"""
Advanced Agentic System Interface
-------------------------------
Provides an interface to interact with the autonomous agent system
using local LLM for improved performance.
"""

import gradio as gr
import asyncio
from typing import Dict, Any, List
import json
from datetime import datetime
import logging

from agentic_system import AgenticSystem
from team_management import TeamManager
from orchestrator import AgentOrchestrator
from reasoning.unified_engine import UnifiedReasoningEngine

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

class AgentInterface:
    """Interface for the agentic system."""
    
    def __init__(self):
        """Initialize the interface components."""
        self.orchestrator = AgentOrchestrator()
        self.reasoning_engine = UnifiedReasoningEngine(
            min_confidence=0.7,
            parallel_threshold=3,
            learning_rate=0.1
        )
        
    async def process_query(self, message: str) -> str:
        """Process user query through the reasoning system."""
        try:
            # Prepare context
            context = {
                'timestamp': datetime.now().isoformat(),
                'objective': 'Provide helpful and accurate responses',
                'mode': 'analytical'
            }
            
            # Get response from reasoning engine
            result = await self.reasoning_engine.reason(
                query=message,
                context=context
            )
            
            if result.success:
                return result.answer
            else:
                return f"Error: Unable to process query. Please try again."
                
        except Exception as e:
            logger.error(f"Error processing query: {e}")
            return f"Error: {str(e)}"

# Initialize interface
interface = AgentInterface()

# Create Gradio interface
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("""
    # AI Reasoning System
    This system uses advanced reasoning strategies including local LLM for improved performance.
    
    Note: First query might take a few seconds as the model loads.
    """)
    
    with gr.Row():
        with gr.Column(scale=4):
            input_text = gr.Textbox(
                label="Your question",
                placeholder="Ask me anything...",
                lines=2
            )
            output_text = gr.Textbox(
                label="Response",
                lines=10,
                interactive=False
            )
            submit_btn = gr.Button("Ask")
            clear_btn = gr.Button("Clear")
            
        with gr.Column(scale=1):
            gr.Markdown("""
            ### Example Questions:
            - What are the implications of artificial intelligence on society?
            - How does climate change affect global ecosystems?
            - What are the philosophical implications of quantum mechanics?
            """)
    
    # Set up event handlers
    submit_btn.click(
        fn=interface.process_query,
        inputs=input_text,
        outputs=output_text
    )
    clear_btn.click(
        lambda: ("", ""),
        inputs=None,
        outputs=[input_text, output_text]
    )

# Launch the interface
if __name__ == "__main__":
    demo.launch()