app.py

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import torch
from transformers import T5ForConditionalGeneration, T5Tokenizer

# Initialize FastAPI app
app = FastAPI()

# Lazy load model and tokenizer
model = None
tokenizer = None

def load_model():
    global model, tokenizer
    if model is None or tokenizer is None:
        tokenizer = T5Tokenizer.from_pretrained('./tokenizer12')
        model = T5ForConditionalGeneration.from_pretrained('./model')
        model.to('cuda' if torch.cuda.is_available() else 'cpu')

# Request body schema using Pydantic
class QuestionRequest(BaseModel):
    context: str
    answer: str

from fastapi import Query

@app.post("/generate_question")
async def generate_question(request: QuestionRequest):
    load_model()
    device = 'cuda' if torch.cuda.is_available() else 'cpu'
    
    input_text = f"context: {request.context} answer: {request.answer}"
    encoding = tokenizer.encode_plus(
        input_text,
        max_length=512,
        padding="max_length",
        truncation=True,
        return_tensors="pt"
    )
    input_ids = encoding["input_ids"].to(device)
    attention_mask = encoding["attention_mask"].to(device)

    model.eval()
    with torch.no_grad():
        beam_outputs = model.generate(
            input_ids=input_ids,
            attention_mask=attention_mask,
            max_length=72,
            early_stopping=True,
            num_beams=5,
            num_return_sequences=3
        )

    return {
        "generated_questions": [
            tokenizer.decode(output, skip_special_tokens=True, clean_up_tokenization_spaces=True)
            for output in beam_outputs
        ]
    }


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