"""FAISS-based semantic search engine"""
import faiss
import numpy as np
from typing import List, Tuple, Optional
import os


class SearchEngine:
    """FAISS-based search engine for image embeddings"""
    
    def __init__(self, dim: int = 512, index_path: str = "faiss_index.bin"):
        self.dim = dim
        self.index_path = index_path
        self.id_map: List[int] = []  # Map FAISS indices to photo IDs
        
        # Load existing index or create new one
        if os.path.exists(index_path):
            self.index = faiss.read_index(index_path)
        else:
            self.index = faiss.IndexFlatL2(dim)
    
    def add_embedding(self, photo_id: int, embedding: np.ndarray) -> None:
        """
        Add an embedding to the index.
        
        Args:
            photo_id: Unique photo identifier
            embedding: 1D numpy array of shape (dim,)
        """
        # Ensure embedding is float32 and correct shape
        embedding = embedding.astype(np.float32).reshape(1, -1)
        
        # Add to FAISS index
        self.index.add(embedding)
        
        # Track photo ID
        self.id_map.append(photo_id)
        
        # Save index to disk
        self.save()
    
    def search(self, query_embedding: np.ndarray, top_k: int = 5) -> List[Tuple[int, float]]:
        """
        Search for similar embeddings.
        
        Args:
            query_embedding: 1D numpy array of shape (dim,)
            top_k: Number of results to return
        
        Returns:
            List of (photo_id, distance) tuples
        """
        if self.index.ntotal == 0:
            return []
        
        # Ensure query is float32 and correct shape
        query_embedding = query_embedding.astype(np.float32).reshape(1, -1)
        
        # Search in FAISS index
        distances, indices = self.index.search(query_embedding, min(top_k, self.index.ntotal))
        
        # Map back to photo IDs
        results = [
            (self.id_map[int(idx)], float(distance))
            for distance, idx in zip(distances[0], indices[0])
        ]
        
        return results
    
    def save(self) -> None:
        """Save index to disk"""
        faiss.write_index(self.index, self.index_path)
    
    def load(self) -> None:
        """Load index from disk"""
        if os.path.exists(self.index_path):
            self.index = faiss.read_index(self.index_path)
    
    def get_stats(self) -> dict:
        """Get index statistics"""
        return {
            "total_embeddings": self.index.ntotal,
            "dimension": self.dim,
            "id_map_size": len(self.id_map)
        }