import faiss
import numpy as np

import re

def preprocess_transcript(text):
    """
    Preprocess the transcript by removing timestamps and speaker labels.
    Example input: "[Speaker Guest-1 - 00:13] This is a test."
    Example output: "This is a test."
    """
    # Remove patterns like [Speaker Guest-X - HH:MM]
    cleaned_text = re.sub(r'\[.*?\]', '', text)
    # Remove extra whitespace
    cleaned_text = ' '.join(cleaned_text.split())
    return cleaned_text
    
def chunk_text(text, chunk_size=300, overlap=50):
    # Preprocess the text to remove timestamps and speaker labels
    text = preprocess_transcript(text)
    
    if not text.strip():
        raise ValueError("Transcript is empty after preprocessing.")
    
    words = text.split()
    chunks = []
    for i in range(0, len(words), chunk_size - overlap):
        chunk = ' '.join(words[i:i + chunk_size])
        chunks.append(chunk)
    return chunks


def embed_chunks(chunks, embedder):
    print(f"Embedding {len(chunks)} chunks...")
    embeddings = embedder.encode(chunks)
    return np.array(embeddings), chunks

def create_faiss_index(embeddings):
    print(f"Creating FAISS index with {embeddings.shape[0]} embeddings...")
    index = faiss.IndexFlatL2(embeddings.shape[1])
    index.add(embeddings)
    print("FAISS index created successfully.")
    return index