import pandas as pd
import faiss
import pickle
import os
import argparse
from sentence_transformers import SentenceTransformer

def main():
    parser = argparse.ArgumentParser(description="Embed labeled reviews into FAISS vectorstore.")
    parser.add_argument('--input_csv', type=str, default='data/processed/amazon_labeled_reviews.csv', help='CSV with texts and predicted_aspects')
    parser.add_argument('--out_dir', type=str, default='vectorstore', help='Directory to output FAISS index')
    parser.add_argument('--text_column', type=str, default='reviewDocument', help='Name of the column containing the review text')
    parser.add_argument('--model_name', type=str, default='all-MiniLM-L6-v2', help='Sentence-transformers model')
    
    args = parser.parse_args()
    
    if not os.path.exists(args.input_csv):
        print(f"Error: {args.input_csv} not found. Please run src/prepare_amazon_data.py first.")
        return
        
    print(f"Loading data from {args.input_csv}...")
    df = pd.read_csv(args.input_csv)
    
    if args.text_column not in df.columns:
        print(f"Error: Column {args.text_column} not found in {args.input_csv}.")
        print(f"Available columns: {df.columns.tolist()}")
        return
        
    # Filter out empty texts
    df = df[df[args.text_column].notna() & (df[args.text_column] != '')]
    texts = df[args.text_column].tolist()
    
    print(f"Loading embedding model: {args.model_name}...")
    embedder = SentenceTransformer(args.model_name)
    
    print(f"Encoding {len(texts)} reviews into dense vectors...")
    # encode handles batching automatically
    embeddings = embedder.encode(texts, show_progress_bar=True, convert_to_numpy=True)
    
    # Initialize FAISS Index (L2 normalized Flat Inner Product = Cosine Similarity)
    dimension = embeddings.shape[1]
    index = faiss.IndexFlatIP(dimension)
    
    # Normalize before adding to FAISS for cosine similarity
    print("Normalizing vectors and adding to FAISS index...")
    faiss.normalize_L2(embeddings)
    index.add(embeddings)
    
    os.makedirs(args.out_dir, exist_ok=True)
    
    # Save FAISS index
    index_path = os.path.join(args.out_dir, 'reviews.index')
    faiss.write_index(index, index_path)
    
    # Save the dataframe mapping (so we can retrieve the actual text + labels later)
    df_path = os.path.join(args.out_dir, 'reviews_metadata.pkl')
    with open(df_path, 'wb') as f:
        pickle.dump(df, f)
        
    print(f"\nSuccessfully embedded {len(texts)} reviews and saved to '{args.out_dir}'!")

if __name__ == "__main__":
    main()