result = f"TIMEOUT ({TIMEOUT}s)" + success = False + except Exception as e: + result = str(e) + success = False + finally: + # Désactive l'alarme + signal.alarm(0) + + execution_time = time.time() - start_time + + status = "✅" if success else "❌" + print(f"{status} {model} ({provider}) - Temps: {execution_time:.2f}s") + if success: + print(f" Réponse: {result[:80]}..." if len(result) > 80 else f" Réponse: {result}") + else: + print(f" Erreur: {result}") + + return success, execution_time, result + + +def main(): + print(f"\nTest de {len(MODELS)} modèles avec leurs providers spécifiques") + print(f"Question: {QUESTION}") + print(f"Timeout: {TIMEOUT}s\n") + + results = [] + for model, provider in MODELS: + success, time_taken, response = test_model(model, provider) + results.append({ + "model": model, + "provider": provider, + "success": success, + "time": time_taken + }) + + print("\n=== RÉSUMÉ ===") + for result in results: + status = "✅" if result["success"] else "❌" + print(f"{status} {result['model']} ({result['provider']}): {result['time']:.2f}s") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/backend/tests/test_lighteval.py b/backend/tests/test_lighteval.py new file mode 100644 index 0000000000000000000000000000000000000000..a53ab269c9d19e7222985b2aac33ccadd93f857c --- /dev/null +++ b/backend/tests/test_lighteval.py @@ -0,0 +1,151 @@ +#!/usr/bin/env python3 +""" +Script minimal pour tester directement lighteval avec la tâche yourbench +""" +import os +import sys +import subprocess +import json +import time +from pathlib import Path +import logging + +# Assurez-vous que l'environnement est correctement configuré +from dotenv import load_dotenv +load_dotenv() + +# Importer le module de définition de tâche yourbench +sys.path.append(os.getcwd()) +from tasks.yourbench_lighteval_task import create_yourbench_task + +def run_lighteval_test(): + """ + Exécuter un test minimal avec lighteval + """ + # Parameters + dataset_name = "yourbench_a" + organization = "yourbench" + model_name = "Qwen/Qwen2.5-72B-Instruct" + provider = "novita" + output_dir = f"uploaded_files/test_{provider}/lighteval_results" + + # Créer le répertoire de sortie + os.makedirs(output_dir, exist_ok=True) + + # Définir le chemin d'accès complet au dataset + dataset_path = f"{organization}/{dataset_name}" + print(f"Dataset à évaluer: {dataset_path}") + + # Créer un fichier temporaire + import tempfile + temp_file_path = tempfile.mktemp(suffix=".py") + print(f"Création du fichier temporaire: {temp_file_path}") + + with open(temp_file_path, 'w') as temp_file: + # Écrire le contenu du fichier + temp_file.write(f""" +import os +import sys +import logging +sys.path.append("{os.getcwd()}") + +from tasks.yourbench_lighteval_task import create_yourbench_task + +# Configurer le logging +logging.basicConfig(level=logging.INFO) + +# Créer la tâche yourbench +yourbench = create_yourbench_task("{dataset_path}", "lighteval") + +# Définir la variable TASKS_TABLE dont lighteval a besoin +TASKS_TABLE = [yourbench] +""") + + # Construire la commande lighteval + cmd = [ + "lighteval", + "endpoint", + "inference-providers", + f"model={model_name},provider={provider}", + "custom|yourbench|0|0", + "--custom-tasks", + temp_file_path, + "--max-samples", "5", # Seulement 1 échantillon + "--output-dir", output_dir, + "--save-details", + "--no-push-to-hub" # Pas de push pour gagner du temps + ] + + # Afficher la commande + print(f"Exécution de la commande: {' '.join(cmd)}") + print(f"Heure de début: {time.strftime('%H:%M:%S')}") + + # Exécuter la commande + try: + # Exécuter avec capture des sorties + result = subprocess.run(cmd, capture_output=True, text=True) + + # Afficher les résultats + print(f"Code de retour: {result.returncode}") + print("--- SORTIE STANDARD ---") + print(result.stdout) + print("--- ERREUR STANDARD ---") + print(result.stderr) + + # Vérifier si des résultats ont été générés + results_dir = Path(output_dir) / "results" + if results_dir.exists(): + print(f"Dossier de résultats créé: {results_dir}") + # Lister les fichiers de résultats + result_files = list(results_dir.glob("**/*.json")) + if result_files: + print(f"Fichiers de résultats trouvés: {result_files}") + # Trier les fichiers par date de modification pour prendre le plus récent + result_files.sort(key=lambda x: x.stat().st_mtime, reverse=True) + latest_result = result_files[0] + print(f"Fichier de résultats le plus récent: {latest_result}") + + # Lire le fichier de résultats + with open(latest_result, 'r') as f: + results = json.load(f) + print("Contenu du fichier de résultats:") + print(json.dumps(results, indent=2)) + + # Analyse des résultats + print("\n==== ANALYSE DES RÉSULTATS ====") + if "results" in results: + for task_name, task_results in results["results"].items(): + print(f"Tâche: {task_name}") + for metric_name, metric_value in task_results.items(): + print(f" {metric_name}: {metric_value}") + else: + print("Aucun résultat trouvé dans le fichier JSON") + + # Vérifier les détails + details_dir = Path(output_dir) / "details" + if details_dir.exists(): + print(f"\nDossier de détails trouvé: {details_dir}") + model_details_dirs = list(details_dir.glob("**/*")) + if model_details_dirs: + print(f"Dossiers de détails par modèle: {model_details_dirs}") + else: + print("Aucun fichier de résultats trouvé.") + else: + print(f"Aucun dossier de résultats créé.") + + except subprocess.CalledProcessError as e: + print(f"Erreur lors de l'exécution de la commande: {e}") + except Exception as e: + print(f"Exception: {e}") + finally: + # Supprimer le fichier temporaire + try: + os.unlink(temp_file_path) + print(f"Fichier temporaire supprimé: {temp_file_path}") + except: + pass + + print(f"Heure de fin: {time.strftime('%H:%M:%S')}") + +if __name__ == "__main__": + run_lighteval_test() \ No newline at end of file diff --git a/backend/tests/test_openai.py b/backend/tests/test_openai.py new file mode 100644 index 0000000000000000000000000000000000000000..ff79ad5dd2560f236a70997978c6e7752216fa0b --- /dev/null +++ b/backend/tests/test_openai.py @@ -0,0 +1,31 @@ +import os +from openai import OpenAI +from dotenv import load_dotenv + +# Load environment variables +load_dotenv() + +def test_openai_connection(): + try: + # Initialize OpenAI client + client = OpenAI(api_key=os.getenv('OPENAI_API_KEY')) + + # Make a simple request + response = client.chat.completions.create( + model="gpt-3.5-turbo", + messages=[ + {"role": "user", "content": "Say 'Hello World'"} + ] + ) + + print("✅ OpenAI API connection successful!") + print(f"Response: {response.choices[0].message.content}") + return True + + except Exception as e: + print("❌ OpenAI API connection failed!") + print(f"Error: {str(e)}") + return False + +if __name__ == "__main__": + test_openai_connection() \ No newline at end of file diff --git a/backend/tests/test_parallel_lighteval.py b/backend/tests/test_parallel_lighteval.py new file mode 100644 index 0000000000000000000000000000000000000000..2c057f0aef76d4089bf5100603ff4b2dd5b44358 --- /dev/null +++ b/backend/tests/test_parallel_lighteval.py @@ -0,0 +1,278 @@ +#!/usr/bin/env python3 +""" +Script to run lighteval tests in parallel for multiple models +""" +import os +import sys +import json +import time +import tempfile +import asyncio +from pathlib import Path +from typing import Tuple, List, Dict, Any + +# Ensure environment is properly configured +from dotenv import load_dotenv +load_dotenv() + +# Import yourbench task module +sys.path.append(os.getcwd()) +from tasks.yourbench_lighteval_task import create_yourbench_task + +# Define models to test +INIT_MODELS = [ + # 70B + ("Qwen/Qwen2.5-72B-Instruct", "novita"), + ("meta-llama/Llama-3.3-70B-Instruct", "novita"), + ("deepseek-ai/DeepSeek-R1-Distill-Llama-70B", "novita"), + # 20 to 30B + ("Qwen/QwQ-32B", "novita"), + # ("mistralai/Mistral-Small-24B-Instruct-2501", "sambanova"), +] + +async def run_lighteval_test_for_model(model_info: Tuple[str, str]) -> Dict[str, Any]: + """ + Run lighteval test for a specific model + """ + model_name, provider = model_info + + # Parameters + dataset_name = "yourbench_a" + organization = "yourbench" + output_dir = f"uploaded_files/test_parallel_{provider}/lighteval_results" + + # Create output directory + os.makedirs(output_dir, exist_ok=True) + + # Define full dataset path + dataset_path = f"{organization}/{dataset_name}" + print(f"Dataset to evaluate for {model_name}: {dataset_path}") + + # Create temporary file + temp_file_path = tempfile.mktemp(suffix=".py") + print(f"Creating temporary file for {model_name}: {temp_file_path}") + + with open(temp_file_path, 'w') as temp_file: + temp_file.write(f""" +import os +import sys +sys.path.append("{os.getcwd()}") + +from tasks.yourbench_lighteval_task import create_yourbench_task + +# Create yourbench task +yourbench = create_yourbench_task("{dataset_path}", "lighteval") + +# Define TASKS_TABLE needed by lighteval +TASKS_TABLE = [yourbench] +""") + + # Build lighteval command args + cmd_args = [ + "lighteval", + "endpoint", + "inference-providers", + f"model={model_name},provider={provider}", + "custom|yourbench|0|0", + "--custom-tasks", + temp_file_path, + "--max-samples", "5", + "--output-dir", output_dir, + "--save-details", + "--no-push-to-hub" + ] + + print(f"Running command for {model_name}: {' '.join(cmd_args)}") + print(f"Start time for {model_name}: {time.strftime('%H:%M:%S')}") + + results = { + "model_name": model_name, + "provider": provider, + "success": False, + "error": None, + "results": None, + "return_code": None + } + + try: + # Prepare environment with needed tokens + env = os.environ.copy() + hf_token = os.getenv("HF_TOKEN") + if hf_token: + env["HF_TOKEN"] = hf_token + env["HUGGING_FACE_HUB_TOKEN"] = hf_token + env["HF_ORGANIZATION"] = organization + + # Run the process asynchronously + process = await asyncio.create_subprocess_exec( + *cmd_args, + stdout=asyncio.subprocess.PIPE, + stderr=asyncio.subprocess.PIPE, + env=env + ) + + # Wait for the process to complete + stdout, stderr = await process.communicate() + + # Store return code + exit_code = process.returncode + results["return_code"] = exit_code + + # Log some output for debugging + if stdout: + stdout_lines = stdout.decode().strip().split('\n') + if stdout_lines and len(stdout_lines) > 0: + print(f"Output from {model_name}: {stdout_lines[0]}") + + # Check if results were generated + results_dir = Path(output_dir) / "results" + if results_dir.exists(): + result_files = list(results_dir.glob("**/*.json")) + if result_files: + # Read the first results file + with open(result_files[0], 'r') as f: + test_results = json.load(f) + results["results"] = test_results + results["success"] = True + + except asyncio.CancelledError: + results["error"] = "Task cancelled" + print(f"Task cancelled for {model_name}") + except Exception as e: + results["error"] = f"Exception: {str(e)}" + print(f"Error running test for {model_name}: {str(e)}") + finally: + # Delete temporary file + try: + os.unlink(temp_file_path) + except: + pass + + print(f"End time for {model_name}: {time.strftime('%H:%M:%S')}") + return results + +async def run_parallel_tests(models: List[Tuple[str, str]]) -> List[Dict[str, Any]]: + """ + Run tests in parallel for multiple models using asyncio + """ + print(f"Starting parallel tests for {len(models)} models") + + # Create tasks for each model + tasks = [run_lighteval_test_for_model(model) for model in models] + + # Run all tasks concurrently and gather results + model_results = await asyncio.gather(*tasks, return_exceptions=True) + + # Process results + results = [] + for i, result in enumerate(model_results): + if isinstance(result, Exception): + # Handle exception + model_name, provider = models[i] + print(f"Test failed for {model_name}: {str(result)}") + results.append({ + "model_name": model_name, + "provider": provider, + "success": False, + "error": str(result), + "results": None, + "return_code": None + }) + else: + # Valid result + results.append(result) + print(f"Test completed for {result['model_name']}") + + return results + +def format_comparison_results(results: List[Dict[str, Any]]) -> Dict[str, Any]: + """ + Format results for easy comparison between models + """ + comparison = { + "metadata": { + "timestamp": time.strftime("%Y-%m-%d %H:%M:%S"), + "total_models_tested": len(results), + "successful_tests": len([r for r in results if r["success"]]) + }, + "models_comparison": [] + } + + # Sort models by accuracy (if available) or name + sorted_results = sorted( + results, + key=lambda x: ( + x["results"]["results"]["all"]["accuracy"] if x["success"] and x["results"] else -1, + x["model_name"] + ), + reverse=True + ) + + for result in sorted_results: + model_result = { + "model_name": result["model_name"], + "provider": result["provider"], + "success": result["success"] + } + + if result["success"] and result["results"]: + model_result.update({ + "accuracy": result["results"]["results"]["all"]["accuracy"], + "accuracy_stderr": result["results"]["results"]["all"]["accuracy_stderr"], + "evaluation_time": float(result["results"]["config_general"]["total_evaluation_time_secondes"]) + }) + else: + model_result["error"] = result["error"] + + comparison["models_comparison"].append(model_result) + + return comparison + +async def main_async(): + """ + Async main function to run parallel tests + """ + print("Starting parallel lighteval tests") + start_time = time.time() + + # Run tests in parallel + results = await run_parallel_tests(INIT_MODELS) + + # Save detailed results + detailed_output_file = "parallel_test_detailed_results.json" + with open(detailed_output_file, 'w') as f: + json.dump(results, f, indent=2) + + # Generate and save comparison results + comparison = format_comparison_results(results) + comparison_file = "models_comparison.json" + with open(comparison_file, 'w') as f: + json.dump(comparison, f, indent=2) + + # Print summary + print("\nTest Summary:") + for model in comparison["models_comparison"]: + status = "✅" if model["success"] else "❌" + print(f"{status} {model['model_name']} ({model['provider']})") + if not model["success"]: + print(f" Error: {model['error']}") + else: + print(f" Accuracy: {model['accuracy']:.2%} (±{model['accuracy_stderr']:.2%})") + print(f" Evaluation time: {model['evaluation_time']:.2f}s") + + duration = time.time() - start_time + print(f"\nTotal execution time: {duration:.2f} seconds") + print(f"Detailed results saved to: {detailed_output_file}") + print(f"Comparison results saved to: {comparison_file}") + +def main(): + """ + Main function to run parallel tests + """ + # Create event loop and run the async main + loop = asyncio.get_event_loop() + loop.run_until_complete(main_async()) + loop.close() + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/backend/tests/test_provider_parallel_support.py b/backend/tests/test_provider_parallel_support.py new file mode 100644 index 0000000000000000000000000000000000000000..851491758102bc460a465682e3a3d7332ef7af10 --- /dev/null +++ b/backend/tests/test_provider_parallel_support.py @@ -0,0 +1,227 @@ +#!