import os
import asyncio
import argparse
try:
    import dotenv
    dotenv.load_dotenv()
except ModuleNotFoundError:
    # python-dotenv is not installed; skip loading environment variables
    dotenv = None
from pathlib import Path
from typing import List, Any
from leann.api import LeannBuilder, LeannSearcher, LeannChat
from llama_index.core.node_parser import SentenceSplitter

# dotenv.load_dotenv()  # handled above if python-dotenv is available

# Default Chrome profile path
DEFAULT_CHROME_PROFILE = os.path.expanduser("~/Library/Application Support/Google/Chrome/Default")

def create_leann_index_from_multiple_chrome_profiles(profile_dirs: List[Path], index_path: str = "chrome_history_index.leann", max_count: int = -1):
    """
    Create LEANN index from multiple Chrome profile data sources.
    
    Args:
        profile_dirs: List of Path objects pointing to Chrome profile directories
        index_path: Path to save the LEANN index
        max_count: Maximum number of history entries to process per profile
    """
    print("Creating LEANN index from multiple Chrome profile data sources...")
    
    # Load documents using ChromeHistoryReader from history_data
    from history_data.history import ChromeHistoryReader
    reader = ChromeHistoryReader()
    
    INDEX_DIR = Path(index_path).parent
    
    if not INDEX_DIR.exists():
        print(f"--- Index directory not found, building new index ---")
        all_documents = []
        total_processed = 0
        
        # Process each Chrome profile directory
        for i, profile_dir in enumerate(profile_dirs):
            print(f"\nProcessing Chrome profile {i+1}/{len(profile_dirs)}: {profile_dir}")
            
            try:
                documents = reader.load_data(
                    chrome_profile_path=str(profile_dir),
                    max_count=max_count
                )
                if documents:
                    print(f"Loaded {len(documents)} history documents from {profile_dir}")
                    all_documents.extend(documents)
                    total_processed += len(documents)
                    
                    # Check if we've reached the max count
                    if max_count > 0 and total_processed >= max_count:
                        print(f"Reached max count of {max_count} documents")
                        break
                else:
                    print(f"No documents loaded from {profile_dir}")
            except Exception as e:
                print(f"Error processing {profile_dir}: {e}")
                continue
        
        if not all_documents:
            print("No documents loaded from any source. Exiting.")
            # highlight info that you need to close all chrome browser before running this script and high light the instruction!!
            print("\033[91mYou need to close or quit all chrome browser before running this script\033[0m")
            return None
        
        print(f"\nTotal loaded {len(all_documents)} history documents from {len(profile_dirs)} profiles")
        
        # Create text splitter with 256 chunk size
        text_splitter = SentenceSplitter(chunk_size=256, chunk_overlap=128)
        
        # Convert Documents to text strings and chunk them
        all_texts = []
        for doc in all_documents:
            # Split the document into chunks
            nodes = text_splitter.get_nodes_from_documents([doc])
            for node in nodes:
                text = node.get_content()
                # text = '[Title] ' + doc.metadata["title"] + '\n' + text
                all_texts.append(text)
        
        print(f"Created {len(all_texts)} text chunks from {len(all_documents)} documents")
        
        # Create LEANN index directory
        print(f"--- Index directory not found, building new index ---")
        INDEX_DIR.mkdir(exist_ok=True)

        print(f"--- Building new LEANN index ---")
        
        print(f"\n[PHASE 1] Building Leann index...")

        # Use HNSW backend for better macOS compatibility
        builder = LeannBuilder(
            backend_name="hnsw",
            embedding_model="facebook/contriever",
            graph_degree=32, 
            complexity=64,
            is_compact=True,
            is_recompute=True,
            num_threads=1  # Force single-threaded mode
        )

        print(f"Adding {len(all_texts)} history chunks to index...")
        for chunk_text in all_texts:
            builder.add_text(chunk_text)
            
        builder.build_index(index_path)
        print(f"\nLEANN index built at {index_path}!")
    else:
        print(f"--- Using existing index at {INDEX_DIR} ---")
    
    return index_path

def create_leann_index(profile_path: str = None, index_path: str = "chrome_history_index.leann", max_count: int = 1000):
    """
    Create LEANN index from Chrome history data.
    
    Args:
        profile_path: Path to the Chrome profile directory (optional, uses default if None)
        index_path: Path to save the LEANN index
        max_count: Maximum number of history entries to process
    """
    print("Creating LEANN index from Chrome history data...")
    INDEX_DIR = Path(index_path).parent
    
    if not INDEX_DIR.exists():
        print(f"--- Index directory not found, building new index ---")
        INDEX_DIR.mkdir(exist_ok=True)

        print(f"--- Building new LEANN index ---")
        
        print(f"\n[PHASE 1] Building Leann index...")

