app.py

#!/usr/bin/env python3
"""
AI Chat Application - Pure FastAPI Backend
Serves custom frontend with OpenAI compatible API
"""

import os
import sys
import json
import logging
import time
from typing import Optional, Dict, Any, Generator, List
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
from fastapi import FastAPI, HTTPException, Response
from fastapi.responses import StreamingResponse, FileResponse
from fastapi.staticfiles import StaticFiles
from fastapi.middleware.cors import CORSMiddleware
import asyncio
import threading
from threading import Thread
from pydantic import BaseModel

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

# Pydantic models for API requests/responses
class ChatMessage(BaseModel):
    role: str
    content: str

class ChatRequest(BaseModel):
    messages: List[ChatMessage]
    model: Optional[str] = "qwen-coder-3-30b"
    temperature: Optional[float] = 0.7
    max_tokens: Optional[int] = 2048
    stream: Optional[bool] = False

class ChatResponse(BaseModel):
    id: str
    object: str = "chat.completion"
    created: int
    model: str
    choices: List[Dict[str, Any]]

# Global model variables
tokenizer = None
model = None

def load_model():
    """Load the Qwen model and tokenizer"""
    global tokenizer, model
    
    try:
        model_name = "Qwen/Qwen3-Coder-30B-A3B-Instruct"  # Adjust model name as needed
        
        logger.info(f"Loading model: {model_name}")
        tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
        model = AutoModelForCausalLM.from_pretrained(
            model_name,
            torch_dtype=torch.float16,
            device_map="auto",
            trust_remote_code=True
        )
        logger.info("Model loaded successfully")
        
    except Exception as e:
        logger.error(f"Error loading model: {e}")
        # For development/testing, use a fallback
        logger.warning("Using fallback model response")

def generate_response(messages: List[ChatMessage], temperature: float = 0.7, max_tokens: int = 2048):
    """Generate response from the model"""
    try:
        if model is None or tokenizer is None:
            # Fallback response for development
            return "I'm a Qwen AI assistant. The model is currently loading, please try again in a moment."
        
        # Format messages for the model
        formatted_messages = []
        for msg in messages:
            formatted_messages.append({"role": msg.role, "content": msg.content})
        
        # Apply chat template
        text = tokenizer.apply_chat_template(
            formatted_messages, 
            tokenize=False, 
            add_generation_prompt=True
        )
        
        # Tokenize
        inputs = tokenizer(text, return_tensors="pt").to(model.device)
        
        # Generate
        with torch.no_grad():
            outputs = model.generate(
                **inputs,
                max_new_tokens=max_tokens,
                temperature=temperature,
                do_sample=True,
                pad_token_id=tokenizer.eos_token_id
            )
        
        # Decode response
        response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
        return response.strip()
        
    except Exception as e:
        logger.error(f"Error generating response: {e}")
        return f"I apologize, but I encountered an error while processing your request: {str(e)}"

def generate_streaming_response(messages: List[ChatMessage], temperature: float = 0.7, max_tokens: int = 2048):
    """Generate streaming response from the model"""
    try:
        if model is None or tokenizer is None:
            # Fallback streaming response
            response = "I'm a Qwen AI assistant. The model is currently loading, please try again in a moment."
            for char in response:
                yield f"data: {json.dumps({'choices': [{'delta': {'content': char}}]})}\n\n"
                time.sleep(0.05)
            yield f"data: {json.dumps({'choices': [{'finish_reason': 'stop'}]})}\n\n"
            yield "data: [DONE]\n\n"
            return
        
        # Format messages
        formatted_messages = []
        for msg in messages:
            formatted_messages.append({"role": msg.role, "content": msg.content})
        
        # Apply chat template
        text = tokenizer.apply_chat_template(
            formatted_messages, 
            tokenize=False, 
            add_generation_prompt=True
        )
        
        # Tokenize
        inputs = tokenizer(text, return_tensors="pt").to(model.device)
        
        # Setup streaming
        streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
        
        generation_kwargs = {
            **inputs,
            "max_new_tokens": max_tokens,
            "temperature": temperature,
            "do_sample": True,
            "pad_token_id": tokenizer.eos_token_id,
            "streamer": streamer
        }
        
        # Start generation in a thread
        thread = Thread(target=model.generate, kwargs=generation_kwargs)
        thread.start()
        
        # Stream the response
        for new_text in streamer:
            if new_text:
                yield f"data: {json.dumps({'choices': [{'delta': {'content': new_text}}]})}\n\n"
        
        yield f"data: {json.dumps({'choices': [{'finish_reason': 'stop'}]})}\n\n"
        yield "data: [DONE]\n\n"
        
    except Exception as e:
        logger.error(f"Error in streaming generation: {e}")
        error_msg = f"Error: {str(e)}"
        yield f"data: {json.dumps({'choices': [{'delta': {'content': error_msg}}]})}\n\n"
        yield f"data: {json.dumps({'choices': [{'finish_reason': 'stop'}]})}\n\n"
        yield "data: [DONE]\n\n"

# FastAPI app
app = FastAPI(title="AI Chat API", description="OpenAI compatible interface for Qwen model")

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

# API endpoints
@app.get("/")
async def serve_index():
    """Serve the main HTML file"""
    return FileResponse("public/index.html")

@app.get("/health")
async def health_check():
    """Health check endpoint"""
    return {"status": "healthy", "model_loaded": model is not None}

@app.get("/ping")
async def ping():
    """Simple ping endpoint"""
    return {"status": "pong"}

@app.get("/api/models")
async def list_models():
    """List available models"""
    return {
        "data": [
            {
                "id": "qwen-coder-3-30b",
                "object": "model",
                "created": int(time.time()),
                "owned_by": "qwen"
            }
        ]
    }

@app.post("/api/chat")
async def chat_completion(request: ChatRequest):
    """OpenAI compatible chat completion endpoint"""
    try:
        if request.stream:
            return StreamingResponse(
                generate_streaming_response(
                    request.messages, 
                    request.temperature or 0.7, 
                    request.max_tokens or 2048
                ),
                media_type="text/plain"
            )
        else:
            response_content = generate_response(
                request.messages, 
                request.temperature or 0.7, 
                request.max_tokens or 2048
            )
            
            return ChatResponse(
                id=f"chatcmpl-{int(time.time())}",
                created=int(time.time()),
                model=request.model or "qwen-coder-3-30b",
                choices=[{
                    "index": 0,
                    "message": {
                        "role": "assistant",
                        "content": response_content
                    },
                    "finish_reason": "stop"
                }]
            )
            
    except Exception as e:
        logger.error(f"Error in chat completion: {e}")
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/v1/chat/completions")
async def openai_chat_completion(request: ChatRequest):
    """OpenAI API compatible endpoint"""
    return await chat_completion(request)

# Mount static files AFTER API routes
app.mount("/", StaticFiles(directory="public", html=True), name="static")

# Startup event
@app.on_event("startup")
async def startup_event():
    """Initialize the model on startup"""
    # Load model in background thread to avoid blocking startup
    thread = Thread(target=load_model)
    thread.daemon = True
    thread.start()

if __name__ == "__main__":
    import uvicorn
    
    # For Hugging Face Spaces
    port = int(os.environ.get("PORT", 7860))
    
    uvicorn.run(
        app, 
        host="0.0.0.0", 
        port=port,
        access_log=True
    )