/usr/bin/env python3 +""" +Script pour tester si un fournisseur d'API supporte réellement les requêtes parallèles +""" +import os +import sys +import time +import asyncio +import json +from pathlib import Path +from datetime import datetime + +# Ensure environment is properly configured +from dotenv import load_dotenv +load_dotenv() + +# Définir le modèle et le fournisseur à tester +MODEL_NAME = "Qwen/QwQ-32B" +PROVIDER = "novita" +REQUEST_COUNT = 5 # Nombre de requêtes + +# Liste de questions +PROMPTS = [ + "Explain in detail how parallel computing has transformed modern data processing.", + "Describe the fundamental differences between CPU and GPU architectures.", + "Analyze the key challenges in distributed systems design.", + "Discuss the evolution of natural language processing from rule-based systems to modern transformer architectures.", + "Explain the concept of quantum computing and how it differs from classical computing paradigms." +] + +async def send_request(prompt, request_id=None, show_logs=True): + """Envoie une requête au modèle et mesure le temps d'exécution""" + if show_logs and request_id is not None: + print(f"Démarrage requête {request_id} à {datetime.now().strftime('%H:%M:%S.%f')[:-3]}") + + start_time = time.time() + + cmd_args = [ + "curl", "-s", + "-X", "POST", + f"https://api-inference.huggingface.co/models/{MODEL_NAME}", + "-H", f"Authorization: Bearer {os.environ.get('HF_TOKEN')}", + "-H", "Content-Type: application/json", + "-d", json.dumps({ + "inputs": prompt, + "parameters": { + "provider": PROVIDER, + "max_new_tokens": 20 + } + }) + ] + + process = await asyncio.create_subprocess_exec( + *cmd_args, + stdout=asyncio.subprocess.PIPE, + stderr=asyncio.subprocess.PIPE + ) + + stdout, stderr = await process.communicate() + + end_time = time.time() + duration = end_time - start_time + + response = stdout.decode("utf-8") + stderr_output = stderr.decode("utf-8") + + # Déterminer le succès + is_success = False + try: + response_json = json.loads(response) + is_success = process.returncode == 0 and isinstance(response_json, list) and "generated_text" in response_json[0] + except json.JSONDecodeError: + is_success = process.returncode == 0 and not ("error" in response.lower()) + except Exception: + is_success = process.returncode == 0 + + # Extraire message d'erreur si échec + error_message = None + if not is_success: + try: + if "error" in response.lower(): + try: + response_json = json.loads(response) + if "error" in response_json: + error_message = response_json["error"] + except: + error_message = f"Erreur non-JSON: {response}" + elif stderr_output: + error_message = stderr_output + else: + error_message = f"Réponse: {response}" + except: + error_message = f"Erreur inconnue. Code: {process.returncode}" + + if show_logs and request_id is not None: + print(f"Fin requête {request_id} à {datetime.now().strftime('%H:%M:%S.%f')[:-3]} (durée: {duration:.2f}s)") + if not is_success: + print(f"ERREUR requête {request_id}: {error_message[:100]}..." if error_message and len(error_message) > 100 else error_message) + + return { + "request_id": request_id, + "prompt": prompt, + "start_time": start_time, + "end_time": end_time, + "duration": duration, + "success": is_success, + "response": response, + "error_message": error_message + } + +async def run_parallel_requests(prompts): + """Exécute les requêtes en parallèle""" + print(f"\n=== Test parallèle: {len(prompts)} requêtes pour {MODEL_NAME} ({PROVIDER}) ===") + print(f"Heure de début: {datetime.now().strftime('%H:%M:%S')}") + + # Synchroniser le démarrage des requêtes + start_event = asyncio.Event() + + async def synchronized_request(prompt, req_id): + await start_event.wait() + return await send_request(prompt, req_id) + + # Créer toutes les tâches + tasks = [asyncio.create_task(synchronized_request(prompts[i], i)) for i in range(len(prompts))] + + # Attendre que toutes les tâches soient prêtes + await asyncio.sleep(1) + + # Lancer toutes les requêtes en même temps + parallel_start_time = time.time() + print(f"Démarrage synchronisé à {datetime.now().strftime('%H:%M:%S.%f')[:-3]}") + start_event.set() + + # Attendre que toutes les tâches se terminent + results = await asyncio.gather(*tasks) + parallel_end_time = time.time() + parallel_duration = parallel_end_time - parallel_start_time + + print(f"Test parallèle terminé en {parallel_duration:.2f}s\n") + return results, parallel_duration + +async def run_sequential_requests(prompts): + """Exécute les mêmes requêtes séquentiellement""" + print(f"\n=== Test séquentiel: {len(prompts)} requêtes pour {MODEL_NAME} ({PROVIDER}) ===") + print(f"Heure de début: {datetime.now().strftime('%H:%M:%S')}") + + sequential_start_time = time.time() + results = [] + + for i, prompt in enumerate(prompts): + print(f"Requête séquentielle {i}...") + result = await send_request(prompt, i) + results.append(result) + + sequential_end_time = time.time() + sequential_duration = sequential_end_time - sequential_start_time + + print(f"Test séquentiel terminé en {sequential_duration:.2f}s\n") + return results, sequential_duration + +async def run_tests(): + """Exécute les tests parallèles puis séquentiels et compare les résultats""" + global_start = time.time() + prompts = PROMPTS[:REQUEST_COUNT] # Utiliser le nombre de prompts spécifié + + # 1. Test parallèle + parallel_results, parallel_duration = await run_parallel_requests(prompts) + + # 2. Test séquentiel + sequential_results, sequential_duration = await run_sequential_requests(prompts) + + # 3. Analyser les résultats + global_end = time.time() + total_duration = global_end - global_start + + # Calculer les métriques + parallel_success = sum(1 for r in parallel_results if r["success"]) + sequential_success = sum(1 for r in sequential_results if r["success"]) + + # Calculer le facteur de parallélisme réel (temps séquentiel / temps parallèle) + if parallel_duration > 0: + parallelism_factor = sequential_duration / parallel_duration + else: + parallelism_factor = 0 + + # Pourcentage d'amélioration + improvement_percent = (1 - (parallel_duration / sequential_duration)) * 100 if sequential_duration > 0 else 0 + + # Afficher le résumé + print("\n====== RÉSUMÉ DES TESTS ======") + print(f"Modèle: {MODEL_NAME}, Provider: {PROVIDER}, Requêtes: {len(prompts)}") + print(f"\nDurée test parallèle: {parallel_duration:.2f}s ({parallel_success}/{len(prompts)} réussies)") + print(f"Durée test séquentiel: {sequential_duration:.2f}s ({sequential_success}/{len(prompts)} réussies)") + print(f"Facteur de parallélisme: {parallelism_factor:.2f}x") + print(f"Amélioration: {improvement_percent:.1f}%") + + if parallelism_factor >= len(prompts) * 0.8: + conclusion = "EXCELLENT parallélisme (proche du théorique maximum)" + elif parallelism_factor >= 2: + conclusion = "BON parallélisme (significativement meilleur que séquentiel)" + elif parallelism_factor >= 1.3: + conclusion = "MOYEN parallélisme (légèrement meilleur que séquentiel)" + else: + conclusion = "FAIBLE ou PAS DE parallélisme (pas d'avantage significatif)" + + print(f"\nConclusion: {conclusion}") + + # Enregistrer les résultats + output_file = f"parallel_test_{PROVIDER}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" + with open(output_file, 'w') as f: + json.dump({ + "model": MODEL_NAME, + "provider": PROVIDER, + "request_count": len(prompts), + "parallel_duration": parallel_duration, + "sequential_duration": sequential_duration, + "parallelism_factor": parallelism_factor, + "improvement_percent": improvement_percent, + "conclusion": conclusion, + "parallel_results": parallel_results, + "sequential_results": sequential_results + }, f, indent=2) + + print(f"\nRésultats détaillés sauvegardés dans {output_file}") + +if __name__ == "__main__": + asyncio.run(run_tests()) \ No newline at end of file diff --git a/backend/tests/test_yourbench_results.py b/backend/tests/test_yourbench_results.py new file mode 100644 index 0000000000000000000000000000000000000000..c0fd75e616ab469175bbdd4c13a17161017e6cc1 --- /dev/null +++ b/backend/tests/test_yourbench_results.py @@ -0,0 +1,394 @@ +#!/usr/bin/env python3 +""" +Script pour tester les résultats de Yourbench et vérifier les datasets sur le Hub Hugging Face. +""" + +import os +import sys +import json +import argparse +import requests +import tempfile +from datetime import datetime +from typing import Dict, List, Any, Optional, Tuple + +# Vérifier si les bibliothèques nécessaires sont installées +try: + from dotenv import load_dotenv + from huggingface_hub import HfApi, DatasetInfo, ModelInfo + from loguru import logger + import pandas as pd +except ImportError: + print("Installation des dépendances...") + import subprocess + subprocess.run(["pip", "install", "python-dotenv", "huggingface_hub", "loguru", "pandas", "pyarrow"], check=True) + from dotenv import load_dotenv + from huggingface_hub import HfApi, DatasetInfo, ModelInfo + from loguru import logger + import pandas as pd + +# Charger les variables d'environnement depuis .env +load_dotenv() + +# Configuration de la journalisation +logger.remove() +logger.add(sys.stderr, format="{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {message}") +logger.add("yourbench_tests.log", rotation="10 MB", retention="1 week") + +def configure_argument_parser() -> argparse.ArgumentParser: + """Configure le parser d'arguments.""" + parser = argparse.ArgumentParser(description="Tester les résultats de Yourbench et vérifier les datasets") + parser.add_argument("--dataset", type=str, help="Nom du dataset à vérifier (sans le nom de l'organisation)") + parser.add_argument("--org", type=str, default=os.environ.get("HF_ORGANIZATION", "yourbench"), + help="Organisation Hugging Face (défaut: valeur de HF_ORGANIZATION dans .env ou 'yourbench')") + parser.add_argument("--verbose", "-v", action="store_true", help="Afficher des informations détaillées") + return parser + +class YourbenchTester: + """Classe pour tester les résultats et datasets de Yourbench.""" + + def __init__(self, organization: str, verbose: bool = False): + """Initialise le testeur Yourbench. + + Args: + organization: Nom de l'organisation sur Hugging Face + verbose: Afficher des informations détaillées + """ + self.organization = organization + self.verbose = verbose + self.hf_token = os.environ.get("HF_TOKEN") + + if not self.hf_token: + logger.error("Variable d'environnement HF_TOKEN non trouvée dans le fichier .env") + sys.exit(1) + + self.api = HfApi(token=self.hf_token) + logger.info(f"Initialisation du testeur pour l'organisation: {organization}") + + def test_dataset_exists(self, dataset_name: str) -> Optional[DatasetInfo]: + """Vérifie si un dataset existe sur le Hub. + + Args: + dataset_name: Nom du dataset à vérifier + + Returns: + Informations sur le dataset s'il existe, None sinon + """ + full_dataset_name = f"{self.organization}/{dataset_name}" + logger.info(f"Vérification de l'existence du dataset: {full_dataset_name}") + + try: + dataset_info = self.api.dataset_info(full_dataset_name) + logger.success(f"Dataset {full_dataset_name} trouvé!") + + if self.verbose: + logger.info(f"ID: {dataset_info.id}") + logger.info(f"Dernière modification: {dataset_info.lastModified}") + logger.info(f"SHA: {dataset_info.sha}") + + return dataset_info + + except Exception as e: + logger.error(f"Impossible de trouver le dataset {full_dataset_name}: {str(e)}") + return None + + def analyze_dataset_content(self, dataset_name: str) -> Tuple[bool, Dict[str, Any]]: + """Analyse le contenu d'un dataset. + + Args: + dataset_name: Nom du dataset à analyser + + Returns: + Tuple contenant un booléen indiquant si l'analyse a réussi et un dictionnaire de statistiques + """ + full_dataset_name = f"{self.organization}/{dataset_name}" + logger.info(f"Analyse du contenu du dataset: {full_dataset_name}") + + stats = { + "fichiers": 0, + "taille_totale": 0, + "fichiers_json": 0, + "fichiers_parquet": 0, + "a_questions": False, + "nb_questions": 0, + "structure_parquet": {}, + "types_documents": set() + } + + try: + # Lister les fichiers dans le dataset + files = self.api.list_repo_files(full_dataset_name, repo_type="dataset") + stats["fichiers"] = len(files) + + if self.verbose: + logger.info(f"Fichiers trouvés dans le dataset: {len(files)}") + for file in files[:10]: # Limiter à 10 fichiers pour éviter un affichage trop verbeux + logger.info(f" - {file}") + if len(files) > 10: + logger.info(f" ... et {len(files) - 10} fichiers supplémentaires") + + # Vérifier la présence de fichiers questions + question_files = [f for f in files if "question" in f.lower() and f.endswith(".json")] + stats["fichiers_json"] = len([f for f in files if f.endswith(".json")]) + + # Vérifier les fichiers Parquet qui sont utilisés par Yourbench + parquet_files = [f for f in files if f.endswith(".parquet")] + stats["fichiers_parquet"] = len(parquet_files) + + if parquet_files: + logger.info(f"Fichiers Parquet trouvés: {len(parquet_files)}") + + # Analyser un échantillon de fichiers Parquet + for parquet_file in parquet_files[:3]: # Limiter à 3 fichiers pour l'analyse + category = parquet_file.split('/')[0] if '/' in parquet_file else "unknown" + + logger.info(f"Analyse du fichier Parquet: {parquet_file} (catégorie: {category})") + + try: + # Télécharger le fichier Parquet + temp_file = self.api.hf_hub_download( + repo_id=full_dataset_name, + filename=parquet_file, + repo_type="dataset" + ) + + # Lire le fichier Parquet avec pandas + df = pd.read_parquet(temp_file) + + # Ajouter des statistiques + stats["structure_parquet"][category] = { + "colonnes": list(df.columns), + "nb_lignes": len(df), + "exemple": df.iloc[0].to_dict() if len(df) > 0 else {} + } + + # Vérifier si ce fichier contient des questions + if any(col for col in df.columns if "question" in col.lower()): + stats["a_questions"] = True + question_col = next(col for col in df.columns if "question" in col.lower()) + stats["nb_questions"] = len(df) + + # Récupérer un exemple de question + if len(df) > 0 and question_col in df.columns: + logger.info(f"Exemple de question: {df[question_col].iloc[0][:100]}...") + + # Identifier les types de documents si disponible + if "doc_type" in df.columns and len(df) > 0: + doc_types = df["doc_type"].unique() + stats["types_documents"].update(doc_types) + + except Exception as e: + logger.warning(f"Erreur lors de l'analyse du fichier {parquet_file}: {str(e)}") + + # Convertir le set en liste pour la sérialisation JSON + stats["types_documents"] = list(stats["types_documents"]) + + if question_files: + stats["a_questions"] = True + + # Analyser un fichier de questions pour comprendre sa structure + sample_file = question_files[0] + content = self.api.hf_hub_download( + repo_id=full_dataset_name, + filename=sample_file, + repo_type="dataset" + ) + + with open(content, 'r') as f: + data = json.load(f) + + if isinstance(data, list): + stats["nb_questions"] = len(data) + elif