        # Load documents using ChromeHistoryReader from history_data
        from history_data.history import ChromeHistoryReader
        reader = ChromeHistoryReader()
        
        documents = reader.load_data(
            chrome_profile_path=profile_path,
            max_count=max_count
        )
        
        if not documents:
            print("No documents loaded. Exiting.")
            return None
        
        print(f"Loaded {len(documents)} history documents")
        
        # Create text splitter with 256 chunk size
        text_splitter = SentenceSplitter(chunk_size=256, chunk_overlap=25)
        
        # Convert Documents to text strings and chunk them
        all_texts = []
        for doc in documents:
            # Split the document into chunks
            nodes = text_splitter.get_nodes_from_documents([doc])
            for node in nodes:
                all_texts.append(node.get_content())
        
        print(f"Created {len(all_texts)} text chunks from {len(documents)} documents")
        
        # Create LEANN index directory
        print(f"--- Index directory not found, building new index ---")
        INDEX_DIR.mkdir(exist_ok=True)

        print(f"--- Building new LEANN index ---")
        
        print(f"\n[PHASE 1] Building Leann index...")

        # Use HNSW backend for better macOS compatibility
        builder = LeannBuilder(
            backend_name="hnsw",
            embedding_model="facebook/contriever",
            graph_degree=32, 
            complexity=64,
            is_compact=True,
            is_recompute=True,
            num_threads=1  # Force single-threaded mode
        )

        print(f"Adding {len(all_texts)} history chunks to index...")
        for chunk_text in all_texts:
            builder.add_text(chunk_text)
            
        builder.build_index(index_path)
        print(f"\nLEANN index built at {index_path}!")
    else:
        print(f"--- Using existing index at {INDEX_DIR} ---")
    
    return index_path

async def query_leann_index(index_path: str, query: str):
    """
    Query the LEANN index.
    
    Args:
        index_path: Path to the LEANN index
        query: The query string
    """
    print(f"\n[PHASE 2] Starting Leann chat session...")
    chat = LeannChat(index_path=index_path)
    
    print(f"You: {query}")
    chat_response = chat.ask(
        query, 
        top_k=10, 
        recompute_beighbor_embeddings=True,
        complexity=32,
        beam_width=1,
        llm_config={
            "type": "openai",
            "model": "gpt-4o",
            "api_key": os.getenv("OPENAI_API_KEY"),
        },
        llm_kwargs={
            "temperature": 0.0,
            "max_tokens": 1000
        }
    )
    print(f"Leann: {chat_response}")

async def main():
    # Parse command line arguments
    parser = argparse.ArgumentParser(description='LEANN Chrome History Reader - Create and query browser history index')
    parser.add_argument('--chrome-profile', type=str, default=DEFAULT_CHROME_PROFILE,
                       help=f'Path to Chrome profile directory (default: {DEFAULT_CHROME_PROFILE}), usually you dont need to change this')
    parser.add_argument('--index-dir', type=str, default="./all_google_new",
                       help='Directory to store the LEANN index (default: ./chrome_history_index_leann_test)')
    parser.add_argument('--max-entries', type=int, default=1000,
                       help='Maximum number of history entries to process (default: 1000)')
    parser.add_argument('--query', type=str, default=None,
                       help='Single query to run (default: runs example queries)')
    parser.add_argument('--auto-find-profiles', action='store_true', default=True,
                       help='Automatically find all Chrome profiles (default: True)')
    
    args = parser.parse_args()
    
    INDEX_DIR = Path(args.index_dir)
    INDEX_PATH = str(INDEX_DIR / "chrome_history.leann")
    
    print(f"Using Chrome profile: {args.chrome_profile}")
    print(f"Index directory: {INDEX_DIR}")
    print(f"Max entries: {args.max_entries}")
    
    # Find Chrome profile directories
    from history_data.history import ChromeHistoryReader
    
    if args.auto_find_profiles:
        profile_dirs = ChromeHistoryReader.find_chrome_profiles()
        if not profile_dirs:
            print("No Chrome profiles found automatically. Exiting.")
            return
    else:
        # Use single specified profile
        profile_path = Path(args.chrome_profile)
        if not profile_path.exists():
            print(f"Chrome profile not found: {profile_path}")
            return
        profile_dirs = [profile_path]
    
    # Create or load the LEANN index from all sources
    index_path = create_leann_index_from_multiple_chrome_profiles(profile_dirs, INDEX_PATH, args.max_entries)
    
    if index_path:
        if args.query:
            # Run single query
            await query_leann_index(index_path, args.query)
        else:
            # Example queries
            queries = [
                "What websites did I visit about machine learning?",
                "Find my search history about programming"
            ]
            
            for query in queries:
                print("\n" + "="*60)
                await query_leann_index(index_path, query)

if __name__ == "__main__":
    asyncio.run(main())