AICycling_mcp/core_app.py

#!/usr/bin/env python3
"""
Core Application - Clean skeleton with separated concerns
"""

import asyncio
import logging
from pathlib import Path
from typing import Dict, Any, Optional

from config import Config, load_config
from llm_client import LLMClient
from mcp_client import MCPClient
from cache_manager import CacheManager
from template_engine import TemplateEngine

logger = logging.getLogger(__name__)

class CyclingAnalyzerApp:
    """Main application class - orchestrates all components"""
    
    def __init__(self, config: Config):
        self.config = config
        self.llm_client = LLMClient(config)
        self.mcp_client = MCPClient(config)
        self.cache_manager = CacheManager()
        self.template_engine = TemplateEngine(config.templates_dir)
        
    async def initialize(self):
        """Initialize all components"""
        logger.info("Initializing application components...")
        
        await self.llm_client.initialize()
        await self.mcp_client.initialize()
        await self._preload_cache()
        
        logger.info("Application initialization complete")
    
    async def cleanup(self):
        """Cleanup all components"""
        await self.mcp_client.cleanup()
        await self.llm_client.cleanup()
    
    async def _preload_cache(self):
        """Pre-load and cache common MCP responses"""
        logger.info("Pre-loading cache...")
        
        # Cache user profile
        if await self.mcp_client.has_tool("user_profile"):
            profile = await self.mcp_client.call_tool("user_profile", {})
            self.cache_manager.set("user_profile", profile)
        
        # Cache recent activities
        if await self.mcp_client.has_tool("get_activities"):
            activities = await self.mcp_client.call_tool("get_activities", {"limit": 10})
            self.cache_manager.set("recent_activities", activities)
            
            # Find and cache last cycling activity details
            cycling_activity = self._find_last_cycling_activity(activities)
            if cycling_activity and await self.mcp_client.has_tool("get_activity_details"):
                details = await self.mcp_client.call_tool(
                    "get_activity_details", 
                    {"activity_id": cycling_activity["activityId"]}
                )
                self.cache_manager.set("last_cycling_details", details)
    
    def _find_last_cycling_activity(self, activities: list) -> Optional[Dict[str, Any]]:
        """Find the most recent cycling activity from activities list"""
        cycling_activities = [
            act for act in activities
            if "cycling" in act.get("activityType", {}).get("typeKey", "").lower()
        ]
        return max(cycling_activities, key=lambda x: x.get("start_time", "")) if cycling_activities else None
    
    # Core functionality methods
    
    async def analyze_workout(self, analysis_type: str = "last_workout", **kwargs) -> str:
        """Analyze workout using LLM with cached data"""
        template_name = f"workflows/{analysis_type}.txt"
        
        # Prepare context data
        context = {
            "user_profile": self.cache_manager.get("user_profile", {}),
            "recent_activities": self.cache_manager.get("recent_activities", []),
            "last_cycling_details": self.cache_manager.get("last_cycling_details", {}),
            **kwargs
        }
        
        # Load and render template
        prompt = self.template_engine.render(template_name, **context)
        
        # Call LLM
        return await self.llm_client.generate(prompt)
    
    async def suggest_next_workout(self, **kwargs) -> str:
        """Generate workout suggestion using MCP tools and LLM"""
        # Use MCP-enabled agent for dynamic tool usage
        template_name = "workflows/suggest_next_workout.txt"
        
        context = {
            "training_rules": kwargs.get("training_rules", ""),
            **kwargs
        }
        
        prompt = self.template_engine.render(template_name, **context)
        
        # Use MCP-enabled LLM client for this
        return await self.llm_client.generate_with_tools(prompt, self.mcp_client)
    
    async def enhanced_analysis(self, analysis_type: str, **kwargs) -> str:
        """Perform enhanced analysis with full MCP tool access"""
        template_name = "workflows/enhanced_analysis.txt"
        
        context = {
            "analysis_type": analysis_type,
            "cached_data": self.cache_manager.get_all(),
            **kwargs
        }
        
        prompt = self.template_engine.render(template_name, **context)
        return await self.llm_client.generate_with_tools(prompt, self.mcp_client)
    
    # Utility methods
    
    def list_available_tools(self) -> list:
        """Get list of available MCP tools"""
        return self.mcp_client.list_tools()
    
    def list_templates(self) -> list:
        """Get list of available templates"""
        return self.template_engine.list_templates()
    
    def get_cached_data(self, key: str = None) -> Any:
        """Get cached data by key, or all if no key provided"""
        return self.cache_manager.get(key) if key else self.cache_manager.get_all()

async def main():
    """Main entry point"""
    logging.basicConfig(level=logging.INFO)
    
    try:
        config = load_config()
        app = CyclingAnalyzerApp(config)
        
        await app.initialize()
        
        # Example usage
        print("Available tools:", len(app.list_available_tools()))
        print("Available templates:", len(app.list_templates()))
        
        # Run analysis
        analysis = await app.analyze_workout("analyze_last_workout", 
                                            training_rules="Sample rules")
        print("Analysis:", analysis[:200] + "...")
        
    except Exception as e:
        logger.error(f"Application error: {e}")
    finally:
        if 'app' in locals():
            await app.cleanup()

if __name__ == "__main__":
    asyncio.run(main())