isinstance(data, dict) and "questions" in data: + stats["nb_questions"] = len(data["questions"]) + + logger.success(f"Fichiers de questions trouvés: {len(question_files)}") + logger.info(f"Exemple de fichier analysé: {sample_file}") + logger.info(f"Nombre de questions trouvées: {stats['nb_questions']}") + + return True, stats + + except Exception as e: + logger.error(f"Erreur lors de l'analyse du dataset {full_dataset_name}: {str(e)}") + return False, stats + + def check_evaluation_results(self, dataset_name: str) -> bool: + """Vérifie s'il existe des résultats d'évaluation pour ce dataset. + + Args: + dataset_name: Nom du dataset à vérifier + + Returns: + True si des résultats d'évaluation existent, False sinon + """ + logger.info(f"Recherche de résultats d'évaluation pour le dataset: {dataset_name}") + + try: + # Lister tous les datasets de l'organisation + datasets = self.api.list_datasets(author=self.organization) + + # Chercher les datasets d'évaluation + eval_datasets = [ds for ds in datasets if ds.id.startswith(f"{self.organization}/evaluation-")] + + if self.verbose: + logger.info(f"Datasets d'évaluation trouvés: {len(eval_datasets)}") + for ds in eval_datasets[:5]: + logger.info(f" - {ds.id}") + + # Vérifier si le dataset spécifié est mentionné dans les évaluations + for eval_ds in eval_datasets: + try: + # Télécharger le README pour voir si le dataset est mentionné + readme_path = self.api.hf_hub_download( + repo_id=eval_ds.id, + filename="README.md", + repo_type="dataset" + ) + + with open(readme_path, 'r') as f: + readme_content = f.read() + + if dataset_name in readme_content: + logger.success(f"Résultats d'évaluation trouvés dans: {eval_ds.id}") + return True + except: + continue + + logger.warning(f"Aucun résultat d'évaluation trouvé pour le dataset: {dataset_name}") + return False + + except Exception as e: + logger.error(f"Erreur lors de la recherche de résultats d'évaluation: {str(e)}") + return False + + def check_model_performances(self, dataset_name: str) -> Dict[str, float]: + """Vérifie les performances des modèles sur le dataset spécifié. + + Args: + dataset_name: Nom du dataset à vérifier + + Returns: + Dictionnaire des performances des modèles (model_name -> score) + """ + logger.info(f"Vérification des performances des modèles sur le dataset: {dataset_name}") + performances = {} + + try: + # Cette partie est spéculative car nous ne connaissons pas la structure exacte + # des résultats. Une approche possible serait de chercher des fichiers JSON + # contenant des métriques dans les datasets d'évaluation. + + # Chercher les datasets d'évaluation + datasets = self.api.list_datasets(author=self.organization) + eval_datasets = [ds for ds in datasets if ds.id.startswith(f"{self.organization}/evaluation-")] + + for eval_ds in eval_datasets: + try: + files = self.api.list_repo_files(eval_ds.id, repo_type="dataset") + result_files = [f for f in files if "result" in f.lower() and f.endswith(".json")] + + for result_file in result_files: + file_path = self.api.hf_hub_download( + repo_id=eval_ds.id, + filename=result_file, + repo_type="dataset" + ) + + with open(file_path, 'r') as f: + results = json.load(f) + + # Analyse basique des résultats (à adapter selon la structure réelle) + if "model_name" in results and "metrics" in results: + model_name = results["model_name"] + metrics = results["metrics"] + + # Prendre la première métrique trouvée comme score + if metrics and isinstance(metrics, dict): + first_metric = list(metrics.keys())[0] + performances[model_name] = metrics[first_metric] + except: + continue + + if performances: + logger.success(f"Performances trouvées pour {len(performances)} modèles") + for model, score in performances.items(): + logger.info(f" - {model}: {score}") + else: + logger.warning("Aucune performance de modèle trouvée") + + return performances + + except Exception as e: + logger.error(f"Erreur lors de la vérification des performances: {str(e)}") + return {} + +def main(): + """Fonction principale.""" + parser = configure_argument_parser() + args = parser.parse_args() + + if not args.dataset: + logger.error("Veuillez spécifier un dataset avec --dataset") + parser.print_help() + return + + # Créer le testeur + tester = YourbenchTester(args.org, args.verbose) + + # 1. Vérifier l'existence du dataset + dataset_info = tester.test_dataset_exists(args.dataset) + + if not dataset_info: + logger.error(f"Le dataset {args.org}/{args.dataset} n'existe pas ou n'est pas accessible") + return + + # 2. Analyser le contenu du dataset + success, stats = tester.analyze_dataset_content(args.dataset) + + if success: + logger.info("\n=== Statistiques du dataset ===") + logger.info(f"Nombre de fichiers: {stats['fichiers']}") + logger.info(f"Fichiers JSON: {stats['fichiers_json']}") + logger.info(f"Fichiers Parquet: {stats['fichiers_parquet']}") + logger.info(f"Contient des questions: {'Oui' if stats['a_questions'] else 'Non'}") + + if stats['a_questions']: + logger.info(f"Nombre de questions: {stats['nb_questions']}") + + if 'types_documents' in stats and stats['types_documents']: + logger.info(f"Types de documents: {', '.join(stats['types_documents'])}") + + # Afficher la structure des fichiers Parquet + if 'structure_parquet' in stats and stats['structure_parquet']: + logger.info("\n=== Structure des fichiers Parquet ===") + for category, info in stats['structure_parquet'].items(): + logger.info(f"\nCatégorie: {category}") + logger.info(f"Nombre de lignes: {info['nb_lignes']}") + logger.info(f"Colonnes: {', '.join(info['colonnes'])}") + + if args.verbose and 'exemple' in info and info['exemple']: + logger.info("\nExemple de ligne:") + for key, value in info['exemple'].items(): + # Tronquer les valeurs trop longues + if isinstance(value, str) and len(value) > 100: + value = value[:100] + "..." + logger.info(f" {key}: {value}") + + # 3. Vérifier s'il existe des résultats d'évaluation + has_evaluations = tester.check_evaluation_results(args.dataset) + + if has_evaluations: + # 4. Vérifier les performances des modèles + performances = tester.check_model_performances(args.dataset) + + if performances: + logger.info("\n=== Classement des modèles ===") + # Trier les modèles par score (du plus élevé au plus bas) + sorted_models = sorted(performances.items(), key=lambda x: x[1], reverse=True) + for i, (model, score) in enumerate(sorted_models, 1): + logger.info(f"{i}. {model}: {score:.4f}") + + logger.success("Test terminé !") + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 0000000000000000000000000000000000000000..f2893d3eef1fc6b5af918d0debd30e9c45de5733 --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,33 @@ +services: + backend: + build: + context: ./backend + dockerfile: Dockerfile.dev + args: + - HF_TOKEN=${HF_TOKEN} + ports: + - "${BACKEND_PORT:-8000}:8000" + volumes: + - ./backend:/app + environment: + - ENVIRONMENT=${ENVIRONMENT:-development} + - HF_TOKEN=${HF_TOKEN} + - HF_HOME=${HF_HOME:-/.cache} + command: uvicorn app.asgi:app --host 0.0.0.0 --port 8000 --reload + + frontend: + build: + context: ./frontend + dockerfile: Dockerfile.dev + ports: + - "${FRONTEND_PORT:-7860}:7860" + volumes: + - ./frontend:/app + - /app/node_modules + environment: + - NODE_ENV=${ENVIRONMENT:-development} + - CHOKIDAR_USEPOLLING=true + - PORT=${FRONTEND_PORT:-7860} + command: npm start + stdin_open: true + tty: true \ No newline at end of file diff --git a/frontend/Dockerfile.dev b/frontend/Dockerfile.dev new file mode 100644 index 0000000000000000000000000000000000000000..259f7c9d8746db26bee8ee531d96cbe0d619321e --- /dev/null +++ b/frontend/Dockerfile.dev @@ -0,0 +1,15 @@ +FROM node:18 + +WORKDIR /app + +# Install required global dependencies +RUN npm install -g react-scripts + +# Copy package.json and package-lock.json +COPY package*.json ./ + +# Install project dependencies +RUN npm install + +# Volume will be mounted here, no need for COPY +CMD ["npm", "start"] \ No newline at end of file diff --git a/frontend/README.md b/frontend/README.md new file mode 100644 index 0000000000000000000000000000000000000000..7ef4ff265f3c870efce128f47bdda8d266689a88 --- /dev/null +++ b/frontend/README.md @@ -0,0 +1,80 @@ +# Frontend - Open LLM Leaderboard 🏆 + +React interface for exploring and comparing open-source language models. + +## 🏗 Architecture + +```mermaid +flowchart TD + Client(["User Browser"]) --> Components["React Components"] + + subgraph Frontend + Components --> Context["Context Layer
• LeaderboardContext
• Global State"] + + API["API Layer
• /api/leaderboard/formatted
• TanStack Query"] --> |Data Feed| Context + + Context --> Hooks["Hooks Layer
• Data Processing
• Filtering
• Caching"] + + Hooks --> Features["Features
• Table Management
• Search & Filters
• Display Options"] + Features --> Cache["Cache Layer
• LocalStorage
• URL State"] + end + + API --> Backend["Backend Server"] + + style Backend fill:#f96,stroke:#333,stroke-width:2px +``` + +## ✨ Core Features + +- 🔍 **Search & Filters**: Real-time filtering, regex search, advanced filters +- 📊 **Data Visualization**: Interactive table, customizable columns, sorting +- 🔄 **State Management**: URL sync, client-side caching (5min TTL) +- 📱 **Responsive Design**: Mobile-friendly, dark/light themes + +## 🛠 Tech Stack + +- React 18 + Material-UI +- TanStack Query & Table +- React Router v6 + +## 📁 Project Structure + +``` +src/ +├── pages/ +│ └── LeaderboardPage/ +│ ├── components/ # UI Components +│ ├── context/ # Global State +│ └── hooks/ # Data Processing +├── components/ # Shared Components +└── utils/ # Helper Functions +``` + +## 🚀 Development + +```bash +# Install dependencies +npm install + +# Start development server +npm start + +# Production build +npm run build +``` + +## 🔧 Environment Variables + +```env +# API Configuration +REACT_APP_API_URL=http://localhost:8000 +REACT_APP_CACHE_DURATION=300000 # 5 minutes +``` + +## 🔄 Data Flow + +1. API fetches leaderboard data from backend +2. Context stores and manages global state +3. Hooks handle data processing and filtering +4. Components render based on processed data +5. Cache maintains user preferences and URL state diff --git a/frontend/package.json b/frontend/package.json new file mode 100644 index 0000000000000000000000000000000000000000..93de14fd49415a97be66fa06310e2a1249b85ad6 --- /dev/null +++ b/frontend/package.json @@ -0,0 +1,55 @@ +{ + "name": "open-llm-leaderboard", + "version": "0.1.0", + "private": true, + "dependencies": { + "@emotion/react": "^11.13.3", + "@emotion/styled": "^11.13.0", + "@huggingface/hub": "^0.14.0", + "@mui/icons-material": "^6.1.7", + "@mui/lab": "^6.0.0-beta.16", + "@mui/material": "^6.1.6", + "@mui/x-data-grid": "^7.22.2", + "@tanstack/react-query": "^5.62.2", + "@tanstack/react-table": "^8.20.5", + "@tanstack/react-virtual": "^3.10.9", + "@testing-library/jest-dom": "^5.17.0", + "@testing-library/react": "^13.4.0", + "@testing-library/user-event": "^13.5.0", + "compression": "^1.7.4", + "cors": "^2.8.5", + "express": "^4.18.2", + "react": "^18.3.1", + "react-dom": "^18.3.1", + "react-router-dom": "^6.28.0", + "react-scripts": "5.0.1", + "serve-static": "^1.15.0", + "web-vitals": "^2.1.4" + }, + "scripts": { + "start": "react-scripts start", + "build": "react-scripts build", + "test": "react-scripts test", + "eject": "react-scripts eject", + "serve": "node server.js" + }, + "eslintConfig": { + "extends": [ + "react-app", + "react-app/jest" + ] + }, + "browserslist": { + "production": [ + ">0.2%", + "not dead", + "not op_mini all" + ], + "development": [ + "last 1 chrome version", + "last 1 firefox version", + "last 1 safari version" + ] + }, + "proxy": "http://backend:8000" +} diff --git a/frontend/public/index.html b/frontend/public/index.html new file mode 100644 index 0000000000000000000000000000000000000000..a8591a1fee67f55b23e147afb2b8a5e7afc5005a --- /dev/null +++ b/frontend/public/index.html @@ -0,0 +1,96 @@ + + + + + + + + + + + + + + + + + + + + + + + + Open LLM Leaderboard - Compare Open Source Large Language Models + + + + + + +
+ + + diff --git a/frontend/public/logo256.png b/frontend/public/logo256.png new file mode 100644 index 0000000000000000000000000000000000000000..58547e134af0ac1200a4608fb1c800b3e8e9ddf1 Binary files /dev/null and b/frontend/public/logo256.png differ diff --git a/frontend/public/logo32.png b/frontend/public/logo32.png new file mode 100644 index 0000000000000000000000000000000000000000..1b6e8fbd42dd1bcc599649bf6f230fde89a6908a Binary files /dev/null and b/frontend/public/logo32.png differ diff --git a/frontend/public/og-image.jpg b/frontend/public/og-image.jpg new file mode 100644 index 0000000000000000000000000000000000000000..1d4a3f3cb7d838489ef0a5dde1ce7c493273f98d Binary files /dev/null and b/frontend/public/og-image.jpg differ diff --git a/frontend/public/robots.txt b/frontend/public/robots.txt new file mode 100644 index 0000000000000000000000000000000000000000..e9e57dc4d41b9b46e05112e9f45b7ea6ac0ba15e --- /dev/null +++ b/frontend/public/robots.txt @@ -0,0 +1,3 @@ +# https://www.robotstxt.org/robotstxt.html +User-agent: * +Disallow: diff --git a/frontend/server.js b/frontend/server.js new file mode 100644 index 0000000000000000000000000000000000000000..653befea69419568b117ce809871639d86d65581 --- /dev/null +++ b/frontend/server.js @@ -0,0 +1,85 @@ +const express = require("express"); +const cors = require("cors"); +const compression = require("compression"); +const path = require("path"); +const serveStatic = require("serve-static"); +const { createProxyMiddleware } = require("http-proxy-middleware"); + +const app = express(); +const port = process.env.PORT || 7860; +const apiPort = process.env.INTERNAL_API_PORT || 7861; + +// Enable CORS for all routes +app.use(cors()); + +// Enable GZIP compression +app.use(compression()); + +// Proxy all API requests to the Python backend +app.use( + "/api", + createProxyMiddleware({ + target: `http://127.0.0.1:${apiPort}`, + changeOrigin: true, + onError: (err, req, res) => { + console.error("Proxy Error:", err); + res.status(500).json({ error: "Proxy Error", details: err.message }); + }, + }) +); + +// Serve static files from the build directory +app.use( + express.static(path.join(__dirname, "build"), { + // Don't cache HTML files + setHeaders: (res, path) => { + if (path.endsWith(".html")) { + res.setHeader("Cache-Control", "no-cache, no-store, must-revalidate"); + res.setHeader("Pragma", "no-cache"); + res.setHeader("Expires", "0"); + } else { + // Cache other static resources for 1 year + res.setHeader("Cache-Control", "public, max-age=31536000"); + } + }, + }) +); + +// Middleware to preserve URL parameters +app.use((req, res, next) => { + // Don't interfere with API requests + if (req.url.startsWith("/api")) { + return next(); + } + + // Preserve original URL parameters + req.originalUrl = req.url; + next(); +}); + +// Handle all other routes by serving index.html +app.get("*", (req, res) => { + // Don't interfere with API requests + if (req.url.startsWith("/api")) { + return next(); + } + + // Headers for client-side routing + res.set({ + "Cache-Control": "no-cache, no-store, must-revalidate", + Pragma: "no-cache", + Expires: "0", + }); + + // Send index.html for all other routes + res.sendFile(path.join(__dirname, "build", "index.html")); +}); + +app.listen(port, "0.0.0.0", () => { + console.log( + `Frontend server is running on port ${port} in ${ + process.env.NODE_ENV || "development" + } mode` + ); + console.log(`API proxy target: http://127.0.0.1:${apiPort}`); +}); diff --git a/frontend/src/App.js b/frontend/src/App.js new file mode 100644 index 0000000000000000000000000000000000000000..43c0c89f4d6c4ba3e1e2324f55a78ac20c0c6172 --- /dev/null +++ b/frontend/src/App.js @@ -0,0 +1,427 @@ +import React, { useState, useEffect } from "react"; +import { + Box, + Container, + CssBaseline, + Typography, + CircularProgress, +} from "@mui/material"; +import { + BrowserRouter as Router, + Routes, + Route, + Navigate, + useNavigate, + useSearchParams, +} from "react-router-dom"; +import getTheme from "./config/theme"; +import { useThemeMode } from "./hooks/useThemeMode"; +import { ThemeProvider } from "@mui/material/styles"; +import BenchmarkGenerator from "./components/BenchmarkGenerator"; +import BenchmarkCreateForm from "./components/BenchmarkCreateForm"; +import BenchmarkDisplay from "./components/BenchmarkDisplay"; +import BenchmarkEvaluation from "./components/BenchmarkEvaluation"; +import EvaluationDisplay from "./components/EvaluationDisplay"; + +// Composant d'en-tête commun +const Header = () => ( + +

