import os
import logging
from typing import Dict, List, Optional
from dotenv import load_dotenv

from llama_index.core import (
    StorageContext,
    load_index_from_storage,
    Settings
)
# Standalone imports for Multimodal RAG
from llama_index.multi_modal_llms.openai import OpenAIMultiModal
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.embeddings.clip import ClipEmbedding

# Load environment variables
load_dotenv()

# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

class MultimodalRAGConfig:
    """Configuration for the Standalone Multimodal RAG Pipeline"""
    BASE_DIR = os.path.dirname(os.path.abspath(__file__))
    # Hardcoded to requested paths
    INDEX_DIR = os.path.join(BASE_DIR, "multimodal_rag_index")
    IMAGES_DIR = os.path.join(BASE_DIR, "extracted_images")
    
    # Models
    TEXT_EMBED_MODEL = "text-embedding-3-small"
    IMAGE_EMBED_MODEL = "ViT-B/32"
    LLM_MODEL = "gpt-4o"
    
    TOP_K = 3  # Retrieve top 3 text and top 3 images

class MultimodalRAGSystem:
    """
    Standalone Multimodal RAG System (Read-Only)
    """
    def __init__(self):
        self.config = MultimodalRAGConfig()
        self.index = None
        self.query_engine = None
        
        self._initialize_system()
        
    def _initialize_system(self):
        logger.info("Initializing Multimodal RAG System...")
        
        if not os.path.exists(self.config.INDEX_DIR):
            logger.error(f"Index directory not found: {self.config.INDEX_DIR}")
            raise FileNotFoundError(f"Index directory not found: {self.config.INDEX_DIR}")
            
        if not os.getenv("OPENAI_API_KEY"):
             logger.error("OPENAI_API_KEY not found in environment variables.")
             raise ValueError("OPENAI_API_KEY not found.")

        # 1. Setup Models
        logger.info("Setting up models...")
        text_embed = OpenAIEmbedding(model=self.config.TEXT_EMBED_MODEL)
        image_embed = ClipEmbedding(model_name=self.config.IMAGE_EMBED_MODEL)
        
        # GPT-4o for Multimodal Generation
        openai_mm_llm = OpenAIMultiModal(
            model=self.config.LLM_MODEL,
            max_new_tokens=512
        )
        
        # 2. Load Index
        logger.info(f"Loading index from {self.config.INDEX_DIR}...")
        storage_context = StorageContext.from_defaults(persist_dir=self.config.INDEX_DIR)
        
        self.index = load_index_from_storage(
            storage_context,
            embed_model=text_embed,
            image_embed_model=image_embed
        )
        
        # 3. Create Query Engine
        self.query_engine = self.index.as_query_engine(
            llm=openai_mm_llm,
            similarity_top_k=self.config.TOP_K,
            image_similarity_top_k=self.config.TOP_K
        )
        
        logger.info(f"System Ready! Model: {self.config.LLM_MODEL}")

    def ask(self, query_str: str) -> Dict:
        """
        Ask a question and return answer + source images
        """
        if not self.query_engine:
             raise RuntimeError("Query engine not initialized")

        logger.info(f"Querying: {query_str}")
        
        response = self.query_engine.query(query_str)
        
        source_images = []
        source_texts = []
        
        for node_score in response.source_nodes:
            node = node_score.node
            if node.metadata.get("image_source"):
                # It's an image node
                # Try to get image path from node attribute or metadata
                img_path = getattr(node, "image_path", None) or node.metadata.get("image_path")
                
                # Normalize path if possible to be relative or filename
                if img_path:
                     img_filename = os.path.basename(img_path)
                     # We assume app.py serves 'extracted_images' as static
                     # So let's provide a relative web path or just the filename for app.py to handle
                     web_path = f"/extracted_images/{img_filename}"
                else:
                     web_path = None
                     img_filename = "unknown"

                source_images.append({
                    "path": web_path,
                    "filename": img_filename,
                    "score": node_score.score,
                    "page": node.metadata.get("page_number"),
                    "file": node.metadata.get("file_name")
                })
            else:
                # Text node
                file_name = node.metadata.get("file_name", "N/A")
                page_num = node.metadata.get("page_number", "N/A")
                
                web_link = None
                if file_name != "N/A":
                    # URL encode the filename to handle spaces and special chars safely
                    from urllib.parse import quote
                    safe_filename = quote(file_name)
                    web_link = f"/documents/{safe_filename}"
                    
                    if page_num != "N/A":
                        web_link += f"#page={page_num}"
                    
                    # DEBUG: Print link construction details
                    logger.info(f"DEBUG: File: {file_name}, Page: {page_num}, Link: {web_link}")
                
                source_texts.append({
                    "text": node.text[:200] + "...",
                    "score": node_score.score,
                    "page": page_num,
                    "file": file_name,
                    "link": web_link
                })
                
        return {
            "answer": str(response),
            "images": source_images,
            "texts": source_texts
        }

# Main for simple testing
def main():
    try:
        rag = MultimodalRAGSystem()
        while True:
            q = input("Query (q to quit): ")
            if q.lower() == 'q': break
            print(rag.ask(q))
    except Exception as e:
        print(f"Error: {e}")

if __name__ == "__main__":
    main()