import os
import faiss
import numpy as np
import torch
from sentence_transformers import SentenceTransformer
from .file_handler import extract_text_from_file

v_device = "cuda" if torch.cuda.is_available() else "cpu"
obj_embedding_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2', device=v_device)

def process_files_to_vectors(v_folder_path):
    """
    Processes files (PDF, PPTX, CSV) found in v_folder_path to build a FAISS vector DB.
    Stores only the reference path in metadata.json (no file name or full path).
    """
    v_vector_folder = os.path.join(v_folder_path, 'vectors')
    os.makedirs(v_vector_folder, exist_ok=True)

    # Create a FAISS index (384 dimensions for all-MiniLM-L6-v2)
    v_index = faiss.IndexFlatL2(384)
    v_metadata = {}
    v_doc_counter = 0

    for v_root, _, v_files in os.walk(v_folder_path):
        for v_file in v_files:
            v_file_path = os.path.join(v_root, v_file)
            
            # Filter files by extension
            if v_file_path.lower().endswith(('.pdf', '.pptx', '.csv')):
                v_text = extract_text_from_file(v_file_path)
                if not v_text.strip():
                    continue  # skip empty files

                # Convert text to embeddings
                v_embeddings = obj_embedding_model.encode([v_text], convert_to_tensor=True).cpu().numpy()
                v_index.add(v_embeddings)

                # Generate a relative path (reference path only)
                v_reference_path = os.path.relpath(v_file_path, start=v_folder_path)

                # Store only the reference path in metadata
                v_metadata[v_doc_counter] = v_reference_path
                v_doc_counter += 1

    # Save the FAISS index
    v_index_path = os.path.join(v_vector_folder, 'vector_index.faiss')
    faiss.write_index(v_index, v_index_path)

    # Save metadata (containing only reference paths)
    import json
    with open(os.path.join(v_vector_folder, 'metadata.json'), 'w', encoding='utf-8') as obj_meta:
        json.dump(v_metadata, obj_meta, indent=4)

    return v_vector_folder