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import gradio as gr
from langchain.llms import LlamaCpp
import os
import json
import torch
import logging
from typing import Optional, List, Dict, Any
from fastapi import FastAPI, HTTPException, Request
from fastapi.responses import JSONResponse
from pydantic import BaseModel
import uvicorn
import time
from threading import Lock

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

class ChatCompletionRequest(BaseModel):
    model: str
    messages: List[Dict[str, str]]
    temperature: Optional[float] = 0.7
    max_tokens: Optional[int] = 2048
    stream: Optional[bool] = False

class QwenModel:
    def __init__(self, model_path: str):
        """Initialize the Qwen model with automatic device detection."""
        try:
            # Check for GPU availability
            self.has_gpu = torch.cuda.is_available()
            self.device_count = torch.cuda.device_count() if self.has_gpu else 0
            logger.info(f"GPU available: {self.has_gpu}, Device count: {self.device_count}")

            # Configure model parameters based on available hardware
            n_gpu_layers = 40 if self.has_gpu else 0
            logger.info(f"Using {'GPU' if self.has_gpu else 'CPU'} for inference")

            self.llm = LlamaCpp(
                model_path=model_path,
                n_gpu_layers=n_gpu_layers,
                n_ctx=4096,
                n_batch=512 if self.has_gpu else 128,  # Reduced batch size for CPU
                verbose=True,
                temperature=0.7,
                max_tokens=2048,
                top_p=0.95,
                top_k=50,
                f16_kv=self.has_gpu,  # Only use f16 when GPU is available
                use_mlock=True,  # Pin memory for better performance
                use_mmap=True,
            )
            
            # Thread lock for concurrent API requests
            self.lock = Lock()
            
        except Exception as e:
            logger.error(f"Failed to initialize model: {str(e)}")
            raise

    def generate_cot_prompt(self, messages: List[Dict[str, str]]) -> str:
        """Generate a chain-of-thought prompt from message history."""
        conversation = []
        for msg in messages:
            role = msg.get("role", "")
            content = msg.get("content", "")
            
            if role == "system":
                conversation.append(f"System: {content}")
            elif role == "user":
                conversation.append(f"Human: {content}")
            elif role == "assistant":
                conversation.append(f"Assistant: {content}")

        last_user_msg = next((msg["content"] for msg in reversed(messages) 
                            if msg["role"] == "user"), None)
        
        if not last_user_msg:
            raise ValueError("No user message found in the conversation")

        cot_template = f"""Previous conversation:
{chr(10).join(conversation)}

Let's approach the latest question step-by-step:

1. Understanding the question:
   {last_user_msg}

2. Breaking down components:
   - Key elements to consider
   - Specific information requested
   - Relevant constraints

3. Reasoning process:
   - Systematic approach
   - Applicable knowledge
   - Potential challenges

4. Step-by-step solution:

"""
        return cot_template

    def process_response(self, response: str) -> str:
        """Process and format the model's response."""
        try:
            response = response.strip()
            # Add structural markers for better readability
            if not response.startswith("Step"):
                response = "Step-by-step solution:\n" + response
            return response
        except Exception as e:
            logger.error(f"Error processing response: {str(e)}")
            return "Error processing response"

    def generate_response(self, 
                         messages: List[Dict[str, str]], 
                         temperature: float = 0.7, 
                         max_tokens: int = 2048) -> Dict[str, Any]:
        """Generate a response using chain-of-thought reasoning."""
        try:
            with self.lock:  # Thread safety for concurrent API requests
                # Generate the CoT prompt
                full_prompt = self.generate_cot_prompt(messages)
                
                # Get response from model
                start_time = time.time()
                response = self.llm(
                    full_prompt,
                    temperature=temperature,
                    max_tokens=max_tokens
                )
                end_time = time.time()
                
