app.py

from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import logging
import gradio as gr
import uvicorn

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

app = FastAPI()

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

MODEL_ID = "tugstugi/Qwen2.5-Coder-0.5B-QwQ-draft"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(MODEL_ID).to(device)

class ChatMessage(BaseModel):
    role: str
    content: str

class ChatRequest(BaseModel):
    messages: list[ChatMessage]

class ChatResponse(BaseModel):
    response: str
    status: str = "success"

def build_prompt(messages):
    prompt = ""
    for message in messages:
        if message["role"] == "user":
            prompt += f"<|im_start|>user\n{message['content']}<|im_end|>\n"
        elif message["role"] == "assistant":
            prompt += f"<|im_start|>assistant\n{message['content']}<|im_end|>\n"
    prompt += "<|im_start|>assistant\n"
    return prompt

def generate_response(conversation_history, max_new_tokens=1500):
    prompt_text = build_prompt(conversation_history)

    inputs = tokenizer(prompt_text, return_tensors="pt").to(device)

    generated_ids = model.generate(
        **inputs,
        max_new_tokens=max_new_tokens,
        do_sample=True,
        temperature=0.8,
        top_p=0.95,
        pad_token_id=tokenizer.eos_token_id
    )

    generated_text = tokenizer.decode(generated_ids[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True)

    return generated_text.strip()

@app.post("/api/chat", response_model=ChatResponse)
async def chat_endpoint(request: ChatRequest):
    try:
        conversation = [{"role": msg.role, "content": msg.content} for msg in request.messages]
        response_text = generate_response(conversation)
        return ChatResponse(response=response_text)
    except Exception as e:
        logger.error(f"Error: {str(e)}")
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/api/health")
async def health_check():
    return {"status": "healthy"}

# Gradio setup
iface = gr.Interface(fn=lambda input: generate_response([{"role": "user", "content": input}]), 
                     inputs="text", outputs="text")
app = gr.mount_gradio_app(app, iface, path="/")

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