"""
Document enrichment module for the RAG solution.

This module handles loading documents from the data directory,
processing them with appropriate loaders, splitting them into chunks,
and storing them in the vector database with proper metadata.
"""

import os
import hashlib
from pathlib import Path
from typing import List, Dict, Any
from datetime import datetime
import sqlite3
from loguru import logger

from llama_index.core import SimpleDirectoryReader, Document
from llama_index.core.node_parser import SentenceSplitter, CodeSplitter
# Removed unused import

from vector_storage import get_vector_store_and_index


class DocumentTracker:
    """Class to handle tracking of processed documents to avoid re-processing."""

    def __init__(self, db_path: str = "document_tracking.db"):
        self.db_path = db_path
        self._init_db()

    def _init_db(self):
        """Initialize the SQLite database for document tracking."""
        conn = sqlite3.connect(self.db_path)
        cursor = conn.cursor()

        # Create table for tracking processed documents
        cursor.execute('''
            CREATE TABLE IF NOT EXISTS processed_documents (
                id INTEGER PRIMARY KEY AUTOINCREMENT,
                filename TEXT UNIQUE NOT NULL,
                filepath TEXT NOT NULL,
                checksum TEXT NOT NULL,
                processed_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
                metadata_json TEXT
            )
        ''')

        conn.commit()
        conn.close()
        logger.info(f"Document tracker initialized with database: {self.db_path}")

    def is_document_processed(self, filepath: str) -> bool:
        """Check if a document has already been processed."""
        conn = sqlite3.connect(self.db_path)
        cursor = conn.cursor()

        # Calculate checksum of the file
        checksum = self._calculate_checksum(filepath)

        cursor.execute(
            "SELECT COUNT(*) FROM processed_documents WHERE filepath = ? AND checksum = ?",
            (filepath, checksum)
        )
        count = cursor.fetchone()[0]

        conn.close()
        return count > 0

    def mark_document_processed(self, filepath: str, metadata: Dict[str, Any] = None):
        """Mark a document as processed in the database."""
        conn = sqlite3.connect(self.db_path)
        cursor = conn.cursor()

        checksum = self._calculate_checksum(filepath)
        filename = Path(filepath).name

        try:
            cursor.execute('''
                INSERT OR REPLACE INTO processed_documents
                (filename, filepath, checksum, processed_at, metadata_json)
                VALUES (?, ?, ?, CURRENT_TIMESTAMP, ?)
            ''', (filename, filepath, checksum, str(metadata) if metadata else None))

            conn.commit()
            logger.info(f"Document marked as processed: {filepath}")
        except sqlite3.Error as e:
            logger.error(f"Error marking document as processed: {e}")
        finally:
            conn.close()

    def _calculate_checksum(self, filepath: str) -> str:
        """Calculate MD5 checksum of a file."""
        hash_md5 = hashlib.md5()
        with open(filepath, "rb") as f:
            # Read file in chunks to handle large files efficiently
            for chunk in iter(lambda: f.read(4096), b""):
                hash_md5.update(chunk)
        return hash_md5.hexdigest()


def get_text_splitter(file_extension: str):
    """Get appropriate text splitter based on file type."""
    from llama_index.core.node_parser import SentenceSplitter, CodeSplitter, TokenTextSplitter
    from llama_index.core.node_parser import MarkdownElementNodeParser

    # For code files, use CodeSplitter
    if file_extension.lower() in ['.py', '.js', '.ts', '.java', '.cpp', '.c', '.h', '.cs', '.go', '.rs', '.php', '.html', '.css', '.md', '.rst']:
        return CodeSplitter(language="python", max_chars=1000)

    # For PDF files, use a parser that can handle multi-page documents
    elif file_extension.lower() == '.pdf':
        return SentenceSplitter(
            chunk_size=512,  # Smaller chunks for dense PDF content
            chunk_overlap=100
        )

    # For presentation files (PowerPoint), use smaller chunks
    elif file_extension.lower() == '.pptx':
        return SentenceSplitter(
            chunk_size=256,  # Slides typically have less text
            chunk_overlap=50
        )

    # For spreadsheets, use smaller chunks
    elif file_extension.lower() == '.xlsx':
        return SentenceSplitter(
            chunk_size=256,
            chunk_overlap=50
        )

    # For text-heavy documents like Word, use medium-sized chunks
    elif file_extension.lower() in ['.docx', '.odt']:
        return SentenceSplitter(
            chunk_size=768,
            chunk_overlap=150
        )

    # For plain text files, use larger chunks
    elif file_extension.lower() == '.txt':
        return SentenceSplitter(
            chunk_size=1024,
            chunk_overlap=200
        )

    # For image files, we'll handle them differently (metadata extraction)
    elif file_extension.lower() in ['.png', '.jpg', '.jpeg', '.gif', '.bmp', '.svg']:
        # Images will be handled by multimodal models, return a simple splitter
        return SentenceSplitter(
            chunk_size=512,
            chunk_overlap=100
        )

    # For other files, use a standard SentenceSplitter
    else:
        return SentenceSplitter(
            chunk_size=768,
            chunk_overlap=150
        )


def process_documents_from_data_folder(data_path: str = "../../../data", recursive: bool = True):
    """
    Process all documents from the data folder using appropriate loaders and store in vector DB.

