app.py

import os
import gradio as gr
from typing import List
import logging
import logging.handlers
import time
import random
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool
from langgraph.prebuilt import create_react_agent
from langchain_core.messages import HumanMessage
from langchain_tavily import TavilySearch

# Configuration - set to False to disable detailed logging
ENABLE_DETAILED_LOGGING = True

# Setup logging with rotation (7 days max)
if ENABLE_DETAILED_LOGGING:
    # Create formatter
    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')

    # Setup console handler
    console_handler = logging.StreamHandler()
    console_handler.setFormatter(formatter)

    # Setup rotating file handler (7 days, daily rotation)
    file_handler = logging.handlers.TimedRotatingFileHandler(
        'agent.log',
        when='midnight',
        interval=1,
        backupCount=7,  # Keep 7 days of logs
        encoding='utf-8'
    )
    file_handler.setFormatter(formatter)

    # Configure root logger
    logging.basicConfig(
        level=logging.INFO,
        handlers=[console_handler, file_handler]
    )
else:
    logging.basicConfig(level=logging.WARNING)

logger = logging.getLogger(__name__)

# Configuration from environment variables
llm_ip = os.environ.get('public_ip')
llm_port = os.environ.get('port')
llm_key = os.environ.get('api_key')
llm_model = os.environ.get('model')

# Tavily API configuration
tavily_key = os.environ.get('tavily_key', '')
if tavily_key:
    os.environ['TAVILY_API_KEY'] = tavily_key

# Tavily search tool integration

class ReactAgentChat:
    def __init__(self, ip: str, port: str, api_key: str, model: str):
        self.ip = ip
        self.port = port
        self.api_key = api_key
        self.model = model
        self.agent = None
        self._setup_agent()

    def _setup_agent(self):
        """Initialize the LangGraph ReAct agent"""
        try:
            if ENABLE_DETAILED_LOGGING:
                logger.info(f"=== SETTING UP AGENT ===")
                logger.info(f"LLM URL: http://{self.ip}:{self.port}/v1")
                logger.info(f"Model: {self.model}")

            # Create OpenAI-compatible model
            llm = ChatOpenAI(
                base_url=f"http://{self.ip}:{self.port}/v1",
                api_key=self.api_key,
                model=self.model,
                temperature=0.7
            )
            if ENABLE_DETAILED_LOGGING:
                logger.info("LLM created successfully")

            # Define tools - use Tavily search API with graceful error handling
            if tavily_key:
                if ENABLE_DETAILED_LOGGING:
                    logger.info("Setting up Tavily search tool")
                try:
                    # Create custom wrapper for Tavily with error handling
                    @tool
                    def web_search(query: str) -> str:
                        """Search the web for current information about any topic."""
                        try:
                            tavily_tool = TavilySearch(
                                max_results=5,
                                topic="general",
                                include_answer=True,
                                search_depth="advanced"
                            )
                            result = tavily_tool.invoke({"query": query})
                            if ENABLE_DETAILED_LOGGING:
                                logger.info(f"Tavily search successful for query: {query}")
                            return result
                        except Exception as e:
                            error_str = str(e).lower()
                            if ENABLE_DETAILED_LOGGING:
                                logger.error(f"Tavily search failed for query '{query}': {e}")

                            # Check for rate limit or quota issues
                            if any(keyword in error_str for keyword in ['rate limit', 'quota', 'limit exceeded', 'usage limit', 'billing']):
                                if ENABLE_DETAILED_LOGGING:
                                    logger.warning(f"Tavily rate limit/quota exceeded: {e}")
                                return "I can't search the web right now."
                            else:
                                if ENABLE_DETAILED_LOGGING:
                                    logger.error(f"Tavily API error: {e}")
                                return "I can't search the web right now."

                    search_tool = web_search
                    if ENABLE_DETAILED_LOGGING:
                        logger.info("Tavily search tool wrapper created successfully")
                except Exception as e:
                    if ENABLE_DETAILED_LOGGING:
                        logger.error(f"Failed to create Tavily tool wrapper: {e}")
                    # Fallback tool
                    @tool
                    def no_search(query: str) -> str:
                        """Search tool unavailable."""
                        return "I can't search the web right now."
                    search_tool = no_search
            else:
                if ENABLE_DETAILED_LOGGING:
                    logger.warning("No Tavily API key found, creating fallback tool")
                @tool
                def no_search(query: str) -> str:
                    """Search tool unavailable."""
                    if ENABLE_DETAILED_LOGGING:
                        logger.error("Search attempted but no Tavily API key configured")
                    return "I can't search the web right now."
                search_tool = no_search

            tools = [search_tool]
            if ENABLE_DETAILED_LOGGING:
                logger.info(f"Tools defined: {[tool.name for tool in tools]}")

            # Bind tools to the model
            model_with_tools = llm.bind_tools(tools)
            if ENABLE_DETAILED_LOGGING:
                logger.info("Tools bound to model")

