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import os
import io
from io import BytesIO
from fastapi import FastAPI, UploadFile, File, Form
from fastapi.responses import JSONResponse, HTMLResponse
from fastapi.middleware.cors import CORSMiddleware
from fastapi.staticfiles import StaticFiles
from huggingface_hub import InferenceClient, login
from PyPDF2 import PdfReader
from docx import Document
from PIL import Image
from routers import ai # conservez vos routes annexes
# ──────────────────────────────────────────────────────────────────────────────
# 1) Authentification Hugging Face
# ──────────────────────────────────────────────────────────────────────────────
HF_TOKEN = os.getenv("HF_TOKEN") or os.getenv("HUGGINGFACE_HUB_TOKEN")
if not HF_TOKEN:
raise RuntimeError(
"Variable d'environnement HF_TOKEN absente ; créez un jeton « Read » "
"sur https://huggingface.co/settings/tokens et exportez-le (voir .env)."
)
login(token=HF_TOKEN) # Authentifie tout le process
# ──────────────────────────────────────────────────────────────────────────────
# 2) Configuration FastAPI
# ──────────────────────────────────────────────────────────────────────────────
PORT = int(os.getenv("PORT", 7860))
app = FastAPI(
title="AI Web App API",
description="Backend API for AI-powered web application",
version="1.0.0"
)
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
app.mount("/", StaticFiles(directory=".", html=True), name="static")
app.include_router(ai.router)
# Clients HF (token passé implicitement)
summary_client = InferenceClient("facebook/bart-large-cnn")
qa_client = InferenceClient("deepset/roberta-base-squad2")
image_caption_client = InferenceClient("nlpconnect/vit-gpt2-image-captioning")
# ──────────────────────────────────────────────────────────────────────────────
# 3) Utils : extraction texte, routes API (inchangés ou presque)
# ──────────────────────────────────────────────────────────────────────────────
def extract_text_from_pdf(content: bytes) -> str:
reader = PdfReader(io.BytesIO(content))
return "\n".join(p.extract_text() or "" for p in reader.pages).strip()
def extract_text_from_docx(content: bytes) -> str:
doc = Document(io.BytesIO(content))
return "\n".join(p.text for p in doc.paragraphs).strip()
def process_uploaded_file(file: UploadFile) -> str:
content = file.file.read()
ext = file.filename.rsplit(".", 1)[-1].lower()
if ext == "pdf":
return extract_text_from_pdf(content)
if ext == "docx":
return extract_text_from_docx(content)
if ext == "txt":
return content.decode("utf-8").strip()
raise ValueError("Type de fichier non supporté")
# … (gardez vos trois routes /analyze, /ask, /interpret_image identiques)
@app.get("/api/health")
async def health_check():
return {"status": "healthy", "version": "1.0.0", "hf_token_set": True}
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=PORT)
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