app.py

from fastapi import FastAPI, HTTPException
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=False,
    allow_methods=["*"],
    allow_headers=["*"]
)

# Load FLAN-T5 model and tokenizer
model_name = "google/flan-t5-base"  # or use "flan-t5-large" if space/resources allow
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

class QueryRequest(BaseModel):
    query: str
    echo: bool = False

@app.post("/api/query")
async def generate_response(req: QueryRequest):
    query = req.query.strip()
    if not query:
        raise HTTPException(status_code=400, detail="Query must not be empty")
    
    if req.echo:
        return {"response": query}
    
    inputs = tokenizer(query, return_tensors="pt", truncation=True)
    outputs = model.generate(**inputs, max_new_tokens=200)
    generated = tokenizer.decode(outputs[0], skip_special_tokens=True)

    return {"response": generated}