"""
Insta-AutoApp Ingestion Pipeline
Preprocesses the OEM manual PDF into a FAISS vector index.

Usage:
    python ingest.py <path_to_pdf>
    
Example:
    python ingest.py manual/bronco_2023_manual.pdf
"""

import os
import sys
import re
import pickle
import logging
from pathlib import Path

import fitz  # PyMuPDF
import faiss
import numpy as np
from sentence_transformers import SentenceTransformer
from langchain_text_splitters import RecursiveCharacterTextSplitter

from config import (
    FAISS_INDEX_PATH,
    FAISS_DOCSTORE_PATH,
    EMBEDDING_MODEL,
    CHUNK_SIZE,
    CHUNK_OVERLAP
)

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

# =============================================================================
# Content Filtering
# =============================================================================

# Pages/sections to exclude (low-value content)
EXCLUDE_PATTERNS = [
    r"table of contents",
    r"^\s*index\s*$",
    r"alphabetical index",
    r"copyright.*ford",
    r"all rights reserved",
    r"^\s*page\s+\d+\s*$",
    r"^\s*\d+\s*$",  # Just page numbers
    r"www\.ford\.com",
    r"owner\.ford\.com",
]

# Sections to prioritize (high-value content)
PRIORITY_KEYWORDS = [
    "warning",
    "indicator",
    "light",
    "lamp",
    "symptom",
    "troubleshoot",
    "problem",
    "issue",
    "check engine",
    "drivetrain",
    "4x4",
    "four-wheel",
    "trail",
    "goat mode",
    "terrain",
    "brake",
    "steering",
    "overheat",
    "temperature",
    "oil pressure",
    "battery",
    "transmission",
    "traction control",
    "stability control",
    "abs",
    "tire pressure",
    "tpms",
]


def should_exclude_text(text: str) -> bool:
    """Check if text chunk should be excluded (low-value content)."""
    text_lower = text.lower().strip()
    
    # Exclude very short chunks
    if len(text_lower) < 50:
        return True
    
    # Check exclusion patterns
    for pattern in EXCLUDE_PATTERNS:
        if re.search(pattern, text_lower, re.IGNORECASE):
            return True
    
    return False


def is_priority_content(text: str) -> bool:
    """Check if text contains priority keywords (symptom-relevant content)."""
    text_lower = text.lower()
    return any(keyword in text_lower for keyword in PRIORITY_KEYWORDS)


# =============================================================================
# PDF Extraction
# =============================================================================

def extract_text_from_pdf(pdf_path: str) -> list[dict]:
    """
    Extract text from PDF using PyMuPDF.
    
    Args:
        pdf_path: Path to the PDF file
        
    Returns:
        List of dicts with 'text' and 'page' keys
    """
    logger.info(f"Opening PDF: {pdf_path}")
    doc = fitz.open(pdf_path)
    
    pages_content = []
    
    for page_num in range(len(doc)):
        page = doc[page_num]
        text = page.get_text("text")
        
        if text.strip():
            pages_content.append({
                "text": text,
                "page": page_num + 1  # 1-indexed
            })
    
    doc.close()
    logger.info(f"Extracted text from {len(pages_content)} pages")
    return pages_content


# =============================================================================
# Text Chunking
# =============================================================================

def chunk_documents(pages_content: list[dict]) -> list[dict]:
    """
    Split extracted text into chunks using LangChain.
    
    Args:
        pages_content: List of page dicts from extract_text_from_pdf
        
    Returns:
        List of chunk dicts with 'text', 'page', and 'is_priority' keys
    """
    # Combine all text with page markers
    full_text = ""
    page_boundaries = []
    
    for page_data in pages_content:
        start_idx = len(full_text)
        full_text += page_data["text"] + "\n\n"
        page_boundaries.append({
            "start": start_idx,
            "end": len(full_text),
            "page": page_data["page"]
        })
    
    # Create text splitter
    # Approximate tokens to characters (1 token ≈ 4 characters)
    chunk_size_chars = CHUNK_SIZE * 4
    chunk_overlap_chars = CHUNK_OVERLAP * 4
    
    splitter = RecursiveCharacterTextSplitter(
        chunk_size=chunk_size_chars,
        chunk_overlap=chunk_overlap_chars,
        separators=["\n\n", "\n", ". ", " ", ""],
        length_function=len
    )
    
    # Split text
    chunks_text = splitter.split_text(full_text)
    logger.info(f"Split into {len(chunks_text)} raw chunks")
    
