app.py

from fastapi import FastAPI, Request
from fastapi.responses import StreamingResponse
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from huggingface_hub import InferenceClient
import os

HF_TOKEN = os.getenv("HF_TOKEN")
MODEL_ID = "google/gemma-2b-it"
client = InferenceClient(token=HF_TOKEN)

app = FastAPI()

# Allow CORS for all origins (for development and Netlify)
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # For production, specify frontend domain
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

class ChatRequest(BaseModel):
    message: str

# @app.post("/chat")
# async def chat(req: ChatRequest):
#     try:
#         messages = [{"role": "user", "content": req.message}]
#         response = client.chat_completion(
#             model=MODEL_ID,
#             messages=messages,
#             temperature=0.7,
#         )
#         return {"response": response.choices[0].message.content}
#     except Exception as e:
#         return {"error": str(e)}


async def chat_endpoint(data: ChatRequest):
    def stream():
        for chunk in client.text_generation(
            model=MODEL_ID,
            prompt=data.message,
            stream=True,
            max_new_tokens=512,
            temperature=0.7,
        ):
            yield chunk

    return StreamingResponse(stream(), media_type="text/plain")