app.py

from fastapi import FastAPI, Request
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

app = FastAPI()

# Load model and tokenizer once at startup
model_name = "distilbert/distilgpt2"  # change this to your own model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

class PromptRequest(BaseModel):
    prompt: str
    max_new_tokens: int = 50

@app.post("/generate")
async def generate_text(req: PromptRequest):
    inputs = tokenizer(req.prompt, return_tensors="pt")
    outputs = model.generate(
        **inputs,
        max_new_tokens=req.max_new_tokens,
        do_sample=True,
        temperature=0.8,
        top_p=0.95,
    )
    generated = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return {"generated_text": generated}