            # Create the ReAct agent
            self.agent = create_react_agent(model_with_tools, tools)
            if ENABLE_DETAILED_LOGGING:
                logger.info("ReAct agent created successfully")

        except Exception as e:
            logger.error(f"=== AGENT SETUP ERROR ===")
            logger.error(f"Failed to setup agent: {e}")
            import traceback
            logger.error(f"Traceback: {traceback.format_exc()}")
            raise e

    def update_config(self, ip: str, port: str, api_key: str, model: str):
        """Update LLM configuration"""
        if (ip != self.ip or port != self.port or
            api_key != self.api_key or model != self.model):
            self.ip = ip
            self.port = port
            self.api_key = api_key
            self.model = model
            self._setup_agent()

    def chat(self, message: str, history: List[List[str]]) -> str:
        """Generate chat response using ReAct agent"""
        try:
            if not self.agent:
                return "Error: Agent not initialized"

            if ENABLE_DETAILED_LOGGING:
                logger.info(f"=== USER INPUT ===")
                logger.info(f"Message: {message}")
                logger.info(f"History length: {len(history)}")

            # Convert history to messages for context handling
            messages = []
            for user_msg, assistant_msg in history:
                messages.append(HumanMessage(content=user_msg))
                if assistant_msg:  # Only add if assistant responded
                    from langchain_core.messages import AIMessage
                    messages.append(AIMessage(content=assistant_msg))

            # Add current message
            messages.append(HumanMessage(content=message))

            # Invoke the agent
            if ENABLE_DETAILED_LOGGING:
                logger.info(f"=== INVOKING AGENT ===")
                logger.info(f"Total messages in history: {len(messages)}")
            response = self.agent.invoke({"messages": messages})

            if ENABLE_DETAILED_LOGGING:
                logger.info(f"=== AGENT RESPONSE ===")
                logger.info(f"Full response: {response}")
                logger.info(f"Number of messages: {len(response.get('messages', []))}")

                # Log each message in the response
                for i, msg in enumerate(response.get("messages", [])):
                    logger.info(f"Message {i}: Type={type(msg).__name__}, Content={getattr(msg, 'content', 'No content')}")

            # Extract the final response
            final_message = response["messages"][-1].content
            if ENABLE_DETAILED_LOGGING:
                logger.info(f"=== FINAL MESSAGE ===")
                logger.info(f"Final message: {final_message}")

            return final_message

        except Exception as e:
            error_msg = f"Agent error: {str(e)}"
            logger.error(f"=== AGENT ERROR ===")
            logger.error(f"Error: {e}")
            logger.error(f"Error type: {type(e)}")
            import traceback
            logger.error(f"Traceback: {traceback.format_exc()}")
            return error_msg

# Global agent instance
react_agent = ReactAgentChat(llm_ip, llm_port, llm_key, llm_model)

def generate_response(message: str, history: List[List[str]], system_prompt: str,
                     max_tokens: int, ip: str, port: str, api_key: str, model: str):
    """Generate response using ReAct agent"""
    global react_agent

    try:
        # Update agent configuration if changed
        react_agent.update_config(ip, port, api_key, model)

        # Generate response
        response = react_agent.chat(message, history)

        # Stream the response word by word for better UX
        words = response.split()
        current_response = ""
        for word in words:
            current_response += word + " "
            yield current_response.strip()

    except Exception as e:
        error_msg = f"Error: {str(e)}"
        logger.error(error_msg)
        yield error_msg

# Create Gradio ChatInterface
chatbot = gr.ChatInterface(
    generate_response,
    chatbot=gr.Chatbot(
        avatar_images=[
            None,
            "https://cdn-avatars.huggingface.co/v1/production/uploads/64e6d37e02dee9bcb9d9fa18/o_HhUnXb_PgyYlqJ6gfEO.png"
        ],
        height="64vh"
    ),
    additional_inputs=[
        gr.Textbox(
            "You are a helpful AI assistant with web search capabilities.",
            label="System Prompt",
            lines=2
        ),
        gr.Slider(50, 2048, label="Max Tokens", value=512,
                 info="Maximum number of tokens in the response"),
        gr.Textbox(llm_ip, label="LLM IP Address",
                  info="IP address of the OpenAI-compatible LLM server"),
        gr.Textbox(llm_port, label="LLM Port",
                  info="Port of the LLM server"),
        gr.Textbox(llm_key, label="API Key", type="password",
                  info="API key for the LLM server"),
        gr.Textbox(llm_model, label="Model Name",
                  info="Name of the model to use"),
    ],
    title="🤖 LangGraph ReAct Agent with DuckDuckGo Search",
    description="Chat with a LangGraph ReAct agent that can search the web using DuckDuckGo. Ask about current events, research topics, or any questions that require up-to-date information!",
    theme="finlaymacklon/smooth_slate",
    submit_btn="Send",
    # retry_btn="🔄 Regenerate Response",
    # undo_btn="↩ Delete Previous",
    clear_btn="🗑️ Clear Chat"
)

if __name__ == "__main__":
    chatbot.queue().launch()