Yourbench Demo

+

+ Quickly create zero-shot benchmarks from your documents – keeping + models accurate and adaptable +

+ +); + +// Page d'accueil avec le formulaire +function HomePage() { + const navigate = useNavigate(); + + const handleStartGeneration = (sid) => { + navigate(`/benchmark-generation?session=${sid}`); + }; + + return ( + <> +
+ + + ); +} + +// Page de génération de benchmark +function BenchmarkGenerationPage() { + const navigate = useNavigate(); + const [searchParams] = useSearchParams(); + const sessionId = searchParams.get("session"); + const [isValidSession, setIsValidSession] = useState(true); + + // Vérifier la validité de la session + useEffect(() => { + if (!sessionId) { + setIsValidSession(false); + } + }, [sessionId]); + + const handleGenerationComplete = (result) => { + console.log("Benchmark generation completed:", result); + if (result && result.success) { + navigate(`/benchmark-display?session=${sessionId}`); + } + }; + + if (!isValidSession) { + return ; + } + + return ( + <> +
+ + + ); +} + +// Page d'affichage du benchmark +function BenchmarkDisplayPage() { + const navigate = useNavigate(); + const [searchParams] = useSearchParams(); + const sessionId = searchParams.get("session"); + const [benchmarkQuestions, setBenchmarkQuestions] = useState([]); + const [datasetUrl, setDatasetUrl] = useState(null); + const [isValidSession, setIsValidSession] = useState(true); + const [isLoading, setIsLoading] = useState(true); + + // Récupérer les questions du benchmark depuis l'API + useEffect(() => { + console.log("BenchmarkDisplayPage useEffect - sessionId:", sessionId); + + if (!sessionId) { + console.log("Session ID manquante, redirection vers l'accueil"); + setIsValidSession(false); + return; + } + + setIsLoading(true); + + const fetchBenchmarkQuestions = async () => { + console.log( + "Tentative de récupération des questions pour la session:", + sessionId + ); + try { + const apiUrl = `http://localhost:3001/benchmark-questions/${sessionId}`; + console.log("Appel API:", apiUrl); + + const response = await fetch(apiUrl); + console.log("Réponse API reçue:", response.status); + + // Check if the response status is 404 (Not Found) or other error + if (!response.ok) { + if (response.status === 404) { + console.error("Session non trouvée"); + setIsValidSession(false); + return; + } else { + console.error(`Erreur serveur: ${response.status}`); + setIsLoading(false); + return; + } + } + + const data = await response.json(); + console.log("Données API:", data); + + if (data.success && data.questions && data.questions.length > 0) { + console.log("Questions chargées avec succès:", data.questions); + setBenchmarkQuestions(data.questions); + } else { + console.warn( + "Échec du chargement des questions, utilisation des valeurs par défaut" + ); + } + + if (data.dataset_url) { + setDatasetUrl(data.dataset_url); + } else { + const url = `https://huggingface.co/datasets/yourbench/yourbench_${sessionId}`; + setDatasetUrl(url); + console.log("URL du dataset générée:", url); + } + } catch (error) { + console.error("Erreur lors de la récupération des questions:", error); + setIsValidSession(false); + } finally { + setIsLoading(false); + } + }; + + fetchBenchmarkQuestions(); + }, [sessionId]); + + const handleStartEvaluation = () => { + console.log("Starting evaluation with session ID:", sessionId); + navigate(`/benchmark-evaluation?session=${sessionId}`); + }; + + // Questions par défaut si l'API échoue + const defaultSampleQuestions = [ + { + id: 1, + question: "What are the key features discussed in the document?", + type: "single_shot", + }, + { + id: 2, + question: + "How does the proposed solution address the challenges mentioned in section 2 in relation to the overall market trends?", + type: "multi_hop", + }, + ]; + + if (!isValidSession) { + return ; + } + + return ( + <> +
+ {isLoading ? ( + + + + ) : ( + 0 + ? benchmarkQuestions + : defaultSampleQuestions + } + /> + )} + + ); +} + +// Page d'évaluation du benchmark +function BenchmarkEvaluationPage() { + const navigate = useNavigate(); + const [searchParams] = useSearchParams(); + const sessionId = searchParams.get("session"); + const [isValidSession, setIsValidSession] = useState(true); + const [isLoading, setIsLoading] = useState(true); + + // Vérifier la validité de la session + useEffect(() => { + if (!sessionId) { + console.log( + "Session ID manquante pour l'évaluation, redirection vers l'accueil" + ); + setIsValidSession(false); + return; + } + + // Verify session exists by calling the API + const checkSession = async () => { + try { + const response = await fetch( + `http://localhost:3001/benchmark-questions/${sessionId}` + ); + + if (!response.ok) { + console.error( + `Session invalide ou erreur serveur: ${response.status}` + ); + setIsValidSession(false); + } + } catch (error) { + console.error("Erreur lors de la vérification de la session:", error); + setIsValidSession(false); + } finally { + setIsLoading(false); + } + }; + + checkSession(); + }, [sessionId]); + + const handleEvaluationComplete = (result) => { + console.log("Évaluation terminée:", result); + // On reste sur la même page car les résultats sont affichés directement + // dans le composant BenchmarkEvaluation + }; + + if (!isValidSession) { + return ; + } + + return ( + <> +
+ {isLoading ? ( + + + + ) : ( + + )} + + ); +} + +// Page d'affichage des résultats d'évaluation +function EvaluationDisplayPage() { + const navigate = useNavigate(); + const [searchParams] = useSearchParams(); + const sessionId = searchParams.get("session"); + const [isValidSession, setIsValidSession] = useState(true); + const [isLoading, setIsLoading] = useState(true); + + // Vérifier la validité de la session + useEffect(() => { + if (!sessionId) { + console.log( + "Session ID manquante pour l'affichage des résultats, redirection vers l'accueil" + ); + setIsValidSession(false); + return; + } + + // Verify session exists by calling the API + const checkSession = async () => { + try { + const response = await fetch( + `http://localhost:3001/benchmark-questions/${sessionId}` + ); + + if (!response.ok) { + console.error( + `Session invalide ou erreur serveur: ${response.status}` + ); + setIsValidSession(false); + } + } catch (error) { + console.error("Erreur lors de la vérification de la session:", error); + setIsValidSession(false); + } finally { + setIsLoading(false); + } + }; + + checkSession(); + }, [sessionId]); + + if (!isValidSession) { + return ; + } + + return ( + <> +
+ {isLoading ? ( + + + + ) : ( + + )} + + ); +} + +// Raccourcis clavier +function KeyboardShortcuts() { + useEffect(() => { + const handleKeyDown = (e) => { + if (e.key === "p") { + console.log("Debug key pressed: Clearing auth data and refreshing"); + localStorage.removeItem("hf_oauth"); + localStorage.removeItem("auth_return_to"); + alert("Auth data cleared. Page will reload."); + window.location.reload(); + } + }; + + window.addEventListener("keydown", handleKeyDown); + return () => { + window.removeEventListener("keydown", handleKeyDown); + }; + }, []); + + return null; +} + +// Composant principal avec les routes +function App() { + const { mode } = useThemeMode(); + const theme = getTheme(mode); + + return ( + + + + + + + + } /> + } + /> + } + /> + } + /> + } + /> + } /> + + + + + + ); +} + +export default App; diff --git a/frontend/src/components/BenchmarkCreateForm.jsx b/frontend/src/components/BenchmarkCreateForm.jsx new file mode 100644 index 0000000000000000000000000000000000000000..047d8945f414d28fcb4c51f7f18c25e556bdbc4f --- /dev/null +++ b/frontend/src/components/BenchmarkCreateForm.jsx @@ -0,0 +1,295 @@ +import React, { useState, useRef, useEffect } from "react"; +import { + Box, + Paper, + Typography, + CircularProgress, + Alert, + Button, + Stepper, + Step, + StepLabel, +} from "@mui/material"; +import { useLocation } from "react-router-dom"; +import CloudUploadIcon from "@mui/icons-material/CloudUpload"; +import PlayArrowIcon from "@mui/icons-material/PlayArrow"; +import AuthContainer from "./shared/AuthContainer"; +import { useThemeMode } from "../hooks/useThemeMode"; +import getTheme from "../config/theme"; + +/** + * Component to display a stepper with three steps: Login, Upload File, and Generate + * + * @param {Object} props - Component props + * @param {number} props.activeStep - Current active step (0-based index) + * @returns {JSX.Element} Stepper component + */ +const StepsDisplay = ({ activeStep }) => { + const steps = ["Login", "Upload File", "Generate"]; + + return ( + + + {steps.map((label) => ( + + {label} + + ))} + + + ); +}; + +/** + * Component for creating a new benchmark, including authentication, file upload, and generation initiation + * + * @param {Object} props - Component props + * @param {Function} props.onStartGeneration - Callback when generation starts with sessionId + * @returns {JSX.Element} BenchmarkCreateForm component + */ +function BenchmarkCreateForm({ onStartGeneration }) { + const { mode } = useThemeMode(); + const theme = getTheme(mode); + const [isDragging, setIsDragging] = useState(false); + const [uploadStatus, setUploadStatus] = useState(null); + const [isLoading, setIsLoading] = useState(false); + const [activeStep, setActiveStep] = useState(0); + const [sessionId, setSessionId] = useState(null); + const fileInputRef = useRef(null); + const location = useLocation(); + + // Check if we're coming back from an OAuth redirect + useEffect(() => { + // If we have code in URL parameters, it's an OAuth callback + const params = new URLSearchParams(window.location.search); + if (params.has("code")) { + console.log("Detected OAuth callback, cleaning URL"); + + // Remove the query parameters from the URL without reloading + window.history.replaceState({}, document.title, window.location.pathname); + + // Check if we have auth data in localStorage after a brief delay to let OAuth process complete + setTimeout(() => { + const storedAuth = localStorage.getItem("hf_oauth"); + if (storedAuth) { + console.log("Found auth data after redirect, refreshing UI state"); + setActiveStep(1); // Move to next step if authenticated + } + }, 1000); + } + }, [location]); + + const handleDragOver = (e) => { + e.preventDefault(); + setIsDragging(true); + }; + + const handleDragLeave = () => { + setIsDragging(false); + }; + + const handleClick = () => { + fileInputRef.current.click(); + }; + + const handleFileChange = (e) => { + const file = e.target.files[0]; + if (!file) return; + + // Vérifier si c'est un PDF, TXT, HTML ou MD + if ( + !file.name.endsWith(".pdf") && + !file.name.endsWith(".txt") && + !file.name.endsWith(".html") && + !file.name.endsWith(".md") + ) { + setUploadStatus({ + success: false, + message: "Only PDF, TXT, HTML and MD files are accepted", + }); + return; + } + + handleFileUpload(file); + }; + + const handleFileUpload = async (file) => { + setIsLoading(true); + setUploadStatus(null); + + try { + const formData = new FormData(); + formData.append("file", file); + + const response = await fetch("http://localhost:3001/upload", { + method: "POST", + body: formData, + }); + + const result = await response.json(); + + if (response.ok) { + setUploadStatus({ + success: true, + message: `File ${result.filename} uploaded successfully`, + }); + // Store the session ID for the benchmark generation + setSessionId(result.session_id); + setActiveStep(2); // Advance to Generate step after successful upload + } else { + setUploadStatus({ + success: false, + message: result.error || "Upload failed", + }); + } + } catch (error) { + setUploadStatus({ + success: false, + message: "Server connection error", + }); + } finally { + setIsLoading(false); + } + }; + + const handleDrop = async (e) => { + e.preventDefault(); + setIsDragging(false); + + const file = e.dataTransfer.files[0]; + if (!file) { + setUploadStatus({ success: false, message: "No file detected" }); + return; + } + + // Vérifier si c'est un PDF, TXT, HTML ou MD + if ( + !file.name.endsWith(".pdf") && + !file.name.endsWith(".txt") && + !file.name.endsWith(".html") && + !file.name.endsWith(".md") + ) { + setUploadStatus({ + success: false, + message: "Only PDF, TXT, HTML and MD files are accepted", + }); + return; + } + + handleFileUpload(file); + }; + + const handleGenerateClick = () => { + if (onStartGeneration && sessionId) { + onStartGeneration(sessionId); + } + }; + + return ( + <> + + + {/* Authentication step */} + {activeStep === 0 && ( + setActiveStep(1)} + /> + )} + + {/* File upload step */} + {activeStep === 1 && ( + + + + + Drag and drop your file here or click to browse + + + Accepted formats: PDF, TXT, HTML, MD + + + {isLoading && ( + + + + )} + + {uploadStatus && ( + + {uploadStatus.message} + + )} + + )} + + {/* Generate button step */} + {activeStep === 2 && ( + + + + Ready to generate your benchmark + + + + )} + + ); +} + +export default BenchmarkCreateForm; diff --git a/frontend/src/components/BenchmarkDisplay.jsx b/frontend/src/components/BenchmarkDisplay.jsx new file mode 100644 index 0000000000000000000000000000000000000000..40d29f7a0581d9ef1671120cb323e443c579bfb9 --- /dev/null +++ b/frontend/src/components/BenchmarkDisplay.jsx @@ -0,0 +1,161 @@ +import React, { useState } from "react"; +import { + Box, + Typography, + Paper, + Button, + Divider, + Card, + CardContent, + Link, + CircularProgress, + Tooltip, +} from "@mui/material"; +import PlayArrowIcon