#!/usr/bin/env python3
"""
Interactive chat interface with Macha AI agent.
Unified chat/conversation interface using tool-calling architecture.
"""

import json
import os
import subprocess
import sys
from datetime import datetime
from pathlib import Path
from typing import List, Dict, Any, Optional

# Add parent directory to path for imports
sys.path.insert(0, str(Path(__file__).parent))

from agent import MachaAgent


class MachaChatSession:
    """Interactive chat session with Macha using tool-calling architecture"""
    
    def __init__(
        self,
        ollama_host: str = "http://localhost:11434",
        model: str = "gpt-oss:latest",
        state_dir: Path = Path("/var/lib/macha"),
        enable_tools: bool = True
    ):
        """Initialize chat session with Macha
        
        Args:
            ollama_host: Ollama API endpoint
            model: Model name to use
            state_dir: State directory for agent
            enable_tools: Whether to enable tool calling (should always be True)
        """
        self.agent = MachaAgent(
            ollama_host=ollama_host,
            model=model,
            state_dir=state_dir,
            enable_tools=enable_tools,
            use_queue=True,
            priority="INTERACTIVE"
        )
        self.conversation_history: List[Dict[str, str]] = []
        self.session_start = datetime.now().isoformat()
        
    def _auto_diagnose_ollama(self) -> str:
        """Automatically diagnose Ollama issues"""
        diagnostics = []
        
        diagnostics.append("🔍 AUTO-DIAGNOSIS: Investigating Ollama failure...\n")
        
        # Check if Ollama service is running
        try:
            result = subprocess.run(
                ['systemctl', 'is-active', 'ollama.service'],
                capture_output=True,
                text=True,
                timeout=5
            )
            if result.returncode == 0:
                diagnostics.append("✅ Ollama service is active")
            else:
                diagnostics.append(f"❌ Ollama service is NOT active: {result.stdout.strip()}")
                # Get service status
                status_result = subprocess.run(
                    ['systemctl', 'status', 'ollama.service', '--no-pager', '-l'],
                    capture_output=True,
                    text=True,
                    timeout=5
                )
                diagnostics.append(f"\nService status:\n```\n{status_result.stdout[-500:]}\n```")
        except Exception as e:
            diagnostics.append(f"⚠️  Could not check service status: {e}")
        
        # Check memory usage
        try:
            result = subprocess.run(['free', '-h'], capture_output=True, text=True, timeout=5)
            lines = result.stdout.split('\n')
            for line in lines[:3]:  # First 3 lines
                diagnostics.append(f"  {line}")
        except Exception as e:
            diagnostics.append(f"⚠️  Could not check memory: {e}")
        
        # Check which models are loaded
        try:
            import requests
            response = requests.get(f"{self.agent.ollama_host}/api/tags", timeout=5)
            if response.status_code == 200:
                models = response.json().get('models', [])
                diagnostics.append(f"\n📦 Loaded models ({len(models)}):")
                for model in models:
                    name = model.get('name', 'unknown')
                    size = model.get('size', 0) / (1024**3)
                    is_current = "← TARGET" if name == self.agent.model else ""
                    diagnostics.append(f"  • {name} ({size:.1f} GB) {is_current}")
                
                # Check if target model is loaded
                model_names = [m.get('name') for m in models]
                if self.agent.model not in model_names:
                    diagnostics.append(f"\n❌ TARGET MODEL NOT LOADED: {self.agent.model}")
                    diagnostics.append(f"   Available models: {', '.join(model_names)}")
            else:
                diagnostics.append(f"❌ Ollama API returned {response.status_code}")
        except Exception as e:
            diagnostics.append(f"⚠️  Could not query Ollama API: {e}")
        
        # Check recent Ollama logs
        try:
            result = subprocess.run(
                ['journalctl', '-u', 'ollama.service', '-n', '10', '--no-pager'],
                capture_output=True,
                text=True,
                timeout=5
            )
            if result.stdout:
                diagnostics.append(f"\n📋 Recent Ollama logs (last 10 lines):\n```\n{result.stdout}\n```")
        except Exception as e:
            diagnostics.append(f"⚠️  Could not check logs: {e}")
        
        return "\n".join(diagnostics)
    
    def process_message(self, user_message: str, verbose: bool = False) -> str:
        """Process a user message and return Macha's response
        
