main.py

from fastapi import FastAPI
from pydantic import BaseModel
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import StreamingResponse
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
import torch

app = FastAPI()

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Load model and tokenizer
model_name = "Qwen/Qwen2.5-7B-Instruct-1M"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto"
)

class Question(BaseModel):
    question: str

def generate_response_chunks(prompt: str):
    try:
        # Prepare input
        messages = [
            {"role": "system", "content": "You are Orion AI assistant..."},
            {"role": "user", "content": prompt}
        ]
        inputs = tokenizer.apply_chat_template(
            messages,
            tokenize=True,
            add_generation_prompt=True,
            return_tensors="pt"
        ).to(model.device)

        # Set up streamer
        streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

        # Generate response with streaming
        with torch.no_grad():
            model.generate(
                inputs,
                max_new_tokens=512,
                do_sample=True,
                temperature=0.7,
                top_p=0.9,
                streamer=streamer
            )
            # Since TextStreamer handles printing, we yield chunks from the streamer
            for text in streamer:
                if text:
                    yield text

    except Exception as e:
        yield f"Error occurred: {str(e)}"

@app.post("/ask")
async def ask(question: Question):
    return StreamingResponse(
        generate_response_chunks(question.question),
        media_type="text/plain"
    )