from "@mui/icons-material/PlayArrow"; +import AssessmentIcon from "@mui/icons-material/Assessment"; +import LinkIcon from "@mui/icons-material/Link"; +import DownloadIcon from "@mui/icons-material/Download"; +import CheckCircleIcon from "@mui/icons-material/CheckCircle"; + +/** + * Component to display benchmark information and evaluation button + * + * @param {Object} props - Component props + * @param {Array} props.sampleQuestions - Array of sample questions to display + * @param {Function} props.onStartEvaluation - Function to call when evaluation button is clicked + * @param {string} props.sessionId - Session ID used for the benchmark generation + * @param {string} props.datasetUrl - URL to the Hugging Face dataset + * @returns {JSX.Element} Benchmark display component + */ +const BenchmarkDisplay = ({ + sampleQuestions = [], + onStartEvaluation, + sessionId, + datasetUrl, +}) => { + const [isDownloading, setIsDownloading] = useState(false); + + // Default questions if none provided + const questions = + sampleQuestions.length > 0 + ? sampleQuestions + : [ + { + id: 1, + question: "What are the key benefits of the described technology?", + type: "single_shot", + }, + { + id: 2, + question: + "Based on the context about machine learning frameworks, how does TensorFlow compare to PyTorch in terms of deployment capabilities?", + type: "multi_hop", + }, + ]; + + const handleEvaluationClick = () => { + if (onStartEvaluation) { + onStartEvaluation(); + } + }; + + const handleDownloadClick = async () => { + if (!sessionId) return; + + setIsDownloading(true); + try { + // Requête pour télécharger le dataset + const downloadUrl = `http://localhost:3001/download-dataset/${sessionId}`; + + // Créer un élément a temporaire pour déclencher le téléchargement + const link = document.createElement("a"); + link.href = downloadUrl; + link.setAttribute("download", `yourbench_${sessionId}_dataset.zip`); + document.body.appendChild(link); + link.click(); + document.body.removeChild(link); + } catch (error) { + console.error("Erreur lors du téléchargement du dataset:", error); + alert("Erreur lors du téléchargement. Veuillez réessayer."); + } finally { + setIsDownloading(false); + } + }; + + return ( + + {/* Header avec titre et bouton de téléchargement alignés */} + + + + Benchmark Created Successfully + + + + + + + + + Your benchmark has been generated. Here are some example questions: + + + + {questions.map((q, index) => ( + + + + {q.type === "multi_hop" + ? "Multi-hop Question" + : "Single-shot Question"} + + {q.question} + + + ))} + + + + + + + ); +}; + +export default BenchmarkDisplay; diff --git a/frontend/src/components/BenchmarkEvaluation.jsx b/frontend/src/components/BenchmarkEvaluation.jsx new file mode 100644 index 0000000000000000000000000000000000000000..be8192df2ca1d26b068406d3c1f3ed6bde92b97c --- /dev/null +++ b/frontend/src/components/BenchmarkEvaluation.jsx @@ -0,0 +1,364 @@ +import React, { useState, useEffect, useRef } from "react"; +import { + Box, + Typography, + CircularProgress, + Alert, + Paper, + Divider, + Button, +} from "@mui/material"; +import AccessTimeIcon from "@mui/icons-material/AccessTime"; +import LogDisplay from "./LogDisplay"; +import { useNavigate } from "react-router-dom"; + +// Evaluation steps +const EVALUATION_STEPS = [ + "preparation", + "model_evaluation", + "results_compilation", +]; + +// Friendly step names for display +const STEP_LABELS = { + preparation: "Preparation", + model_evaluation: "Model Evaluation", + results_compilation: "Results Compilation", +}; + +/** + * Component to handle benchmark evaluation and display logs + * + * @param {Object} props - Component props + * @param {string} props.sessionId - Session ID of the benchmark to evaluate + * @param {Function} props.onComplete - Function to call when evaluation is complete + * @returns {JSX.Element} Benchmark evaluation component + */ +const BenchmarkEvaluation = ({ sessionId, onComplete }) => { + const [evaluating, setEvaluating] = useState(false); + const [evaluationComplete, setEvaluationComplete] = useState(false); + const [evaluationLogs, setEvaluationLogs] = useState([]); + const [error, setError] = useState(null); + const [currentPhase, setCurrentPhase] = useState("initializing"); + const [completedSteps, setCompletedSteps] = useState([]); + const [activeStep, setActiveStep] = useState(0); + const [elapsedTime, setElapsedTime] = useState(0); + + const pollingIntervalRef = useRef(null); + const timerIntervalRef = useRef(null); + const startTimeRef = useRef(null); + + const navigate = useNavigate(); + + // Start evaluation when component mounts + useEffect(() => { + // Set start time + startTimeRef.current = Date.now(); + + // Start timer + timerIntervalRef.current = setInterval(() => { + const timeElapsed = Math.floor( + (Date.now() - startTimeRef.current) / 1000 + ); + setElapsedTime(timeElapsed); + }, 1000); + + startEvaluation(); + + // Clean up intervals on unmount + return () => { + if (pollingIntervalRef.current) { + clearInterval(pollingIntervalRef.current); + } + if (timerIntervalRef.current) { + clearInterval(timerIntervalRef.current); + } + }; + }, []); + + // Determine current phase and completed steps from logs + useEffect(() => { + if (evaluationLogs.length === 0) return; + + // Check all logs for completed steps + const newCompletedSteps = [...completedSteps]; + let newActiveStep = activeStep; + + evaluationLogs.forEach((log) => { + // Detect completed steps (format: [SUCCESS] Stage completed: step_name) + const match = log.match(/\[SUCCESS\] Stage completed: (\w+)/); + if (match && match[1]) { + const completedStep = match[1].trim(); + if ( + EVALUATION_STEPS.includes(completedStep) && + !newCompletedSteps.includes(completedStep) + ) { + newCompletedSteps.push(completedStep); + // Set active step to index of next step + const stepIndex = EVALUATION_STEPS.indexOf(completedStep); + if (stepIndex >= 0 && stepIndex + 1 > newActiveStep) { + newActiveStep = stepIndex + 1; + if (newActiveStep >= EVALUATION_STEPS.length) { + newActiveStep = EVALUATION_STEPS.length; + } + } + } + } + }); + + // Update state if there are new completed steps + if (newCompletedSteps.length > completedSteps.length) { + setCompletedSteps(newCompletedSteps); + setActiveStep(newActiveStep); + } + + // Check recent logs to determine current phase + const recentLogs = evaluationLogs.slice(-10); + + // Detect completion conditions + const isComplete = + recentLogs.some((log) => + log.includes("[SUCCESS] Evaluation completed") + ) || + completedSteps.includes("results_compilation") || + newCompletedSteps.includes("results_compilation"); + + if (isComplete) { + setCurrentPhase("complete"); + setEvaluationComplete(true); + // Stop polling when evaluation is complete + if (pollingIntervalRef.current) { + clearInterval(pollingIntervalRef.current); + } + if (timerIntervalRef.current) { + clearInterval(timerIntervalRef.current); + } + // Notify parent component that evaluation is complete + if (onComplete) { + onComplete({ + success: true, + sessionId, + logs: evaluationLogs, + }); + } + } else if (recentLogs.some((log) => log.includes("Comparing models"))) { + setCurrentPhase("compiling_results"); + } else if (recentLogs.some((log) => log.includes("Starting evaluations"))) { + setCurrentPhase("evaluating"); + } else if (recentLogs.some((log) => log.includes("Initialization"))) { + setCurrentPhase("preparing"); + } + }, [evaluationLogs, completedSteps, activeStep, sessionId, onComplete]); + + // Format elapsed time as HH:MM:SS + const formatElapsedTime = () => { + const hours = Math.floor(elapsedTime / 3600); + const minutes = Math.floor((elapsedTime % 3600) / 60); + const seconds = elapsedTime % 60; + + return [ + hours.toString().padStart(2, "0"), + minutes.toString().padStart(2, "0"), + seconds.toString().padStart(2, "0"), + ].join(":"); + }; + + // Start benchmark evaluation + const startEvaluation = async () => { + if (!sessionId) { + setError("Missing session ID"); + return; + } + + setEvaluating(true); + setEvaluationLogs([]); + setError(null); + setCurrentPhase("initializing"); + setCompletedSteps([]); + setActiveStep(0); + + try { + // Call API to start evaluation + const response = await fetch("http://localhost:3001/evaluate-benchmark", { + method: "POST", + headers: { + "Content-Type": "application/json", + }, + body: JSON.stringify({ + session_id: sessionId, + }), + }); + + const result = await response.json(); + + if (response.ok) { + setEvaluationLogs(result.logs || []); + + // Set up polling to retrieve more logs + pollingIntervalRef.current = setInterval(async () => { + // Check if we're already done + if (evaluationComplete) { + clearInterval(pollingIntervalRef.current); + return; + } + + try { + // Call API to get latest logs + const logsResponse = await fetch( + `http://localhost:3001/evaluation-logs/${sessionId}` + ); + + if (logsResponse.ok) { + const logsResult = await logsResponse.json(); + + // Update logs if there are new ones + if ( + logsResult.logs && + logsResult.logs.length > evaluationLogs.length + ) { + setEvaluationLogs(logsResult.logs); + } + + // Check if evaluation is complete + if (logsResult.is_completed) { + setEvaluationComplete(true); + clearInterval(pollingIntervalRef.current); + } + } + } catch (error) { + console.log("Error polling logs:", error); + // Don't stop polling on network errors + } + }, 2000); // Poll every 2 seconds + } else { + // Handle error + setEvaluationLogs([`Error: ${result.error || "Unknown error"}`]); + setError(result.error || "Benchmark evaluation failed"); + } + } catch (error) { + console.error("Error starting evaluation:", error); + setEvaluationLogs([`Error: ${error.message || "Unknown error"}`]); + setError("Error connecting to server"); + } finally { + setEvaluating(false); + } + }; + + // Get title based on current phase + const getPhaseTitle = () => { + switch (currentPhase) { + case "initializing": + return "Preparing evaluation..."; + case "preparing": + return "Preparing models..."; + case "evaluating": + return "Evaluating models..."; + case "compiling_results": + return "Compiling results..."; + case "complete": + return "Evaluation completed successfully!"; + default: + return "Processing..."; + } + }; + + // Get current step info for display + const getCurrentStepInfo = () => { + const totalSteps = EVALUATION_STEPS.length; + const currentStepIndex = activeStep; + + // If no active step yet + if (currentStepIndex === 0 && completedSteps.length === 0) { + return `Starting... (0%)`; + } + + // If all steps completed + if (currentStepIndex >= totalSteps) { + return `Completed (100%)`; + } + + // Calculate percentage + const percentage = Math.round((currentStepIndex / totalSteps) * 100); + + // Get current step name + const currentStepName = + STEP_LABELS[EVALUATION_STEPS[currentStepIndex]] || "Processing"; + + return `${currentStepName} (${percentage}%)`; + }; + + // Function to navigate to results page + const viewResults = () => { + navigate(`/evaluation-display?session=${sessionId}`); + }; + + return ( + + {error ? ( + + {error} + + ) : ( + <> + {evaluationComplete ? ( + <> + + Evaluation completed successfully! + + + + ) : ( + <> + + + {getPhaseTitle()} + + + {/* Step progress indicator */} + + {getCurrentStepInfo()} + + + {/* Timer display */} + + {formatElapsedTime()} + + + )} + + )} + + {/* Use the LogDisplay component for logs */} + + + ); +}; + +export default BenchmarkEvaluation; diff --git a/frontend/src/components/BenchmarkGenerator.jsx b/frontend/src/components/BenchmarkGenerator.jsx new file mode 100644 index 0000000000000000000000000000000000000000..90caa908f92fe5ad15826ce7ff4a8174cc32a85d --- /dev/null +++ b/frontend/src/components/BenchmarkGenerator.jsx @@ -0,0 +1,398 @@ +import React, { useState, useEffect, useRef } from "react"; +import { Box, Typography, CircularProgress, Alert, Paper } from "@mui/material"; +import PlayArrowIcon from "@mui/icons-material/PlayArrow"; +import AccessTimeIcon from "@mui/icons-material/AccessTime"; +import LogDisplay from "./LogDisplay"; + +// Define all benchmark steps in sequence +const BENCHMARK_STEPS = [ + "ingestion", + "upload_ingest_to_hub", + "summarization", + "chunking", + "single_shot_question_generation", + "multi_hop_question_generation", + "lighteval", +]; + +// Step labels for display (more user-friendly names) +const STEP_LABELS = { + ingestion: "Ingestion", + upload_ingest_to_hub: "Upload to Hub", + summarization: "Summarization", + chunking: "Chunking", + single_shot_question_generation: "Single-shot QG", + multi_hop_question_generation: "Multi-hop QG", + lighteval: "LightEval", +}; + +/** + * Component to handle benchmark generation and display logs + * + * @param {Object} props - Component props + * @param {string} props.sessionId - The session ID for the uploaded file + * @param {Function} props.onComplete - Function to call when generation is complete + * @returns {JSX.Element} Benchmark generator component + */ +const BenchmarkGenerator = ({ sessionId, onComplete }) => { + const [generating, setGenerating] = useState(false); + const [generationComplete, setGenerationComplete] = useState(false); + const [generationLogs, setGenerationLogs] = useState([]); + const [error, setError] = useState(null); + const [currentPhase, setCurrentPhase] = useState("initializing"); + const [completedSteps, setCompletedSteps] = useState([]); + const [activeStep, setActiveStep] = useState(0); + const [elapsedTime, setElapsedTime] = useState(0); + + // Reference to keep track of the polling interval + const pollingIntervalRef = useRef(null); + + // Reference to keep track of the timer interval + const timerIntervalRef = useRef(null); + + // Reference for starting time + const startTimeRef = useRef(null); + + // Start generation on component mount + useEffect(() => { + // Set start time + startTimeRef.current = Date.now(); + + // Start timer + timerIntervalRef.current = setInterval(() => { + const timeElapsed = Math.floor( + (Date.now() - startTimeRef.current) / 1000 + ); + setElapsedTime(timeElapsed); + }, 1000); + + generateBenchmark(); + + // Clean up the polling interval and timer when the component unmounts + return () => { + if (pollingIntervalRef.current) { + clearInterval(pollingIntervalRef.current); + } + if (timerIntervalRef.current) { + clearInterval(timerIntervalRef.current); + } + }; + }, []); + + // Determine the current phase and completed steps based on logs + useEffect(() => { + if (generationLogs.length === 0) return; + + // Check all logs for completed stages + const newCompletedSteps = [...completedSteps]; + let newActiveStep = activeStep; + + generationLogs.forEach((log) => { + const match = log.match(/\[SUCCESS\] Stage completed: (\w+)/); + if (match && match[1]) { + const completedStep = match[1].trim(); + if ( + BENCHMARK_STEPS.includes(completedStep) && + !newCompletedSteps.includes(completedStep) + ) { + newCompletedSteps.push(completedStep); + // Set active step to the index of the next step + const stepIndex = BENCHMARK_STEPS.indexOf(completedStep); + if (stepIndex >= 0 && stepIndex + 1 > newActiveStep) { + newActiveStep = stepIndex + 1; + if (newActiveStep >= BENCHMARK_STEPS.length) { + newActiveStep = BENCHMARK_STEPS.length; + } + } + } + } + }); + + // Update state if there are new completed steps + if (newCompletedSteps.length > completedSteps.length) { + setCompletedSteps(newCompletedSteps); + setActiveStep(newActiveStep); + } + + // Check the latest logs to determine the current phase + const recentLogs = generationLogs.slice(-10); // Check more logs + + // Detect completion conditions + const isComplete = + recentLogs.some((log) => + log.includes("[SUCCESS] Ingestion process completed successfully") + ) || + recentLogs.some((log) => + log.includes( + "[SUCCESS] Configuration and ingestion completed successfully" + ) + ) || + completedSteps.includes("lighteval") || + newCompletedSteps.includes("lighteval"); + + if (isComplete) { + setCurrentPhase("complete"); + setGenerationComplete(true); + // Stop polling when benchmark is complete + if (pollingIntervalRef.current) { + clearInterval(pollingIntervalRef.current); + } + // Notify parent component that generation is complete + if (onComplete) { + console.log("Notifying parent that generation is complete"); + onComplete({ + success: true, + sessionId, + logs: generationLogs, + }); + } + } else if ( + recentLogs.some((log) => log.includes("starting benchmark creation")) + ) { + setCurrentPhase("benchmarking"); + } else if ( + recentLogs.some((log) => log.includes("Generating base configuration")) + ) { + setCurrentPhase("configuring"); + } + }, [generationLogs, completedSteps, activeStep, sessionId, onComplete]); + + const generateBenchmark = async () => { + if (!sessionId) { + setError("Missing session ID"); + return; + } + + setGenerating(true); + setGenerationLogs([]); + setError(null); + setCurrentPhase("initializing"); + setCompletedSteps([]); + setActiveStep(0); + + try { + // Call the API to generate the benchmark + const response = await fetch("http://localhost:3001/generate-benchmark", { + method: "POST", + headers: { + "Content-Type": "application/json", + }, + body: JSON.stringify({ + session_id: sessionId, + }), + }); + + const result = await response.json(); + + if (response.ok) { + setGenerationLogs(result.logs || []); + + // D'abord, on commence par interroger les logs de configuration + const pollConfigLogs = async () => { + try { + // Call the API to get the config logs + const configLogsResponse = await fetch( + `http://localhost:3001/config-logs/${sessionId}` + ); + + if (configLogsResponse.ok) { + const configLogsResult = await configLogsResponse.json(); + + // Update logs if there are new ones + if ( + configLogsResult.logs && + configLogsResult.logs.length > generationLogs.length + ) { + setGenerationLogs(configLogsResult.logs); + } + + // If config task is completed, switch to polling benchmark logs + if (configLogsResult.is_completed) { + // Attendre un court instant pour permettre au serveur de démarrer le benchmark + setTimeout(() => { + console.log( + "Configuration completed, switching to benchmark polling" + ); + clearInterval(configPollingIntervalRef.current); + pollBenchmarkLogs(); + }, 1000); + } + } + } catch (error) { + console.log("Error polling for config logs:", error); + // Don't stop polling on network errors + } + }; + + // Fonction pour interroger les logs du benchmark + const pollBenchmarkLogs = async () => { + // Set up polling for benchmark logs + pollingIntervalRef.current = setInterval(async () => { + // Check if we already completed + if (generationComplete) { + clearInterval(pollingIntervalRef.current); + return; + } + + try { + // Call the API to get the latest benchmark logs + const logsResponse = await fetch( + `http://localhost:3001/benchmark-logs/${sessionId}` + ); + + if (logsResponse.ok) { + const logsResult = await logsResponse.json(); + + // Update logs if there are new ones + if ( + logsResult.logs && + logsResult.logs.length > generationLogs.length + ) { + setGenerationLogs(logsResult.logs); + } + + // Check if the task is completed + if (logsResult.is_completed) { + setGenerationComplete(true); + clearInterval(pollingIntervalRef.current); + // Notification is now handled in the useEffect above + } + } + } catch (error) { + console.log("Error polling for benchmark logs:", error); + // Don't stop polling on network errors + } + }, 3000); // Poll every 3 seconds + }; + + // Démarrer le polling des logs de configuration + const configPollingIntervalRef = { current: null }; + configPollingIntervalRef.current = setInterval(pollConfigLogs, 1000); // Poll config logs more frequently (every second) + } else { + // Handle error + setGenerationLogs([`Error: ${result.error || "Unknown error"}`]); + setError(result.error || "Benchmark generation failed"); + } + } catch (error) { + console.error("Error generating benchmark:", error); + setGenerationLogs([`Error: ${error.message || "Unknown error"}`]); + setError("Server connection error"); + } finally { + setGenerating(false); + } + }; + + // Get title based on current phase + const getPhaseTitle = () => { + switch (currentPhase) { + case "initializing": + return "Benchmark generation..."; + case "configuring": + return "Generating configuration file..."; + case "benchmarking": + return "Creating benchmark..."; + case "complete": + return "Benchmark generated successfully!"; + default: + return "Processing..."; + } + }; + + // Get the current step information for display + const getCurrentStepInfo = () => { + const totalSteps = BENCHMARK_STEPS.length; + const currentStepIndex = activeStep; + + // If there's no active step yet + if (currentStepIndex === 0 && completedSteps.length === 0) { + return `Starting... (0%)`; + } + + // If all steps are completed + if (currentStepIndex >= totalSteps) { + return `Complete (100%)`; + } + + // Calculate percentage + const percentage = Math.round((currentStepIndex / totalSteps) * 100); + + // Get current step name + const currentStepName = + STEP_LABELS[BENCHMARK_STEPS[currentStepIndex]] || "Processing"; + + return `${currentStepName} (${percentage}%)`; + }; + + // Format elapsed time in HH:MM:SS + const formatElapsedTime = () => { + const hours = Math.floor(elapsedTime / 3600); + const minutes = Math.floor((elapsedTime % 3600) / 60); + const seconds = elapsedTime % 60; + + return [ + hours.toString().padStart(2, "0"), + minutes.toString().padStart(2, "0"), + seconds.toString().padStart(2, "0"), + ].join(":"); + }; + + // If complete, stop the timer + useEffect(() => { + if (generationComplete && timerIntervalRef.current) { + clearInterval(timerIntervalRef.current); + } + }, [generationComplete]); + + return ( + + {error ? ( + + {error} + + ) : ( + <> + + + {getPhaseTitle()} + + + {/* Step progress indicator */} + + {getCurrentStepInfo()} + + + {/* Timer display */} + + + {formatElapsedTime()} + + + + )} + + {/* Use the LogDisplay component */} + + + ); +}; + +export default BenchmarkGenerator; diff --git a/frontend/src/components/EvaluationDisplay.jsx b/frontend/src/components/EvaluationDisplay.jsx new file mode 100644 index 0000000000000000000000000000000000000000..45e292879c8564020259febc2c2c0f780f10fa2c --- /dev/null +++ b/frontend/src/components/EvaluationDisplay.jsx @@ -0,0 +1,196 @@ +import React, { useState, useEffect } from "react"; +import { + Box, + Paper, + Typography, + Table, + TableBody, + TableCell, + TableContainer, + TableHead, + TableRow, + Alert, + LinearProgress, + Card, + CardContent, + Link, +} from "@mui/material"; +import OpenInNewIcon from "@mui/icons-material/OpenInNew"; + +const EvaluationDisplay = ({ sessionId }) => { + const [results, setResults] = useState(null); + const [loading, setLoading] = useState(true); + const [error, setError] = useState(null); + + useEffect(() => { + const fetchEvaluationResults = async () => { + if (!sessionId) { + setError("No session ID provided"); + setLoading(false); + return; + } + + try { + // Fetch evaluation results from the API + const response = await fetch( + `http://localhost:3001/evaluation-results/${sessionId}` + ); + + if (!response.ok) { + throw new Error(`Failed to fetch results: ${response.status}`); + } + + const data = await response.json(); + + if (!data.success) { + throw new Error(data.message || "Failed to fetch evaluation results"); + } + + setResults(data.results); + } catch (err) { + console.error("Error fetching evaluation results:", err); + setError(err.message); + } finally { + setLoading(false); + } + }; + + fetchEvaluationResults(); + }, [sessionId]); + + // Format accuracy as percentage + const formatAccuracy = (value) => { + return `${(value * 100).toFixed(2)}%`; + }; + + // Format evaluation time + const formatTime = (seconds) => { + return `${seconds.toFixed(2)}s`; + }; + + if (loading) { + return ( + + + Loading Evaluation Results... + + + + ); + } + + if (error) { + return ( + + {error} + + ); + } + + if ( + !results || + !results.models_comparison || + results.models_comparison.length === 0 + ) { + return ( + + No evaluation results found for this benchmark. + + ); + } + + return ( + + + Evaluation Results + + + + + + + Rank + Model + Provider + Accuracy + Std Error + Eval Time + Status + + + + {results.models_comparison.map((model, index) => ( + + {index + 1} + + + {model.model_name} + + + + {model.provider} + + {model.success ? formatAccuracy(model.accuracy) : "-"} + + + {model.success ? formatAccuracy(model.accuracy_stderr) : "-"} + + + {model.success ? formatTime(model.evaluation_time) : "-"} + + + {model.success ? ( + ✓ Success + ) : ( + ✗ Failed + )} + + + ))} + +
+ + + + + Need larger evaluation?{" "} + + Go to this page + + + + + ); +}; + +export default EvaluationDisplay; diff --git a/frontend/src/components/Footer/Footer.js b/frontend/src/components/Footer/Footer.js new file mode 100644 index 0000000000000000000000000000000000000000..2064e062f55de1cf477fd80211f2bd5d9835fb63 --- /dev/null +++ b/frontend/src/components/Footer/Footer.js @@ -0,0 +1,30 @@ +import React from "react"; +import { Box, Typography, Link } from "@mui/material"; + +const Footer = () => { + return ( + + + © 2024 Hugging Face - Open LLM Leaderboard - Made with 🤗 by the HF team + -{" "} + + huggingface.co + + + + ); +}; + +export default Footer; diff --git a/frontend/src/components/LogDisplay.jsx b/frontend/src/components/LogDisplay.jsx new file mode 100644 index 0000000000000000000000000000000000000000..4c3fa3e689dfa00c1173b11c53133baa645afe00 --- /dev/null +++ b/frontend/src/components/LogDisplay.jsx @@ -0,0 +1,67 @@ +import React, { useRef, useEffect } from "react"; +import { Box, Typography } from "@mui/material"; + +/** + * A reusable component for displaying logs with auto-scrolling and styling + * + * @param {Object} props - Component props + * @param {Array} props.logs - Array of log messages to display + * @param {number} props.height - Height of the log container in pixels (default: 300) + * @param {Object} props.containerStyle - Additional styles for the container + * @returns {JSX.Element} Log display component + */ +const LogDisplay = ({ logs = [], height = 300, containerStyle = {} }) => { + const logsEndRef = useRef(null); + + // Auto-scroll logs to bottom when new logs are added + useEffect(() => { + if (logsEndRef.current) { + logsEndRef.current.scrollIntoView({ behavior: "smooth" }); + } + }, [logs]); + + return ( + + {logs.length === 0 ? ( + + Waiting for logs... + + ) : ( + logs.map((log, index) => { + // Style logs based on content + let style = { opacity: 0.7 }; + if (log.includes("[ERROR]")) { + style = { ...style, color: "#d32f2f" }; // Red for errors + } else if (log.includes("[WARN]")) { + style = { ...style, color: "#ed6c02" }; // Orange for warnings + } else if (log.includes("[SUCCESS]")) { + style = { ...style, color: "#2e7d32", opacity: 0.8 }; // Green for success + } + + return ( +
+ {log} +
+ ); + }) + )} +
+ + ); +}; + +export default LogDisplay; diff --git a/frontend/src/components/Logo/HFLogo.js b/frontend/src/components/Logo/HFLogo.js new file mode 100644 index 0000000000000000000000000000000000000000..e49263da5f52e62f50db806f6f295d94e75be47f --- /dev/null +++ b/frontend/src/components/Logo/HFLogo.js @@ -0,0 +1,19 @@ +import React from 'react'; + +const HFLogo = () => ( + + hg-logo + + +); + +export default HFLogo; \ No newline at end of file diff --git a/frontend/src/components/Logo/Logo.js b/frontend/src/components/Logo/Logo.js new file mode 100644 index 0000000000000000000000000000000000000000..55db4a876d67bdc378ac86c8a5aba2276ff6df33 --- /dev/null +++ b/frontend/src/components/Logo/Logo.js @@ -0,0 +1,56 @@ +import React from "react"; +import { useNavigate, useSearchParams, useLocation } from "react-router-dom"; +import { Box } from "@mui/material"; +import HFLogo from "./HFLogo"; +import { useLeaderboard } from "../../pages/LeaderboardPage/components/Leaderboard/context/LeaderboardContext"; + +const Logo = ({ height = "40px" }) => { + const navigate = useNavigate(); + const [searchParams, setSearchParams] = useSearchParams(); + const location = useLocation(); + const { actions } = useLeaderboard(); + + const handleReset = () => { + // Reset all leaderboard state first + actions.resetAll(); + + // Then clean URL in one go + if ( + location.pathname !== "/" || + searchParams.toString() !== "" || + location.hash !== "" + ) { + window.history.replaceState(null, "", "/"); + navigate("/", { replace: true, state: { skipUrlSync: true } }); + setSearchParams({}, { replace: true, state: { skipUrlSync: true } }); + } + }; + + return ( + + + + + + ); +}; + +export default Logo; diff --git a/frontend/src/components/Navigation/Navigation.js b/frontend/src/components/Navigation/Navigation.js new file mode 100644 index 0000000000000000000000000000000000000000..aef6ce63105663596092a643387c2310f191a575 --- /dev/null +++ b/frontend/src/components/Navigation/Navigation.js @@ -0,0 +1,504 @@ +import React, { useState } from "react"; +import { + AppBar, + Toolbar, + Box, + Link as MuiLink, + IconButton, + Tooltip, + ButtonBase, + Typography, +} from "@mui/material"; +import { useLocation, useNavigate, useSearchParams } from "react-router-dom"; +import OpenInNewIcon from "@mui/icons-material/OpenInNew"; +import LightModeOutlinedIcon from "@mui/icons-material/LightModeOutlined"; +import DarkModeOutlinedIcon from "@mui/icons-material/DarkModeOutlined"; +import { alpha } from "@mui/material/styles"; +import MenuIcon from "@mui/icons-material/Menu"; +import { Menu, MenuItem, useMediaQuery, useTheme } from "@mui/material"; + +const Navigation = ({ onToggleTheme, mode }) => { + const location = useLocation(); + const navigate = useNavigate(); + const [searchParams] = useSearchParams(); + const [anchorEl, setAnchorEl] = useState(null); + const theme = useTheme(); + const isMobile = useMediaQuery(theme.breakpoints.down("md")); + const [hasChanged, setHasChanged] = useState(false); + + const handleThemeToggle = () => { + setHasChanged(true); + onToggleTheme(); + }; + + const iconStyle = { + fontSize: "1.125rem", + ...