    Args:
        data_path: Path to the data folder relative to current directory
        recursive: Whether to process subdirectories recursively
    """
    logger.info(f"Starting document enrichment from: {data_path}")

    # Initialize document tracker
    tracker = DocumentTracker()

    # Initialize vector storage
    vector_store, index = get_vector_store_and_index()

    # Get the absolute path to the data directory
    # The data_path is relative to the current working directory
    data_abs_path = Path(data_path)

    # If the path is relative, resolve it from the current working directory
    if not data_abs_path.is_absolute():
        data_abs_path = Path.cwd() / data_abs_path

    logger.info(f"Looking for documents in: {data_abs_path.absolute()}")

    if not data_abs_path.exists():
        logger.error(f"Data directory does not exist: {data_abs_path.absolute()}")
        return

    # Find all supported files in the data directory
    supported_extensions = {
        '.pdf', '.docx', '.xlsx', '.pptx', '.odt', '.txt',
        '.png', '.jpg', '.jpeg', '.gif', '.bmp', '.svg',
        '.zip', '.rar', '.tar', '.gz'
    }

    # Walk through the directory structure
    all_files = []
    if recursive:
        for root, dirs, files in os.walk(data_abs_path):
            for file in files:
                file_ext = Path(file).suffix.lower()
                if file_ext in supported_extensions:
                    all_files.append(os.path.join(root, file))
    else:
        for file in data_abs_path.iterdir():
            if file.is_file():
                file_ext = file.suffix.lower()
                if file_ext in supported_extensions:
                    all_files.append(str(file))

    logger.info(f"Found {len(all_files)} files to process")

    processed_count = 0
    skipped_count = 0

    for file_path in all_files:
        logger.info(f"Processing file: {file_path}")

        # Check if document has already been processed
        if tracker.is_document_processed(file_path):
            logger.info(f"Skipping already processed file: {file_path}")
            skipped_count += 1
            continue

        try:
            # Load the document using SimpleDirectoryReader
            # This automatically selects the appropriate reader based on file extension
            def file_metadata_func(file_path_str):
                return {"filename": Path(file_path_str).name}

            reader = SimpleDirectoryReader(
                input_files=[file_path],
                file_metadata=file_metadata_func
            )
            documents = reader.load_data()

            # Process each document
            for doc in documents:
                # Extract additional metadata based on document type
                file_ext = Path(file_path).suffix

                # Add additional metadata
                doc.metadata["file_path"] = file_path
                doc.metadata["processed_at"] = datetime.now().isoformat()

                # Handle document-type-specific metadata
                if file_ext.lower() == '.pdf':
                    # PDF-specific metadata
                    doc.metadata["page_label"] = doc.metadata.get("page_label", "unknown")
                    doc.metadata["file_type"] = "pdf"

                elif file_ext.lower() in ['.docx', '.odt']:
                    # Word document metadata
                    doc.metadata["section"] = doc.metadata.get("section", "unknown")
                    doc.metadata["file_type"] = "document"

                elif file_ext.lower() == '.pptx':
                    # PowerPoint metadata
                    doc.metadata["slide_id"] = doc.metadata.get("slide_id", "unknown")
                    doc.metadata["file_type"] = "presentation"

                elif file_ext.lower() == '.xlsx':
                    # Excel metadata
                    doc.metadata["sheet_name"] = doc.metadata.get("sheet_name", "unknown")
                    doc.metadata["file_type"] = "spreadsheet"

                # Determine the appropriate text splitter based on file type
                splitter = get_text_splitter(file_ext)

                # Split the document into nodes
                nodes = splitter.get_nodes_from_documents([doc])

                # Insert nodes into the vector index
                nodes_with_enhanced_metadata = []
                for i, node in enumerate(nodes):
                    # Enhance node metadata with additional information
                    node.metadata["original_doc_id"] = doc.doc_id
                    node.metadata["chunk_number"] = i
                    node.metadata["total_chunks"] = len(nodes)
                    node.metadata["file_path"] = file_path
                    nodes_with_enhanced_metadata.append(node)

                # Add all nodes to the index at once
                if nodes_with_enhanced_metadata:
                    index.insert_nodes(nodes_with_enhanced_metadata)

                logger.info(f"Processed {len(nodes)} nodes from {file_path}")

            # Mark document as processed only after successful insertion
            tracker.mark_document_processed(file_path, {"nodes_count": len(documents)})
            processed_count += 1

        except Exception as e:
            logger.error(f"Error processing file {file_path}: {str(e)}")
            continue

    logger.info(f"Document enrichment completed. Processed: {processed_count}, Skipped: {skipped_count}")


def enrich_documents():
    """Main function to run the document enrichment process."""
    logger.info("Starting document enrichment process")
    process_documents_from_data_folder()
    logger.info("Document enrichment process completed")


if __name__ == "__main__":
    # Example usage
    logger.info("Running document enrichment...")
    enrich_documents()