app.py

import os
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

app = FastAPI()

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

class PromptRequest(BaseModel):
    prompt: str

# Path to model folder inside the Space
MODEL_PATH = "./"

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, use_fast=False)
model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_PATH)

@app.get("/")
async def health_check():
    return {"status": "healthy", "message": "API is running"}

@app.post("/predict")
async def predict(request: PromptRequest):
    inputs = tokenizer(request.prompt, return_tensors="pt", truncation=True, padding=True)
    outputs = model.generate(**inputs, max_new_tokens=256)
    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return {"result": result}

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=7860)