(hasChanged && { + animation: "rotateIn 0.3s cubic-bezier(0.4, 0, 0.2, 1)", + "@keyframes rotateIn": { + "0%": { + opacity: 0, + transform: + mode === "light" + ? "rotate(-90deg) scale(0.8)" + : "rotate(90deg) scale(0.8)", + }, + "100%": { + opacity: 1, + transform: "rotate(0) scale(1)", + }, + }, + }), + }; + + // Function to sync URL with parent HF page + const syncUrlWithParent = (queryString, hash) => { + // Check if we're in an HF Space iframe + const isHFSpace = window.location !== window.parent.location; + if (isHFSpace) { + try { + // Build complete URL with hash + const fullPath = `${queryString}${hash ? "#" + hash : ""}`; + window.parent.postMessage( + { + type: "urlUpdate", + path: fullPath, + }, + "https://huggingface.co" + ); + } catch (e) { + console.warn("Unable to sync URL with parent:", e); + } + } + }; + + const linkStyle = (isActive = false) => ({ + textDecoration: "none", + color: isActive ? "text.primary" : "text.secondary", + fontSize: "0.8125rem", + opacity: isActive ? 1 : 0.8, + display: "flex", + alignItems: "center", + gap: 0.5, + paddingBottom: "2px", + cursor: "pointer", + position: "relative", + "&:hover": { + opacity: 1, + color: "text.primary", + }, + "&::after": isActive + ? { + content: '""', + position: "absolute", + bottom: "-4px", + left: "0", + width: "100%", + height: "2px", + backgroundColor: (theme) => + alpha( + theme.palette.text.primary, + theme.palette.mode === "dark" ? 0.3 : 0.2 + ), + borderRadius: "2px", + } + : {}, + }); + + const Separator = () => ( + ({ + width: "4px", + height: "4px", + borderRadius: "100%", + backgroundColor: alpha( + theme.palette.text.primary, + theme.palette.mode === "dark" ? 0.2 : 0.15 + ), + })} + /> + ); + + const handleNavigation = (path) => (e) => { + e.preventDefault(); + const searchString = searchParams.toString(); + const queryString = searchString ? `?${searchString}` : ""; + const newPath = `${path}${queryString}`; + + // Local navigation via React Router + navigate(newPath); + + // If in HF Space, sync with parent + if (window.location !== window.parent.location) { + syncUrlWithParent(queryString, newPath); + } + }; + + const handleMenuOpen = (event) => { + setAnchorEl(event.currentTarget); + }; + + const handleMenuClose = () => { + setAnchorEl(null); + }; + + return ( + + + {isMobile ? ( + + + + + + + `1px solid ${alpha(theme.palette.divider, 0.1)}`, + backgroundColor: (theme) => + theme.palette.mode === "dark" + ? alpha(theme.palette.background.paper, 0.8) + : theme.palette.background.paper, + backdropFilter: "blur(20px)", + "& .MuiList-root": { + py: 1, + }, + "& .MuiMenuItem-root": { + px: 2, + py: 1, + fontSize: "0.8125rem", + color: "text.secondary", + transition: "all 0.2s ease-in-out", + position: "relative", + "&:hover": { + backgroundColor: (theme) => + alpha( + theme.palette.text.primary, + theme.palette.mode === "dark" ? 0.1 : 0.06 + ), + color: "text.primary", + }, + "&.Mui-selected": { + backgroundColor: "transparent", + color: "text.primary", + "&::after": { + content: '""', + position: "absolute", + left: "8px", + width: "4px", + height: "100%", + top: "0", + backgroundColor: (theme) => + alpha( + theme.palette.text.primary, + theme.palette.mode === "dark" ? 0.3 : 0.2 + ), + borderRadius: "2px", + }, + "&:hover": { + backgroundColor: (theme) => + alpha( + theme.palette.text.primary, + theme.palette.mode === "dark" ? 0.1 : 0.06 + ), + }, + }, + }, + }, + }} + transformOrigin={{ horizontal: "left", vertical: "top" }} + anchorOrigin={{ horizontal: "left", vertical: "bottom" }} + > + {/* Navigation Section */} + + + Navigation + + + { + handleNavigation("/")(e); + handleMenuClose(); + }} + selected={location.pathname === "/"} + > + Leaderboard + + { + handleNavigation("/add")(e); + handleMenuClose(); + }} + selected={location.pathname === "/add"} + > + Submit model + + { + handleNavigation("/vote")(e); + handleMenuClose(); + }} + selected={location.pathname === "/vote"} + > + Vote for next model + + { + handleNavigation("/quote")(e); + handleMenuClose(); + }} + selected={location.pathname === "/quote"} + > + Citations + + + {/* Separator */} + + `1px solid ${alpha(theme.palette.divider, 0.1)}`, + }} + /> + + {/* External Links Section */} + + + External links + + + + Compare models + + + + About + + + + + + ({ + color: "text.secondary", + borderRadius: "100%", + padding: 0, + width: "36px", + height: "36px", + display: "flex", + alignItems: "center", + justifyContent: "center", + transition: "all 0.2s ease-in-out", + "&:hover": { + color: "text.primary", + backgroundColor: alpha( + theme.palette.text.primary, + theme.palette.mode === "dark" ? 0.1 : 0.06 + ), + }, + "&.MuiButtonBase-root": { + overflow: "hidden", + }, + "& .MuiTouchRipple-root": { + color: alpha(theme.palette.text.primary, 0.3), + }, + })} + > + {mode === "light" ? ( + + ) : ( + + )} + + + + ) : ( + // Desktop version + + {/* Internal navigation */} + + + Leaderboard + + + Submit model + + + Vote for next model + + + Citations + + + + + + {/* External links */} + + + Compare models + + + + About + + + + + + + {/* Dark mode toggle */} + + ({ + color: "text.secondary", + borderRadius: "100%", + padding: 0, + width: "36px", + height: "36px", + display: "flex", + alignItems: "center", + justifyContent: "center", + transition: "all 0.2s ease-in-out", + "&:hover": { + color: "text.primary", + backgroundColor: alpha( + theme.palette.text.primary, + theme.palette.mode === "dark" ? 0.1 : 0.06 + ), + }, + "&.MuiButtonBase-root": { + overflow: "hidden", + }, + "& .MuiTouchRipple-root": { + color: alpha(theme.palette.text.primary, 0.3), + }, + })} + > + {mode === "light" ? ( + + ) : ( + + )} + + + + )} + + + ); +}; + +export default Navigation; diff --git a/frontend/src/components/shared/AuthContainer.js b/frontend/src/components/shared/AuthContainer.js new file mode 100644 index 0000000000000000000000000000000000000000..3b13092498c07beed62bcdafbb05d5b16c45e73a --- /dev/null +++ b/frontend/src/components/shared/AuthContainer.js @@ -0,0 +1,192 @@ +import React, { useEffect } from "react"; +import { + Box, + Typography, + Button, + Chip, + Stack, + Paper, + CircularProgress, + useTheme, + useMediaQuery, +} from "@mui/material"; +import HFLogo from "../Logo/HFLogo"; +import { useAuth } from "../../hooks/useAuth"; +import LogoutIcon from "@mui/icons-material/Logout"; +import { useNavigate } from "react-router-dom"; + +function AuthContainer({ actionText = "DO_ACTION", onSuccess }) { + const { isAuthenticated, user, login, logout, loading } = useAuth(); + const navigate = useNavigate(); + const theme = useTheme(); + const isMobile = useMediaQuery(theme.breakpoints.down("sm")); + + // Trigger onSuccess callback when user is authenticated + useEffect(() => { + if (isAuthenticated && onSuccess) { + // Add a small delay to ensure UI is updated properly + setTimeout(() => { + console.log("User is authenticated, calling onSuccess callback"); + onSuccess(); + }, 100); + } + }, [isAuthenticated, onSuccess]); + + // Check localStorage manually as a fallback + useEffect(() => { + if (!isAuthenticated && !loading && onSuccess) { + const storedAuth = localStorage.getItem("hf_oauth"); + if (storedAuth) { + console.log( + "Found auth data in localStorage but isAuthenticated is false, forcing onSuccess" + ); + onSuccess(); + } + } + }, [isAuthenticated, loading, onSuccess]); + + const handleLogout = () => { + if (isAuthenticated && logout) { + logout(); + navigate("/", { replace: true }); + window.location.reload(); + } + }; + + if (loading) { + return ( + + + + ); + } + + if (!isAuthenticated) { + return ( + + + Login to {actionText} + + + You need to be logged in with your Hugging Face account to{" "} + {actionText.toLowerCase()} + + + + ); + } + + return ( + + + + + Connected as {user?.username} + + + + + + + ); +} + +export default AuthContainer; diff --git a/frontend/src/components/shared/CodeBlock.js b/frontend/src/components/shared/CodeBlock.js new file mode 100644 index 0000000000000000000000000000000000000000..6f06f6eed1f6a17dd70334d3a7bb4d0ab897355c --- /dev/null +++ b/frontend/src/components/shared/CodeBlock.js @@ -0,0 +1,37 @@ +import React from 'react'; +import { Box, IconButton } from '@mui/material'; +import ContentCopyIcon from '@mui/icons-material/ContentCopy'; + +const CodeBlock = ({ code }) => ( + + navigator.clipboard.writeText(code)} + sx={{ + position: 'absolute', + top: 8, + right: 8, + color: 'grey.500', + '&:hover': { color: 'grey.300' }, + }} + > + + + + {code} + + +); + +export default CodeBlock; \ No newline at end of file diff --git a/frontend/src/components/shared/FilterTag.js b/frontend/src/components/shared/FilterTag.js new file mode 100644 index 0000000000000000000000000000000000000000..3cd154cb61a699bf94a2af0ba78286e3588aa754 --- /dev/null +++ b/frontend/src/components/shared/FilterTag.js @@ -0,0 +1,139 @@ +import React from "react"; +import { Chip } from "@mui/material"; +import { useTheme } from "@mui/material/styles"; +import { alpha } from "@mui/material/styles"; +import CheckBoxOutlineBlankIcon from "@mui/icons-material/CheckBoxOutlineBlank"; +import CheckBoxOutlinedIcon from "@mui/icons-material/CheckBoxOutlined"; + +const FilterTag = ({ + label, + checked, + onChange, + count, + isHideFilter = false, + totalCount = 0, + variant = "tag", + showCheckbox = false, + stacked = false, + sx = {}, +}) => { + const theme = useTheme(); + + const formatCount = (count) => { + if (count === undefined) return ""; + return `${count}`; + }; + + const mainLabel = label; + const countLabel = count !== undefined ? formatCount(count) : ""; + + return ( + + ) : ( + + ) + ) : null + } + label={ + + {mainLabel} + {countLabel && ( + <> + + {countLabel} + + )} + + } + onClick={onChange} + variant="outlined" + color={ + checked + ? variant === "secondary" + ? "secondary" + : "primary" + : "default" + } + size="small" + data-checked={checked} + sx={{ + height: "32px", + fontWeight: 600, + opacity: checked ? 1 : 0.8, + borderRadius: "5px", + borderWidth: "1px", + borderStyle: "solid", + cursor: "pointer", + pl: showCheckbox ? 0.5 : 0, + mr: 0.5, + mb: 0.5, + transition: "opacity 0.2s ease, border-color 0.2s ease", + "& .MuiChip-label": { + px: 0.75, + pl: showCheckbox ? 0.6 : 0.75, + }, + "& .MuiChip-icon": { + mr: 0.5, + pl: 0.2, + }, + "&:hover": { + opacity: 1, + backgroundColor: checked + ? alpha( + theme.palette[variant === "secondary" ? "secondary" : "primary"] + .main, + theme.palette.mode === "light" ? 0.08 : 0.16 + ) + : "action.hover", + borderWidth: "1px", + }, + backgroundColor: checked + ? alpha( + theme.palette[variant === "secondary" ? "secondary" : "primary"] + .main, + theme.palette.mode === "light" ? 0.08 : 0.16 + ) + : "background.paper", + borderColor: checked + ? variant === "secondary" + ? "secondary.main" + : "primary.main" + : "divider", + ...sx, + }} + /> + ); +}; + +export default FilterTag; diff --git a/frontend/src/components/shared/InfoIconWithTooltip.js b/frontend/src/components/shared/InfoIconWithTooltip.js new file mode 100644 index 0000000000000000000000000000000000000000..2b307ccaf8d7bebb91c81b2ff7cf746a4fbac05e --- /dev/null +++ b/frontend/src/components/shared/InfoIconWithTooltip.js @@ -0,0 +1,87 @@ +import React from "react"; +import { Box, Tooltip, Portal, Backdrop } from "@mui/material"; +import InfoOutlinedIcon from "@mui/icons-material/InfoOutlined"; + +const InfoIconWithTooltip = ({ tooltip, iconProps = {}, sx = {} }) => { + const [open, setOpen] = React.useState(false); + + return ( + <> + setOpen(true)} + onClose={() => setOpen(false)} + componentsProps={{ + tooltip: { + sx: { + bgcolor: "rgba(33, 33, 33, 0.95)", + padding: "12px 16px", + maxWidth: "none !important", + width: "auto", + minWidth: "200px", + fontSize: "0.875rem", + lineHeight: 1.5, + position: "relative", + zIndex: 1501, + "& .MuiTooltip-arrow": { + color: "rgba(33, 33, 33, 0.95)", + }, + }, + }, + popper: { + sx: { + zIndex: 1501, + maxWidth: "min(600px, 90vw) !important", + '&[data-popper-placement*="bottom"] .MuiTooltip-tooltip': { + marginTop: "10px", + }, + '&[data-popper-placement*="top"] .MuiTooltip-tooltip': { + marginBottom: "10px", + }, + }, + }, + }} + > + + + + + {open && ( + + + + )} + + ); +}; + +export default InfoIconWithTooltip; diff --git a/frontend/src/components/shared/PageHeader.js b/frontend/src/components/shared/PageHeader.js new file mode 100644 index 0000000000000000000000000000000000000000..5aada38223f216bc7df8c65725dda552947e757d --- /dev/null +++ b/frontend/src/components/shared/PageHeader.js @@ -0,0 +1,29 @@ +import React from "react"; +import { Box, Typography } from "@mui/material"; + +const PageHeader = ({ title, subtitle }) => { + return ( + + + {title} + + {subtitle && ( + + {subtitle} + + )} + + ); +}; + +export default PageHeader; diff --git a/frontend/src/config/auth.js b/frontend/src/config/auth.js new file mode 100644 index 0000000000000000000000000000000000000000..080186d5c84067380607de10b7345157e5e3a726 --- /dev/null +++ b/frontend/src/config/auth.js @@ -0,0 +1,7 @@ +export const HF_CONFIG = { + CLIENT_ID: "a8077b24-00b4-42b9-8202-75e72e3b86b1", + STORAGE_KEY: "hf_oauth", + SCOPE: "openid profile", + PROD_URL: "https://yourbench-yourbench-simple-demo.hf.space", + DEV_URL: window.location.origin, +}; diff --git a/frontend/src/config/theme.js b/frontend/src/config/theme.js new file mode 100644 index 0000000000000000000000000000000000000000..4bd6e4ae0ac0810a89f7aafb480b3b12fbe0f524 --- /dev/null +++ b/frontend/src/config/theme.js @@ -0,0 +1,390 @@ +import { createTheme, alpha } from "@mui/material/styles"; + +const getDesignTokens = (mode) => ({ + typography: { + fontFamily: [ + "-apple-system", + "BlinkMacSystemFont", + '"Segoe UI"', + "Roboto", + '"Helvetica Neue"', + "Arial", + "sans-serif", + ].join(","), + h1: { + fontFamily: '"Source Sans Pro", sans-serif', + }, + h2: { + fontFamily: '"Source Sans Pro", sans-serif', + }, + h3: { + fontFamily: '"Source Sans Pro", sans-serif', + }, + h4: { + fontFamily: '"Source Sans Pro", sans-serif', + }, + h5: { + fontFamily: '"Source Sans Pro", sans-serif', + }, + h6: { + fontFamily: '"Source Sans Pro", sans-serif', + }, + subtitle1: { + fontFamily: '"Source Sans Pro", sans-serif', + }, + subtitle2: { + fontFamily: '"Source Sans Pro", sans-serif', + }, + }, + palette: { + mode, + primary: { + main: "#4F86C6", + light: mode === "light" ? "#7BA7D7" : "#6B97D7", + dark: mode === "light" ? "#2B5C94" : "#3B6CA4", + 50: mode === "light" ? alpha("#4F86C6", 0.05) : alpha("#4F86C6", 0.15), + 100: mode === "light" ? alpha("#4F86C6", 0.1) : alpha("#4F86C6", 0.2), + 200: mode === "light" ? alpha("#4F86C6", 0.2) : alpha("#4F86C6", 0.3), + contrastText: "#fff", + }, + background: { + default: mode === "light" ? "#f8f9fa" : "#0a0a0a", + paper: mode === "light" ? "#fff" : "#1a1a1a", + subtle: mode === "light" ? "grey.100" : "grey.900", + hover: mode === "light" ? "action.hover" : alpha("#fff", 0.08), + tooltip: mode === "light" ? alpha("#212121", 0.9) : alpha("#fff", 0.9), + }, + text: { + primary: mode === "light" ? "rgba(0, 0, 0, 0.87)" : "#fff", + secondary: + mode === "light" ? "rgba(0, 0, 0, 0.6)" : "rgba(255, 255, 255, 0.7)", + disabled: + mode === "light" ? "rgba(0, 0, 0, 0.38)" : "rgba(255, 255, 255, 0.5)", + hint: + mode === "light" ? "rgba(0, 0, 0, 0.38)" : "rgba(255, 255, 255, 0.5)", + }, + divider: + mode === "light" ? "rgba(0, 0, 0, 0.12)" : "rgba(255, 255, 255, 0.12)", + action: { + active: + mode === "light" ? "rgba(0, 0, 0, 0.54)" : "rgba(255, 255, 255, 0.7)", + hover: + mode === "light" ? "rgba(0, 0, 0, 0.04)" : "rgba(255, 255, 255, 0.08)", + selected: + mode === "light" ? "rgba(0, 0, 0, 0.08)" : "rgba(255, 255, 255, 0.16)", + disabled: + mode === "light" ? "rgba(0, 0, 0, 0.26)" : "rgba(255, 255, 255, 0.3)", + disabledBackground: + mode === "light" ? "rgba(0, 0, 0, 0.12)" : "rgba(255, 255, 255, 0.12)", + }, + }, + shape: { + borderRadius: 8, + }, + components: { + MuiCssBaseline: { + styleOverrides: { + "html, body": { + backgroundColor: "background.default", + color: mode === "dark" ? "#fff" : "#000", + }, + body: { + "& *::-webkit-scrollbar": { + width: 8, + height: 8, + backgroundColor: "transparent", + }, + "& *::-webkit-scrollbar-thumb": { + borderRadius: 8, + backgroundColor: + mode === "light" ? alpha("#000", 0.2) : alpha("#fff", 0.1), + "&:hover": { + backgroundColor: + mode === "light" ? alpha("#000", 0.3) : alpha("#fff", 0.15), + }, + }, + }, + }, + }, + MuiButton: { + styleOverrides: { + root: { + borderRadius: 8, + }, + }, + }, + MuiPaper: { + defaultProps: { + elevation: 0, + }, + styleOverrides: { + root: { + backgroundImage: "none", + boxShadow: "none", + border: "1px solid", + borderColor: + mode === "light" + ? "rgba(0, 0, 0, 0.12)!important" + : "rgba(255, 255, 255, 0.25)!important", + }, + rounded: { + borderRadius: 12, + }, + }, + }, + + MuiTableCell: { + styleOverrides: { + root: { + borderColor: (theme) => + alpha( + theme.palette.divider, + theme.palette.mode === "dark" ? 