"""
Vanilla vector search using FAISS index and OpenAI embeddings.
"""

import numpy as np
import faiss
from typing import Tuple, List, Optional
from openai import OpenAI

import pickle
import logging
from config import *
from utils import EmbeddingGenerator, classify_image

logger = logging.getLogger(__name__)

# Initialize OpenAI client
client = OpenAI(api_key=OPENAI_API_KEY)

# Global variables for lazy loading
_index = None
_texts = None
_metadata = None

def _load_vanilla_index():
    """Lazy load vanilla FAISS index and metadata."""
    global _index, _texts, _metadata
    
    if _index is None:
        try:
            if VANILLA_FAISS_INDEX.exists() and VANILLA_METADATA.exists():
                logger.info("Loading vanilla FAISS index...")
                
                # Load FAISS index
                _index = faiss.read_index(str(VANILLA_FAISS_INDEX))
                
                # Load metadata
                with open(VANILLA_METADATA, 'rb') as f:
                    data = pickle.load(f)
                
                if isinstance(data, list):
                    # New format with metadata list
                    _texts = [item['text'] for item in data]
                    _metadata = [item['metadata'] for item in data]
                else:
                    # Old format with dict
                    _texts = data.get('texts', [])
                    _metadata = data.get('metadata', [])
                
                logger.info(f"✓ Loaded vanilla index with {len(_texts)} documents")
            else:
                logger.warning("Vanilla index not found. Run preprocess.py first.")
                _index = None
                _texts = []
                _metadata = []
                
        except Exception as e:
            logger.error(f"Error loading vanilla index: {e}")
            _index = None
            _texts = []
            _metadata = []


def query(question: str, image_path: Optional[str] = None, top_k: int = None) -> Tuple[str, List[dict]]:
    """
    Query using vanilla vector search.
    
    Args:
        question: User's question
        image_path: Optional path to an image (for multimodal queries)
        top_k: Number of relevant chunks to retrieve
    
    Returns:
        Tuple of (answer, citations)
    """
    if top_k is None:
        top_k = DEFAULT_TOP_K
    
    # Load index if not already loaded
    _load_vanilla_index()
    
    if _index is None or len(_texts) == 0:
        return "Index not loaded. Please run preprocess.py first.", []
    
    # Generate query embedding using embedding generator
    embedding_gen = EmbeddingGenerator()
    query_embedding = embedding_gen.embed_text_openai([question])
    
    # Normalize for cosine similarity
    query_embedding = query_embedding.astype(np.float32)
    faiss.normalize_L2(query_embedding)
    
    # Search the index
    distances, indices = _index.search(query_embedding, top_k)
    
    # Collect retrieved chunks and citations
    retrieved_chunks = []
    citations = []
    sources_seen = set()
    
    for idx, distance in zip(indices[0], distances[0]):
        if idx < len(_texts) and distance > MIN_RELEVANCE_SCORE:
            chunk_text = _texts[idx]
            chunk_meta = _metadata[idx]
            
            retrieved_chunks.append({
                'text': chunk_text,
                'score': float(distance),
                'metadata': chunk_meta
            })
            
            # Build citation
            if chunk_meta['source'] not in sources_seen:
                citation = {
                    'source': chunk_meta['source'],
                    'type': chunk_meta['type'],
                    'relevance_score': round(float(distance), 3)
                }
                
                if chunk_meta['type'] == 'pdf':
                    citation['path'] = chunk_meta['path']
                else:  # HTML
                    citation['url'] = chunk_meta.get('url', '')
                
                citations.append(citation)
                sources_seen.add(chunk_meta['source'])
    
    # Handle image if provided
    image_context = ""
    if image_path:
        try:
            classification = classify_image(image_path)
            image_context = f"\n\n[Image Context: The provided image appears to be a {classification}.]"
        except Exception as e:
            logger.error(f"Error processing image: {e}")
    
    # Build context for the prompt
    context = "\n\n---\n\n".join([chunk['text'] for chunk in retrieved_chunks])
    
    if not context:
        return "No relevant documents found for your query.", []
    
    # Generate answer using OpenAI
    prompt = f"""Use the following context to answer the question:

{context}{image_context}

Question: {question}

Please provide a comprehensive answer based on the context provided. If the context doesn't contain enough information, say so."""
    
    # For GPT-5, temperature must be default (1.0)
    response = client.chat.completions.create(
        model=OPENAI_CHAT_MODEL,
        messages=[
            {"role": "system", "content": "You are a helpful assistant for manufacturing equipment safety. Always cite your sources when providing information."},
            {"role": "user", "content": prompt}
        ],
        max_completion_tokens=DEFAULT_MAX_TOKENS
    )
    
    answer = response.choices[0].message.content
    
    return answer, citations


def query_with_feedback(question: str, feedback_scores: List[float] = None, top_k: int = 5) -> Tuple[str, List[dict]]:
    """
    Query with relevance feedback to refine results.
    
    Args:
        question: User's question
        feedback_scores: Optional relevance scores for previous results
        top_k: Number of relevant chunks to retrieve
    
    Returns:
        Tuple of (answer, citations)
    """
    # For now, just use regular query
    # TODO: Implement Rocchio algorithm or similar for relevance feedback
    return query(question, top_k=top_k)


if __name__ == "__main__":
    # Test the vanilla query
    test_questions = [
        "What are general machine guarding requirements?",
        "How do I perform lockout/tagout procedures?",
        "What safety measures are needed for robotic systems?"
    ]
    
    for q in test_questions:
        print(f"\nQuestion: {q}")
        answer, citations = query(q)
        print(f"Answer: {answer[:200]}...")
        print(f"Citations: {[c['source'] for c in citations]}")
        print("-" * 50)