app.py

from fastapi import FastAPI, Request
from transformers import AutoModelForCausalLM, AutoTokenizer
from fastapi.responses import StreamingResponse
import torch

app = FastAPI()

# Load the model and tokenizer
model_name = "EleutherAI/gpt-neo-1.3B"  # Replace with your desired model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

@app.get("/")
def read_root():
    return {"Hello": "World"}

@app.post("/predict")
async def predict(request: Request):
    data = await request.json()
    prompt = data.get("prompt", "")
    if not prompt:
        return {"error": "Prompt is required"}

    # Tokenize the input
    inputs = tokenizer(prompt, return_tensors="pt").to("cpu")  # Use "cuda" if GPU is enabled

    # Generator function to stream tokens
    def token_generator():
        outputs = model.generate(
            inputs.input_ids,
            max_length=100,
            do_sample=True,
            num_return_sequences=1,
            temperature=0.7,
            top_p=0.9,
            repetition_penalty=1.2,
        )
        for token_id in outputs[0]:
            token = tokenizer.decode(token_id, skip_special_tokens=True)
            yield f"{token} "
    
    # Return StreamingResponse
    return StreamingResponse(token_generator(), media_type="text/plain")