0.1 : 0.2 + ), + }, + head: { + backgroundColor: mode === "light" ? "grey.50" : "grey.900", + color: "text.primary", + fontWeight: 600, + }, + }, + }, + MuiTableRow: { + styleOverrides: { + root: { + backgroundColor: "transparent", + }, + }, + }, + MuiTableContainer: { + styleOverrides: { + root: { + backgroundColor: "background.paper", + borderRadius: 8, + border: "none", + boxShadow: "none", + }, + }, + }, + MuiSlider: { + styleOverrides: { + root: { + "& .MuiSlider-valueLabel": { + backgroundColor: "background.paper", + color: "text.primary", + border: "1px solid", + borderColor: "divider", + boxShadow: + mode === "light" + ? "0px 2px 4px rgba(0, 0, 0, 0.1)" + : "0px 2px 4px rgba(0, 0, 0, 0.3)", + }, + }, + thumb: { + "&:hover": { + boxShadow: (theme) => + `0px 0px 0px 8px ${alpha( + theme.palette.primary.main, + mode === "light" ? 0.08 : 0.16 + )}`, + }, + "&.Mui-active": { + boxShadow: (theme) => + `0px 0px 0px 12px ${alpha( + theme.palette.primary.main, + mode === "light" ? 0.08 : 0.16 + )}`, + }, + }, + track: { + border: "none", + }, + rail: { + opacity: mode === "light" ? 0.38 : 0.3, + }, + mark: { + backgroundColor: mode === "light" ? "grey.400" : "grey.600", + }, + markLabel: { + color: "text.secondary", + }, + }, + }, + MuiTextField: { + styleOverrides: { + root: { + "& .MuiOutlinedInput-root": { + borderRadius: 8, + }, + }, + }, + }, + MuiChip: { + styleOverrides: { + root: { + borderRadius: 8, + }, + outlinedInfo: { + borderWidth: 2, + fontWeight: 600, + bgcolor: "info.100", + borderColor: "info.400", + color: "info.700", + "& .MuiChip-label": { + px: 1.2, + }, + "&:hover": { + bgcolor: "info.200", + }, + }, + outlinedWarning: { + borderWidth: 2, + fontWeight: 600, + bgcolor: "warning.100", + borderColor: "warning.400", + color: "warning.700", + "& .MuiChip-label": { + px: 1.2, + }, + "&:hover": { + bgcolor: "warning.200", + }, + }, + outlinedSuccess: { + borderWidth: 2, + fontWeight: 600, + bgcolor: "success.100", + borderColor: "success.400", + color: "success.700", + "& .MuiChip-label": { + px: 1.2, + }, + "&:hover": { + bgcolor: "success.200", + }, + }, + outlinedError: { + borderWidth: 2, + fontWeight: 600, + bgcolor: "error.100", + borderColor: "error.400", + color: "error.700", + "& .MuiChip-label": { + px: 1.2, + }, + "&:hover": { + bgcolor: "error.200", + }, + }, + outlinedPrimary: { + borderWidth: 2, + fontWeight: 600, + bgcolor: "primary.100", + borderColor: "primary.400", + color: "primary.700", + "& .MuiChip-label": { + px: 1.2, + }, + "&:hover": { + bgcolor: "primary.200", + }, + }, + outlinedSecondary: { + borderWidth: 2, + fontWeight: 600, + bgcolor: "secondary.100", + borderColor: "secondary.400", + color: "secondary.700", + "& .MuiChip-label": { + px: 1.2, + }, + "&:hover": { + bgcolor: "secondary.200", + }, + }, + }, + }, + MuiIconButton: { + styleOverrides: { + root: { + borderRadius: 8, + padding: "8px", + "&.MuiIconButton-sizeSmall": { + padding: "4px", + borderRadius: 6, + }, + }, + }, + }, + MuiTooltip: { + styleOverrides: { + tooltip: { + backgroundColor: + mode === "light" ? alpha("#212121", 0.9) : alpha("#424242", 0.9), + color: "#fff", + fontSize: "0.875rem", + padding: "8px 12px", + maxWidth: 400, + borderRadius: 8, + lineHeight: 1.4, + border: "1px solid", + borderColor: + mode === "light" ? alpha("#fff", 0.1) : alpha("#fff", 0.05), + boxShadow: + mode === "light" + ? "0 2px 8px rgba(0, 0, 0, 0.15)" + : "0 2px 8px rgba(0, 0, 0, 0.5)", + "& b": { + fontWeight: 600, + color: "inherit", + }, + "& a": { + color: mode === "light" ? "#90caf9" : "#64b5f6", + textDecoration: "none", + "&:hover": { + textDecoration: "underline", + }, + }, + }, + arrow: { + color: + mode === "light" ? alpha("#212121", 0.9) : alpha("#424242", 0.9), + "&:before": { + border: "1px solid", + borderColor: + mode === "light" ? alpha("#fff", 0.1) : alpha("#fff", 0.05), + }, + }, + }, + defaultProps: { + arrow: true, + enterDelay: 400, + leaveDelay: 200, + }, + }, + MuiAppBar: { + styleOverrides: { + root: { + border: "none", + borderBottom: "none", + }, + }, + }, + }, + breakpoints: { + values: { + xs: 0, + sm: 600, + md: 900, + lg: 1240, + xl: 1536, + }, + }, +}); + +const getTheme = (mode) => { + const tokens = getDesignTokens(mode); + return createTheme(tokens); +}; + +export default getTheme; diff --git a/frontend/src/hooks/useAuth.js b/frontend/src/hooks/useAuth.js new file mode 100644 index 0000000000000000000000000000000000000000..1c77782bfe19a28d10f7828b82bc1dea09f19b75 --- /dev/null +++ b/frontend/src/hooks/useAuth.js @@ -0,0 +1,214 @@ +import { useState, useEffect } from "react"; +import { useLocation, useNavigate } from "react-router-dom"; +import { oauthLoginUrl, oauthHandleRedirectIfPresent } from "@huggingface/hub"; +import { HF_CONFIG } from "../config/auth"; + +async function fetchUserInfo(token) { + try { + const response = await fetch("https://huggingface.co/api/whoami-v2", { + headers: { + Authorization: `Bearer ${token}`, + }, + }); + if (!response.ok) { + throw new Error(`Failed to fetch user info: ${response.status}`); + } + return response.json(); + } catch (error) { + console.error("Error fetching user info:", error); + throw error; + } +} + +export function useAuth() { + const [isAuthenticated, setIsAuthenticated] = useState(false); + const [user, setUser] = useState(null); + const [loading, setLoading] = useState(true); + const [error, setError] = useState(null); + const location = useLocation(); + const navigate = useNavigate(); + + // Initialisation de l'authentification + useEffect(() => { + let mounted = true; + const initAuth = async () => { + try { + console.group("Auth Initialization"); + setLoading(true); + + // Vérifier s'il y a une redirection OAuth d'abord + let oauthResult; + try { + oauthResult = await oauthHandleRedirectIfPresent(); + console.log("OAuth redirect handled successfully"); + } catch (oauthError) { + console.error("OAuth redirect handling error:", oauthError); + + // If the error is invalid_grant, this might be a refresh token issue + // The user might already be logged in, so let's check localStorage + if ( + oauthError.message && + oauthError.message.includes("invalid_grant") + ) { + console.log( + "Invalid grant error detected - checking local storage for existing auth" + ); + } else { + // For other errors, clear auth data + localStorage.removeItem(HF_CONFIG.STORAGE_KEY); + if (mounted) { + setIsAuthenticated(false); + setUser(null); + setError(oauthError.message); + setLoading(false); + } + console.groupEnd(); + return; + } + } + + // Si pas de redirection ou erreur invalid_grant, vérifier le localStorage + if (!oauthResult) { + const storedAuth = localStorage.getItem(HF_CONFIG.STORAGE_KEY); + if (storedAuth) { + try { + const parsedAuth = JSON.parse(storedAuth); + console.log("Found existing auth"); + + // Vérifier si le token est valide en appelant l'API + try { + const userInfo = await fetchUserInfo(parsedAuth.access_token); + if (mounted) { + setIsAuthenticated(true); + setUser({ + username: userInfo.name, + token: parsedAuth.access_token, + }); + console.log("Successfully authenticated from stored token"); + } + } catch (tokenError) { + console.error("Stored token validation failed:", tokenError); + localStorage.removeItem(HF_CONFIG.STORAGE_KEY); + if (mounted) { + setIsAuthenticated(false); + setUser(null); + } + } + } catch (err) { + console.log("Invalid stored auth data, clearing...", err); + localStorage.removeItem(HF_CONFIG.STORAGE_KEY); + if (mounted) { + setIsAuthenticated(false); + setUser(null); + } + } + } + } else { + console.log("Processing OAuth redirect"); + const token = oauthResult.accessToken; + const userInfo = await fetchUserInfo(token); + + const authData = { + access_token: token, + username: userInfo.name, + }; + + localStorage.setItem(HF_CONFIG.STORAGE_KEY, JSON.stringify(authData)); + + if (mounted) { + setIsAuthenticated(true); + setUser({ + username: userInfo.name, + token: token, + }); + } + + // Rediriger vers la page d'origine + const returnTo = localStorage.getItem("auth_return_to"); + if (returnTo) { + navigate(returnTo); + localStorage.removeItem("auth_return_to"); + } + } + } catch (err) { + console.error("Auth initialization error:", err); + if (mounted) { + setError(err.message); + setIsAuthenticated(false); + setUser(null); + } + } finally { + if (mounted) { + setLoading(false); + } + console.groupEnd(); + } + }; + + initAuth(); + + return () => { + mounted = false; + }; + }, [navigate, location.pathname]); + + const login = async () => { + try { + console.group("Login Process"); + setLoading(true); + setError(null); // Clear any previous errors + + // Sauvegarder la route actuelle pour la redirection post-auth + const currentRoute = window.location.hash.replace("#", "") || "/"; + localStorage.setItem("auth_return_to", currentRoute); + + // Déterminer l'URL de redirection en fonction de l'environnement + const redirectUrl = HF_CONFIG.DEV_URL; + console.log("Using redirect URL:", redirectUrl); + + // Générer l'URL de login et rediriger + const loginUrl = await oauthLoginUrl({ + clientId: HF_CONFIG.CLIENT_ID, + redirectUrl, + scope: HF_CONFIG.SCOPE, + }); + + window.location.href = loginUrl + "&prompt=consent"; + + console.groupEnd(); + } catch (err) { + console.error("Login error:", err); + setError(err.message); + setLoading(false); + console.groupEnd(); + } + }; + + const logout = () => { + console.group("Logout Process"); + setLoading(true); + try { + console.log("Clearing auth data..."); + localStorage.removeItem(HF_CONFIG.STORAGE_KEY); + localStorage.removeItem("auth_return_to"); + setIsAuthenticated(false); + setUser(null); + console.log("Logged out successfully"); + } catch (err) { + console.error("Logout error:", err); + setError(err.message); + } finally { + setLoading(false); + console.groupEnd(); + } + }; + + return { + isAuthenticated, + user, + loading, + error, + login, + logout, + }; +} diff --git a/frontend/src/hooks/useDevShortcuts.js b/frontend/src/hooks/useDevShortcuts.js new file mode 100644 index 0000000000000000000000000000000000000000..09a018b34980dd6dfed1c837364ace8b7899d918 --- /dev/null +++ b/frontend/src/hooks/useDevShortcuts.js @@ -0,0 +1,41 @@ +import { useEffect } from "react"; +import { useNavigate } from "react-router-dom"; + +/** + * Hook pour les raccourcis clavier du mode développeur + * + * @param {Object} options - Options pour le hook + * @param {string} options.sessionId - ID de session en cours (si disponible) + * @returns {void} + */ +const useDevShortcuts = ({ sessionId = null } = {}) => { + const navigate = useNavigate(); + + useEffect(() => { + const handleKeyDown = (e) => { + // Raccourci 'p' - effacer les données d'authentification et recharger + if (e.key === "p") { + console.log("Debug key pressed: Clearing auth data and refreshing"); + localStorage.removeItem("hf_oauth"); + localStorage.removeItem("auth_return_to"); + // Afficher un bref message + alert("Auth data cleared. Page will reload."); + // Recharger la page + window.location.reload(); + } + + // Raccourci 'd' - aller directement à l'affichage du benchmark + if (e.key === "d" && sessionId) { + console.log("Debug key pressed: Showing BenchmarkDisplay"); + navigate(`/benchmark-display?session=${sessionId}`); + } + }; + + window.addEventListener("keydown", handleKeyDown); + return () => { + window.removeEventListener("keydown", handleKeyDown); + }; + }, [sessionId, navigate]); +}; + +export default useDevShortcuts; diff --git a/frontend/src/hooks/useThemeMode.js b/frontend/src/hooks/useThemeMode.js new file mode 100644 index 0000000000000000000000000000000000000000..93030109e2b32281c05178cc4207cb5544e94e4f --- /dev/null +++ b/frontend/src/hooks/useThemeMode.js @@ -0,0 +1,28 @@ +import { useState, useEffect } from 'react'; + +export const useThemeMode = () => { + // Get system preference + const getSystemPreference = () => { + return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light'; + }; + + // Initialize theme mode from system preference + const [mode, setMode] = useState(getSystemPreference); + + // Listen to system preference changes + useEffect(() => { + const mediaQuery = window.matchMedia('(prefers-color-scheme: dark)'); + const handleChange = (e) => { + setMode(e.matches ? 'dark' : 'light'); + }; + + mediaQuery.addEventListener('change', handleChange); + return () => mediaQuery.removeEventListener('change', handleChange); + }, []); + + const toggleTheme = () => { + setMode((prevMode) => (prevMode === 'light' ? 'dark' : 'light')); + }; + + return { mode, toggleTheme }; +}; \ No newline at end of file diff --git a/frontend/src/index.js b/frontend/src/index.js new file mode 100644 index 0000000000000000000000000000000000000000..71a354b9f436d2c989309c53cc08af97c877a022 --- /dev/null +++ b/frontend/src/index.js @@ -0,0 +1,6 @@ +import React from "react"; +import ReactDOM from "react-dom/client"; +import App from "./App"; + +const root = ReactDOM.createRoot(document.getElementById("root")); +root.render();