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()

# Enable CORS for frontend fetch requests
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=False,
    allow_methods=["*"],
    allow_headers=["*"]
)

# Load FLAN-T5 model
model_name = "google/flan-t5-base"
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}
    
    # Encode input
    inputs = tokenizer(query, return_tensors="pt", truncation=True)

    # Generate response with better decoding
    outputs = model.generate(
        **inputs,
        max_new_tokens=150,
        temperature=0.9,
        top_p=0.95,
        repetition_penalty=1.2,
        do_sample=True,
        num_return_sequences=1
    )

    # Decode output
    generated = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return {"response": generated}