"""Base agent module for all specialized agents."""
import json
import logging
from datetime import datetime
from pathlib import Path
from typing import Any, Dict, Optional
import re
from openai import OpenAI
from config import settings


logger = logging.getLogger(__name__)


class BaseAgent:
    """Base class for all agents in the system."""

    def __init__(self, agent_id: str, agent_name: str, system_prompt: str):
        """Initialize the base agent.

        Args:
            agent_id: Unique identifier for the agent
            agent_name: Human-readable name of the agent
            system_prompt: System prompt for the agent's behavior
        """
        self.agent_id = agent_id
        self.agent_name = agent_name
        self.system_prompt = system_prompt
        
        # Initialize DashScope client (OpenAI-compatible API)
        self.client = OpenAI(
            api_key=settings.openrouter_api_key,
            base_url=settings.openrouter_base_url,
        )
        
        # Data storage
        self.data_dir = Path(settings.data_dir) / agent_id
        self.data_dir.mkdir(parents=True, exist_ok=True)
        
        logger.info(f"Initialized agent: {agent_name} ({agent_id})")

    def process(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
        """Process inputs and generate outputs using the LLM.

        Args:
            inputs: Dictionary containing input data for the agent

        Returns:
            Dictionary containing the agent's outputs
        """
        # Prepare the prompt
        input_text = json.dumps(inputs, indent=2)
        
        logger.info(f"{self.agent_name}: Processing inputs")
        
        try:
            # Call DashScope API via OpenAI client
            response = self.client.chat.completions.create(
                model=settings.model_name,
                messages=[
                    {
                        "role": "system",
                        "content": self.system_prompt,
                    },
                    {
                        "role": "user",
                        "content": f"Process the following inputs:\n\n{input_text}",
                    },
                ],
                temperature=0.7,
                max_tokens=4096,
            )
            
            output_text = response.choices[0].message.content
            
            # Parse output (assuming JSON format)
            try:
                # Attempt direct JSON parsing first
                output_data = json.loads(output_text)
            except json.JSONDecodeError:
                # If direct parsing fails, try to extract JSON from markdown or embedded text
                json_match = re.search(r'```json\s*(.*?)\s*```', output_text, re.DOTALL)
                if json_match:
                    try:
                        output_data = json.loads(json_match.group(1))
                    except json.JSONDecodeError:
                        # Fallback if markdown-wrapped JSON is malformed
                        output_data = {"output": output_text}
                else:
                    # Try to find any JSON object embedded in the text
                    json_obj_match = re.search(r'(\{.*\})', output_text, re.DOTALL)
                    if json_obj_match:
                        try:
                            output_data = json.loads(json_obj_match.group(1))
                        except json.JSONDecodeError:
                            # Fallback if embedded JSON is malformed
                            output_data = {"output": output_text}
                    else:
                        # If no JSON found, wrap the entire output
                        output_data = {"output": output_text}
            
            logger.info(f"{self.agent_name}: Processing completed")
            
            return output_data
            
        except Exception as e:
            logger.error(f"{self.agent_name}: Error during processing - {str(e)}")
            raise

    def save_state(self, data: Dict[str, Any], filename: Optional[str] = None) -> str:
        """Save agent state and data to persistent JSON storage.

        Args:
            data: Data to save
            filename: Optional custom filename (defaults to timestamp)

        Returns:
            Path to saved file
        """
        if filename is None:
            timestamp = datetime.now().isoformat().replace(":", "-")
            filename = f"{self.agent_id}_{timestamp}.json"
        
        filepath = self.data_dir / filename
        
        # Add metadata
        state_data = {
            "agent_id": self.agent_id,
            "agent_name": self.agent_name,
            "timestamp": datetime.now().isoformat(),
            "data": data,
        }
        
        with open(filepath, "w") as f:
            json.dump(state_data, f, indent=2)
        
        logger.info(f"{self.agent_name}: State saved to {filepath}")
        
        return str(filepath)

    def load_state(self, filename: str) -> Dict[str, Any]:
        """Load agent state from persistent storage.

        Args:
            filename: Filename to load

        Returns:
            Loaded data
        """
        filepath = self.data_dir / filename
        
        with open(filepath, "r") as f:
            state_data = json.load(f)
        
        logger.info(f"{self.agent_name}: State loaded from {filepath}")
        
        return state_data.get("data", {})