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Create app.py
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app.py
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from fastapi import FastAPI, HTTPException
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from fastapi.staticfiles import StaticFiles
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from fastapi.responses import FileResponse
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from pydantic import BaseModel
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import os
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from huggingface_hub import HfApi, hf_hub_download
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import json
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app = FastAPI()
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# ⚠️ REMPLACE PAR LE CHEMIN DE TON DATASET
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DATASET_REPO_ID = "Javare/Local_AI_Leaderboard"
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FILENAME = "scores.json"
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HF_TOKEN = os.environ.get("HF_TOKEN")
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class ScoreEntry(BaseModel):
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config: str
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browser: str
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power: str
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score: float
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def get_scores():
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try:
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# Télécharge le fichier de scores depuis le dataset Hugging Face
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path = hf_hub_download(repo_id=DATASET_REPO_ID, filename=FILENAME, repo_type="dataset", token=HF_TOKEN)
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with open(path, "r", encoding="utf-8") as f:
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return json.load(f)
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except Exception:
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# Si le fichier n'existe pas encore (premier lancement), on démarre à vide
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return []
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@app.get("/api/scores")
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def read_scores():
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scores = get_scores()
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scores.sort(key=lambda x: x["score"], reverse=True)
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return scores[:10] # Renvoie uniquement le TOP 10
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@app.post("/api/score")
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def add_score(entry: ScoreEntry):
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if not HF_TOKEN:
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raise HTTPException(status_code=500, detail="HF_TOKEN manquant dans les Secrets du Space")
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scores = get_scores()
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scores.append(entry.dict())
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# Sauvegarde locale temporaire du JSON complet
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local_path = "scores.json"
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with open(local_path, "w", encoding="utf-8") as f:
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json.dump(scores, f, ensure_ascii=False, indent=2)
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# Envoi sécurisé vers ton Dataset Hugging Face
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api = HfApi()
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try:
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api.upload_file(
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path_or_fileobj=local_path,
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path_in_repo=FILENAME,
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repo_id=DATASET_REPO_ID,
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repo_type="dataset",
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token=HF_TOKEN
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)
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"Erreur de synchronisation : {str(e)}")
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return {"status": "success"}
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# Déclare l'accès aux fichiers statiques de ton interface
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@app.get("/")
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def read_index():
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return FileResponse("index.html")
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app.mount("/assets", StaticFiles(directory="assets"), name="assets")
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