import os
from src.train import train_vae
import pandas as pd

def continual_train(progress_callback=None):
    """
    Fine-tune the VAE on new data. Optionally log progress via progress_callback.
    """
    # Assume new data is already in data/new_data.csv and preprocessed
    if not os.path.exists("data/new_data.csv"):
        print("No new data found for continual training.")
        return
    # Optionally, preprocess new data if needed (skipped for simplicity)
    # For now, just retrain on all processed data
    print("Loading all processed data for fine-tuning...")
    if os.path.exists("data/processed_patient_data.csv"):
        feature_df = pd.read_csv("data/processed_patient_data.csv")
        # Optionally, append new data
        new_df = pd.read_csv("data/new_data.csv")
        feature_df = pd.concat([feature_df, new_df], ignore_index=True)
        feature_df.to_csv("data/processed_patient_data.csv", index=False)
    else:
        feature_df = pd.read_csv("data/new_data.csv")
        feature_df.to_csv("data/processed_patient_data.csv", index=False)
    print(f"Fine-tuning on {feature_df.shape[0]} samples...")
    # Call train_vae with progress_callback
    train_vae(progress_callback=progress_callback)