app.py

from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
from fastapi.staticfiles import StaticFiles
from huggingface_hub import InferenceClient
from sentence_transformers import SentenceTransformer

from fastapi import Request
import requests
import numpy as np
import argparse
import os

HOST = os.environ.get("API_URL", "0.0.0.0")
PORT = os.environ.get("PORT", 7860)
parser = argparse.ArgumentParser()
parser.add_argument("--host", default=HOST)
parser.add_argument("--port", type=int, default=PORT)
parser.add_argument("--reload", action="store_true", default=True)
parser.add_argument("--ssl_certfile")
parser.add_argument("--ssl_keyfile")
args = parser.parse_args()

app = FastAPI()
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)


app = FastAPI()
embedder = SentenceTransformer('sentence-transformers/distiluse-base-multilingual-cased-v1')

@app.post("/api/embed")
async def embed(request: Request):
    data = await request.json()
    texts = data.get("texts", [])
    embeddings = embedder.encode(texts)
    return {"embeddings": embeddings.tolist()}

@app.get("/invert")
async def invert(text: str):
    return {
        "original": text,
        "inverted": text[::-1],
    }

HF_TOKEN = os.getenv("REACT_APP_HF_TOKEN")
if not HF_TOKEN:
    raise RuntimeError("Le token Hugging Face (HF_TOKEN) n'est pas défini dans les variables d'environnement.")

# Initialisation du client HF
hf_client = InferenceClient(
    provider="novita",
    api_key=HF_TOKEN,
)

@app.post("/api/chat")
async def chat(request: Request):
    data = await request.json()
    user_message = data.get("message", "").strip()
    if not user_message:
        raise HTTPException(status_code=400, detail="Le champ 'message' est requis.")

    try:
        # Appel au modèle en mode chat
        completion = hf_client.chat.completions.create(
            model="mistralai/Mistral-7B-Instruct-v0.3",
            messages=[
                {"role": "system", "content": "Tu es un assistant médical spécialisé en schizophrénie."},
                {"role": "user",   "content": user_message}
            ],
            max_tokens=512,
            temperature=0.7,
        )

        bot_msg = completion.choices[0].message.content
        return {"response": bot_msg}

    except Exception as e:
        # En cas d'erreur d'inférence
        raise HTTPException(status_code=502, detail=f"Erreur d'inférence HF : {e}")


@app.get("/data")
async def get_data():
    data = {"data": np.random.rand(100).tolist()}
    return JSONResponse(data)


app.mount("/", StaticFiles(directory="static", html=True), name="static")

if __name__ == "__main__":
    import uvicorn

    print(args)
    uvicorn.run(
        "app:app",
        host=args.host,
        port=args.port,
        reload=args.reload,
        ssl_certfile=args.ssl_certfile,
        ssl_keyfile=args.ssl_keyfile,
    )