#!/usr/bin/env python3
"""
Claw Code Runtime - Layer 2 of Archon Architecture

This harness contains all business logic, orchestrating between
the thin Hermes profile (Layer 1) and Gemma intelligence (Layer 3).
"""

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

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

from runtime.tool_registry import ToolRegistry


class ClawHarness:
    """
    The Claw Runtime - where all logic lives.
    
    Responsibilities:
    - Message routing and orchestration
    - Tool execution management
    - Context window management
    - Conversation state tracking
    """
    
    def __init__(self, ollama_host: str = "http://localhost:11434"):
        self.ollama_host = ollama_host
        self.tools = ToolRegistry()
        self.conversation_history: List[Dict[str, Any]] = []
        self.session_id: Optional[str] = None
        
    def process_message(self, message: str, context: Optional[Dict] = None) -> Dict[str, Any]:
        """
        Main entry point for processing messages.
        
        Flow:
        1. Parse intent
        2. Determine if tools needed
        3. Route to Gemma or execute tools
        4. Return formatted response
        """
        context = context or {}
        
        # Log to conversation history
        self.conversation_history.append({
            "role": "user",
            "content": message,
            "timestamp": self._get_timestamp()
        })
        
        # Check for tool invocation
        tool_call = self.tools.parse_tool_call(message)
        if tool_call:
            result = self._execute_tool(tool_call)
            return self._format_response(
                content=result,
                tool_used=tool_call["name"],
                metadata={"tool_result": True}
            )
        
        # Route to intelligence layer (Gemma)
        return self._route_to_intelligence(message, context)
    
    def _route_to_intelligence(self, message: str, context: Dict) -> Dict[str, Any]:
        """Route message to Gemma via Ollama."""
        from ollama_client import OllamaClient
        
        client = OllamaClient(base_url=self.ollama_host)
        
        # Build prompt with context
        prompt = self._build_prompt(message, context)
        
        # Get response from Gemma
        response = client.generate(
            model="gemma3:4b",
            prompt=prompt,
            system=self._get_system_prompt()
        )
        
        # Log response
        self.conversation_history.append({
            "role": "assistant",
            "content": response.get("response", ""),
            "timestamp": self._get_timestamp()
        })
        
        return self._format_response(
            content=response.get("response", ""),
            metadata={
                "model": "gemma3:4b",
                "tokens_used": response.get("eval_count", 0)
            }
        )
    
    def _execute_tool(self, tool_call: Dict) -> str:
        """Execute a tool and return result."""
        return self.tools.execute(tool_call)
    
    def _build_prompt(self, message: str, context: Dict) -> str:
        """Build context-aware prompt for Gemma."""
        history = "\n".join([
            f"{msg['role']}: {msg['content']}"
            for msg in self.conversation_history[-5:]  # Last 5 messages
        ])
        
        return f"""Previous conversation:
{history}

User: {message}

Assistant:"""
    
    def _get_system_prompt(self) -> str:
        """Get system prompt for Gemma."""
        return """You are the Archon POC, a helpful AI assistant.
Be concise but thorough. Tag your response with #archon-poc."""
    
    def _format_response(self, content: str, tool_used: Optional[str] = None, 
                        metadata: Optional[Dict] = None) -> Dict[str, Any]:
        """Format response for return to Layer 1."""
        response = {
            "content": content,
            "status": "success",
            "layer": "claw_runtime",
            "tag": "#archon-poc"
        }
        
        if tool_used:
            response["tool_used"] = tool_used
        if metadata:
            response["metadata"] = metadata
            
        return response
    
    def _get_timestamp(self) -> str:
        """Get current timestamp."""
        from datetime import datetime
        return datetime.now().isoformat()


def main():
    """CLI entry point."""
    harness = ClawHarness()
    
    if len(sys.argv) > 1:
        message = " ".join(sys.argv[1:])
        result = harness.process_message(message)
        print(json.dumps(result, indent=2))
    else:
        # Interactive mode
        print("Archon Harness - Interactive Mode")
        print("Type 'exit' to quit\n")
        
        while True:
            try:
                message = input("> ")
                if message.lower() in ("exit", "quit"):
                    break
                    
                result = harness.process_message(message)
                print(f"\n{result['content']}\n")
                
            except KeyboardInterrupt:
                break
            except EOFError:
                break
        
        print("\nGoodbye!")


if __name__ == "__main__":
    main()