app.py

from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForCausalLM
import os

# Use a writable folder for offloading weights
offload_dir = "/tmp/offload"
os.makedirs(offload_dir, exist_ok=True)

app = FastAPI()

# CORS setup
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=False,
    allow_methods=["*"],
    allow_headers=["*"]
)

# Smaller & faster model
model_name = "tiiuae/falcon-rw-1b"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto",
    low_cpu_mem_usage=True,
    offload_folder=offload_dir
)

class PromptRequest(BaseModel):
    prompt: str

@app.post("/api/generate-story")
async def generate_story(req: PromptRequest):
    prompt = req.prompt.strip()
    if not prompt:
        raise HTTPException(status_code=400, detail="Prompt must not be empty")

    inputs = tokenizer(prompt, return_tensors="pt", truncation=True).to(model.device)
    outputs = model.generate(
        **inputs,
        max_new_tokens=150,
        do_sample=True,
        temperature=0.9,
        top_p=0.9
    )
    story = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return {"story": story}