        Args:
            user_message: The user's message
            verbose: Whether to show detailed token counts
            
        Returns:
            Macha's response
        """
        
        # Add user message to history
        self.conversation_history.append({
            'role': 'user',
            'message': user_message,
            'timestamp': datetime.now().isoformat()
        })
        
        # Build chat messages for tool-calling API
        messages = []
        
        # Query relevant knowledge based on user message
        knowledge_context = self.agent._query_relevant_knowledge(user_message, limit=3)
        
        # Add recent conversation history (last 15 messages to stay within context limits)
        recent_history = self.conversation_history[-15:]
        for entry in recent_history:
            content = entry['message']
            # Truncate very long messages (e.g., command outputs)
            if len(content) > 3000:
                content = content[:1500] + "\n... [message truncated] ...\n" + content[-1500:]
            # Add knowledge context to last user message if available
            if entry == recent_history[-1] and knowledge_context:
                content += knowledge_context
            messages.append({
                "role": entry['role'],
                "content": content
            })
        
        if verbose:
            # Estimate tokens for debugging
            total_chars = sum(len(json.dumps(m)) for m in messages)
            estimated_tokens = total_chars // 4
            print(f"[Context: {estimated_tokens:,} tokens, {len(messages)} messages]")
        
        try:
            # Use tool-aware chat API - this handles all tool calling automatically
            response_data = self.agent._query_ollama_with_tools(
                messages,
                tool_definitions=self.agent.tools.get_tool_definitions() if self.agent.enable_tools else []
            )
            
            # Extract the final response
            ai_response = response_data.get("content", "")
            
        except Exception as e:
            error_msg = (
                f"❌ CRITICAL: Failed to communicate with Ollama inference engine\n\n"
                f"Error Type: {type(e).__name__}\n"
                f"Error Message: {str(e)}\n\n"
            )
            # Auto-diagnose the issue
            diagnostics = self._auto_diagnose_ollama()
            return error_msg + "\n" + diagnostics
        
        if not ai_response:
            error_msg = (
                f"❌ Empty response from Ollama inference engine\n\n"
                f"The request succeeded but returned no data. This usually means:\n"
                f"  • The model ({self.agent.model}) is still loading\n"
                f"  • Ollama ran out of memory during generation\n"
                f"  • The prompt was too large for the context window\n\n"
            )
            # Auto-diagnose the issue
            diagnostics = self._auto_diagnose_ollama()
            return error_msg + "\n" + diagnostics
        
        # Add response to history
        self.conversation_history.append({
            'role': 'assistant',
            'message': ai_response,
            'timestamp': datetime.now().isoformat()
        })
        
        return ai_response
    
    def run_interactive(self):
        """Run the interactive chat session"""
        print("=" * 70)
        print("🌐 MACHA INTERACTIVE CHAT")
        print("=" * 70)
        print("Type your message and press Enter. Commands:")
        print("  /exit or /quit - End the chat session")
        print("  /clear - Clear conversation history")
        print("  /history - Show conversation history")
        print("  /debug - Show Ollama connection status")
        print("=" * 70)
        print()
        
        while True:
            try:
                # Get user input
                user_input = input("\n💬 YOU: ").strip()
                
                if not user_input:
                    continue
                
                # Handle special commands
                if user_input.lower() in ['/exit', '/quit']:
                    print("\n👋 Ending chat session. Goodbye!")
                    break
                
                elif user_input.lower() == '/clear':
                    self.conversation_history.clear()
                    print("🧹 Conversation history cleared.")
                    continue
                
                elif user_input.lower() == '/history':
                    print("\n" + "=" * 70)
                    print("CONVERSATION HISTORY")
                    print("=" * 70)
                    for entry in self.conversation_history:
                        role = entry['role'].upper()
                        msg = entry['message'][:100] + "..." if len(entry['message']) > 100 else entry['message']
                        print(f"{role}: {msg}")
                    print("=" * 70)
                    continue
                
                elif user_input.lower() == '/debug':
                    print("\n" + "=" * 70)
                    print("MACHA ARCHITECTURE & STATUS")
                    print("=" * 70)
                    
                    print("\n🏗️  SYSTEM ARCHITECTURE:")
                    print(f"  Hostname: macha.coven.systems")
                    print(f"  Service: macha-autonomous.service (systemd)")
                    print(f"  Working Directory: /var/lib/macha")
                    
                    print("\n👤 EXECUTION CONTEXT:")
                    current_user = os.getenv('USER') or os.getenv('USERNAME') or 'unknown'
                    print(f"  Current User: {current_user}")
                    print(f"  UID: {os.getuid()}")
                    