    # Map chunks back to pages and filter
    chunks = []
    filtered_count = 0
    
    for chunk_text in chunks_text:
        # Skip excluded content
        if should_exclude_text(chunk_text):
            filtered_count += 1
            continue
        
        # Find which page this chunk came from (approximate)
        chunk_start = full_text.find(chunk_text[:100])  # Find by first 100 chars
        page_num = 1
        for boundary in page_boundaries:
            if boundary["start"] <= chunk_start < boundary["end"]:
                page_num = boundary["page"]
                break
        
        chunks.append({
            "text": chunk_text.strip(),
            "page": page_num,
            "is_priority": is_priority_content(chunk_text)
        })
    
    logger.info(f"Filtered out {filtered_count} low-value chunks")
    logger.info(f"Final chunk count: {len(chunks)}")
    
    # Log priority content stats
    priority_count = sum(1 for c in chunks if c["is_priority"])
    logger.info(f"Priority chunks (symptom-relevant): {priority_count}")
    
    return chunks


# =============================================================================
# Embedding and Index Creation
# =============================================================================

def create_faiss_index(chunks: list[dict]) -> tuple[faiss.Index, list[str], list[dict]]:
    """
    Create FAISS index from document chunks.
    
    Args:
        chunks: List of chunk dicts from chunk_documents
        
    Returns:
        Tuple of (faiss_index, documents_list, metadata_list)
    """
    # Load embedding model
    logger.info(f"Loading embedding model: {EMBEDDING_MODEL}")
    model = SentenceTransformer(EMBEDDING_MODEL)
    
    # Extract texts
    texts = [chunk["text"] for chunk in chunks]
    
    # Generate embeddings
    logger.info(f"Generating embeddings for {len(texts)} chunks...")
    embeddings = model.encode(texts, show_progress_bar=True, convert_to_numpy=True)
    embeddings = embeddings.astype("float32")
    
    # Create FAISS index
    dimension = embeddings.shape[1]
    logger.info(f"Creating FAISS index (dimension: {dimension})")
    index = faiss.IndexFlatL2(dimension)
    index.add(embeddings)
    
    # Prepare metadata
    metadata = [{"page": chunk["page"], "is_priority": chunk["is_priority"]} for chunk in chunks]
    
    return index, texts, metadata


def save_index(index: faiss.Index, documents: list[str], metadata: list[dict]):
    """Save FAISS index and document store to disk."""
    # Ensure data directory exists
    os.makedirs(os.path.dirname(FAISS_INDEX_PATH), exist_ok=True)
    
    # Save FAISS index
    logger.info(f"Saving FAISS index to {FAISS_INDEX_PATH}")
    faiss.write_index(index, FAISS_INDEX_PATH)
    
    # Save document store
    logger.info(f"Saving document store to {FAISS_DOCSTORE_PATH}")
    docstore = {
        "documents": documents,
        "metadata": metadata
    }
    with open(FAISS_DOCSTORE_PATH, "wb") as f:
        pickle.dump(docstore, f)
    
    logger.info("Index saved successfully!")


# =============================================================================
# Main
# =============================================================================

def main():
    if len(sys.argv) < 2:
        print("Usage: python ingest.py <path_to_pdf>")
        print("Example: python ingest.py manual/bronco_2023_manual.pdf")
        sys.exit(1)
    
    pdf_path = sys.argv[1]
    
    if not os.path.exists(pdf_path):
        logger.error(f"PDF file not found: {pdf_path}")
        sys.exit(1)
    
    logger.info("=" * 60)
    logger.info("Insta-AutoApp Ingestion Pipeline")
    logger.info("=" * 60)
    
    # Step 1: Extract text from PDF
    pages_content = extract_text_from_pdf(pdf_path)
    
    # Step 2: Chunk documents
    chunks = chunk_documents(pages_content)
    
    # Step 3: Create FAISS index
    index, documents, metadata = create_faiss_index(chunks)
    
    # Step 4: Save to disk
    save_index(index, documents, metadata)
    
    logger.info("=" * 60)
    logger.info("Ingestion complete!")
    logger.info(f"Total chunks indexed: {len(documents)}")
    logger.info(f"Index file: {FAISS_INDEX_PATH}")
    logger.info(f"Document store: {FAISS_DOCSTORE_PATH}")
    logger.info("=" * 60)


if __name__ == "__main__":
    main()