"""Instructor module for generating storytelling briefs from trending topics."""
import os
import json
import logging
from typing import Any, Dict, List, Optional
from base_agent import BaseAgent
from config import settings

logger = logging.getLogger(__name__)

class Instructor(BaseAgent):
    """Instructor Agent: Fuses trending topics into storytelling briefs.
    
    This agent uses Instructor.md as a persistent pre-prompt and calls the 
    DashScope API using the Qwen model specified in the environment.
    """

    def __init__(self):
        """Initialize the Instructor agent with guidelines from Instructor.md."""
        # Load guidelines from the root folder
        content = self._load_instructor_md()
        
        # Construct the full system prompt (pre-prompt)
        system_prompt = f"""You are the Instructor Agent, a master of viral storytelling and audience psychology.
Your primary role is to take trending topics and fuse them into compelling, viral-ready storytelling briefs.

### YOUR CORE PRINCIPLES (from Instructor.md):
{content}

### YOUR OBJECTIVE:
1. Analyze the trending topics provided by the user.
2. Synthesize these trends into a single, cohesive storytelling concept.
3. Ensure the concept leverages human psychology (relatability, conflict, transformation).
4. Output a structured JSON brief that will drive the multi-agent writing pipeline.

### OUTPUT JSON STRUCTURE:
{{
    "user_brief": "The main story prompt/premise based on the trends.",
    "hook_brief": "A specific 3-second hook to grab attention.",
    "style_guide": "The visual and tonal style (e.g., 'TikTok relatable comedy').",
    "character_bible": "Brief description of the key characters.",
    "world_building_document": "The setting and world rules.",
    "season_arc_document": "How this episode fits into a larger context.",
    "character_voice_guide": "How characters should speak.",
    "continuity_log": "The starting state of the world."
}}
"""
        super().__init__(
            agent_id="instructor",
            agent_name="Instructor",
            system_prompt=system_prompt
        )

    def _load_instructor_md(self) -> str:
        """Load the Instructor.md file from the root directory."""
        try:
            # Look for Instructor.md in the current working directory (root)
            path = "Instructor.md"
            if not os.path.exists(path):
                # Fallback to the directory of this file
                path = os.path.join(os.path.dirname(__file__), "Instructor.md")
            
            with open(path, "r") as f:
                return f.read()
        except Exception as e:
            logger.error(f"Failed to load Instructor.md: {str(e)}")
            return "Master viral storytelling and audience psychology."

    def generate_brief(self, trending_topics: List[str]) -> Dict[str, Any]:
        """Generate a storytelling brief from trending topics using the LLM.

        Args:
            trending_topics: List of topics to fuse.

        Returns:
            Dictionary containing the brief fields.
        """
        inputs = {
            "trending_topics": trending_topics,
            "instruction": "Fuse these trending topics into a viral storytelling brief following your core principles."
        }
        
        logger.info(f"[INSTRUCTOR] Calling DashScope API (Model: {settings.model_name}) to generate brief...")
        
        # Use the BaseAgent's process method which calls the LLM (DashScope/Qwen)
        brief = self.process(inputs)
        
        # Validation and defaults for the pipeline
        required = [
            "user_brief", "hook_brief", "style_guide", "character_bible",
            "world_building_document", "season_arc_document", 
            "character_voice_guide", "continuity_log"
        ]
        
        for field in required:
            if field not in brief:
                brief[field] = f"Auto-generated {field.replace('_', ' ')}."
                
        return brief