import os
import sys
from llama_cpp import Llama
import json
import re

current_dir = os.path.dirname(os.path.abspath(__file__))
parent_dir = os.path.dirname(current_dir)
sys.path.append(parent_dir)

from core.rag.store import VectorStore

class CreativeDirector:
    def __init__(self, llm_instance: Llama):
        if not llm_instance:
            raise ValueError("CreativeDirector received an invalid LLM instance.")
        print("🧠 Initializing AI Director with pre-loaded LLM...")
        self.llm = llm_instance
        self.memory = VectorStore(collection_name="creative_mind")
        print("✅ AI Director is Online.")

    def chat(self, user_message: str, history: list, task_context: str):
        """Main Chat Logic with RAG, optimized for speed."""
        
        print(f"   - 🧠 Thinking...")
        
        retrieved_docs = self.memory.search(user_message, n_results=1)
        expert_knowledge = retrieved_docs[0][:150] if retrieved_docs else "Be creative and direct."

        prompt = f"""Instruction: Act as a Viral Content Expert. Give 1 short tip for "{task_context}".
Context: {expert_knowledge}
User: {user_message}
Response:"""

        try:
            response = self.llm(
                prompt,
                max_tokens=50, 
                stop=["Instruction:", "User:", "\n\n"], 
                temperature=0.7,
                echo=False
            )
            
            reply = response['choices'][0]['text'].strip()
            if not reply:
                return "Try showing a 'before vs after' comparison. It always works!"
                
            print(f"   - 🗣️ Reply: {reply}")
            return reply
            
        except Exception as e:
            print(f"   - ❌ AI Chat Error: {e}")
            return "My AI brain is a bit slow today. Please ask again!"

    def generate_final_plan(self, task_context: str, history: list):
        """Generates the final script using simple text fallback."""
        print(f"   - 🎬 Generating final plan for: {task_context}")

        conversation_summary = "\n".join([f"- {msg['content']}" for msg in history[-3:]])

        prompt = f"""Instruction: Create a video script for "{task_context}".
Chat Summary: {conversation_summary}

Format your answer exactly like this:
HOOK: (Write hook here)
SCRIPT: (Write script here)
VISUALS: (Write visuals here)
TOOLS: (Write tools here)

Response:"""

        try:
            response = self.llm(
                prompt,
                max_tokens=300, 
                stop=["Instruction:", "Response:"],
                temperature=0.6,
                echo=False
            )
            
            raw_text = response['choices'][0]['text'].strip()
            print(f"   - 🤖 Raw Text: {raw_text}")

            hook_match = re.search(r'HOOK:\s*(.*?)(?=\nSCRIPT:)', raw_text, re.DOTALL | re.IGNORECASE)
            script_match = re.search(r'SCRIPT:\s*(.*?)(?=\nVISUALS:)', raw_text, re.DOTALL | re.IGNORECASE)
            visuals_match = re.search(r'VISUALS:\s*(.*?)(?=\nTOOLS:)', raw_text, re.DOTALL | re.IGNORECASE)
            tools_match = re.search(r'TOOLS:\s*(.*)', raw_text, re.DOTALL | re.IGNORECASE)

            return {
                "hook": hook_match.group(1).strip() if hook_match else "Start with a bang!",
                "script": script_match.group(1).strip() if script_match else raw_text,
                "visuals": [visuals_match.group(1).strip()] if visuals_match else ["Talking Head"],
                "tools": [tools_match.group(1).strip()] if tools_match else ["CapCut"]
            }

        except Exception as e:
            print(f"   - ❌ Final Plan Generation Error: {e}")
            return {
                "hook": "Error generating plan.",
                "script": "Please try again later.",
                "visuals": [],
                "tools": []
            }