                # Process response
                processed_response = self.process_response(response)
                
                # Format response in OpenAI-compatible structure
                return {
                    "id": f"chatcmpl-{int(time.time()*1000)}",
                    "object": "chat.completion",
                    "created": int(time.time()),
                    "model": "qwen-2.5-14b",
                    "choices": [{
                        "index": 0,
                        "message": {
                            "role": "assistant",
                            "content": processed_response
                        },
                        "finish_reason": "stop"
                    }],
                    "usage": {
                        "prompt_tokens": len(full_prompt.split()),
                        "completion_tokens": len(processed_response.split()),
                        "total_tokens": len(full_prompt.split()) + len(processed_response.split())
                    },
                    "system_info": {
                        "device": "gpu" if self.has_gpu else "cpu",
                        "processing_time": round(end_time - start_time, 2)
                    }
                }
        except Exception as e:
            logger.error(f"Error generating response: {str(e)}")
            raise HTTPException(status_code=500, detail=str(e))

# Initialize FastAPI
app = FastAPI(title="Qwen 2.5 API")

def create_gradio_interface(model: QwenModel):
    """Create and configure the Gradio interface."""
    
    def predict(message: str, 
                temperature: float, 
                max_tokens: int) -> str:
        messages = [{"role": "user", "content": message}]
        response = model.generate_response(
            messages, 
            temperature=temperature, 
            max_tokens=max_tokens
        )
        return response["choices"][0]["message"]["content"]

    iface = gr.Interface(
        fn=predict,
        inputs=[
            gr.Textbox(
                label="Input",
                placeholder="Enter your question or task here...",
                lines=5
            ),
            gr.Slider(
                minimum=0.1,
                maximum=1.0,
                value=0.7,
                label="Temperature",
                info="Higher values make the output more random"
            ),
            gr.Slider(
                minimum=64,
                maximum=4096,
                value=2048,
                step=64,
                label="Max Tokens",
                info="Maximum length of the generated response"
            )
        ],
        outputs=gr.Textbox(label="Response", lines=10),
        title=f"Qwen 2.5 14B Instruct Model ({'GPU' if model.has_gpu else 'CPU'} Mode)",
        description="""This is a Qwen 2.5 14B model interface with chain-of-thought prompting.
        The model will break down complex problems and solve them step by step.""",
        examples=[
            ["Explain how photosynthesis works", 0.7, 2048],
            ["Solve the quadratic equation: x² + 5x + 6 = 0", 0.7, 1024],
            ["What are the implications of Moore's Law for future computing?", 0.8, 2048]
        ]
    )
    return iface

# Global model instance
model = None

@app.on_event("startup")
async def startup_event():
    """Initialize the model on startup."""
    global model
    model_path = "G17c21ds/Qwen2.5-14B-Instruct-Uncensored-Q8_0-GGUF"
    model = QwenModel(model_path)
    logger.info("Model initialized successfully")

@app.post("/v1/chat/completions")
async def create_chat_completion(request: ChatCompletionRequest):
    """OpenAI-compatible chat completions endpoint."""
    try:
        response = model.generate_response(
            request.messages,
            temperature=request.temperature,
            max_tokens=request.max_tokens
        )
        return JSONResponse(content=response)
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

def main():
    """Main function to initialize and launch the application."""
    try:
        global model
        # Model path
        model_path = "G17c21ds/Qwen2.5-14B-Instruct-Uncensored-Q8_0-GGUF"
        
        # Initialize the model if not already initialized
        if model is None:
            model = QwenModel(model_path)
        
        # Create and launch the Gradio interface
        interface = create_gradio_interface(model)
        
        # Mount FastAPI app to Gradio
        app.mount("/", interface.app)
        
        # Launch with uvicorn
        uvicorn.run(
            app,
            host="0.0.0.0",
            port=7860,
            log_level="info"
        )
    except Exception as e:
        logger.error(f"Application failed to start: {str(e)}")
        raise

if __name__ == "__main__":
    main()