                    # Check if user has sudo access
                    try:
                        result = subprocess.run(['sudo', '-n', 'true'], 
                                              capture_output=True, timeout=1)
                        if result.returncode == 0:
                            print(f"  Sudo Access: ✓ Yes (passwordless)")
                        else:
                            print(f"  Sudo Access: ⚠ Requires password")
                    except:
                        print(f"  Sudo Access: ❌ No")
                    
                    print(f"  Note: Chat runs as invoking user (you), using macha's tools")
                    
                    print("\n🧠 INFERENCE ENGINE:")
                    print(f"  Backend: Ollama")
                    print(f"  Host: {self.agent.ollama_host}")
                    print(f"  Model: {self.agent.model}")
                    print(f"  Service: ollama.service (systemd)")
                    print(f"  Queue Worker: ollama-queue-worker.service")
                    
                    print("\n💾 DATABASE:")
                    print(f"  Backend: ChromaDB")
                    print(f"  State: {self.agent.state_dir}")
                    
                    print("\n🔍 OLLAMA STATUS:")
                    # Try to query Ollama status
                    try:
                        import requests
                        # Check if Ollama is running
                        response = requests.get(f"{self.agent.ollama_host}/api/tags", timeout=5)
                        if response.status_code == 200:
                            models = response.json().get('models', [])
                            print(f"  Status: ✓ Running")
                            print(f"  Loaded models: {len(models)}")
                            for model in models:
                                name = model.get('name', 'unknown')
                                size = model.get('size', 0) / (1024**3)  # GB
                                is_current = "← ACTIVE" if name == self.agent.model else ""
                                print(f"    • {name} ({size:.1f} GB) {is_current}")
                        else:
                            print(f"  Status: ❌ Error (HTTP {response.status_code})")
                    except Exception as e:
                        print(f"  Status: ❌ Cannot connect: {e}")
                        print(f"  Hint: Check 'systemctl status ollama.service'")
                    
                    print("\n🛠️  TOOLS:")
                    print(f"  Enabled: {self.agent.enable_tools}")
                    if self.agent.enable_tools:
                        print(f"  Available tools: {len(self.agent.tools.get_tool_definitions())}")
                        print(f"  Architecture: Centralized command_patterns.py")
                    
                    print("\n💡 CONVERSATION:")
                    print(f"  History: {len(self.conversation_history)} messages")
                    print(f"  Session started: {self.session_start}")
                    
                    print("=" * 70)
                    continue
                
                # Process the message
                print("\n🤖 MACHA: ", end='', flush=True)
                response = self.process_message(user_input, verbose=False)
                print(response)
                
            except KeyboardInterrupt:
                print("\n\n👋 Chat interrupted. Use /exit to quit properly.")
                continue
            except EOFError:
                print("\n\n👋 Ending chat session. Goodbye!")
                break
            except Exception as e:
                print(f"\n❌ Error: {e}")
                import traceback
                traceback.print_exc()
                continue
    
    def ask_once(self, question: str, verbose: bool = True) -> str:
        """Ask a single question and return the response (for macha-ask command)
        
        Args:
            question: The question to ask
            verbose: Whether to show detailed context information
            
        Returns:
            Macha's response
        """
        response = self.process_message(question, verbose=verbose)
        return response


def main():
    """Main entry point for macha-chat"""
    session = MachaChatSession()
    session.run_interactive()


def ask_main():
    """Entry point for macha-ask"""
    if len(sys.argv) < 2:
        print("Usage: macha-ask <question>", file=sys.stderr)
        sys.exit(1)
    
    question = " ".join(sys.argv[1:])
    session = MachaChatSession()
    
    response = session.ask_once(question, verbose=True)
    
    print("\n" + "=" * 60)
    print("MACHA:")
    print("=" * 60)
    print(response)
    print("=" * 60)
    print()


if __name__ == "__main__":
    main()