import os
import shutil
import pandas as pd

# Define the image folder and CSV files
image_folder = "images/"
train_csv = 'train_caption.csv'
val_csv = 'validation_caption.csv'
test_csv = 'test_caption.csv'

# Load the CSV files
train_df = pd.read_csv(train_csv)
val_df = pd.read_csv(val_csv)
test_df = pd.read_csv(test_csv)

# Create directories for train, test, validation images
train_dir = 'images/train/'
val_dir = 'images/validation/'
test_dir = 'images/test/'

os.makedirs(train_dir, exist_ok=True)
os.makedirs(val_dir, exist_ok=True)
os.makedirs(test_dir, exist_ok=True)

# Define a function to copy images to the respective folder
def copy_images(df, destination_folder):
    for file_name in df['file_name']:
        # Construct the destination path
        dest_path = os.path.join(destination_folder, os.path.basename(file_name))
        # Copy the image
        if os.path.exists(file_name):
            shutil.copy(file_name, dest_path)

# Copy the images based on the CSVs
copy_images(train_df, train_dir)
copy_images(val_df, val_dir)
copy_images(test_df, test_dir)

# Load the CSV files
# train_df = pd.read_csv('train.csv')
# val_df = pd.read_csv('validation.csv')
# test_df = pd.read_csv('test.csv')

# # Update file_name with respective image directories
# train_df['file_name'] = 'images/train/' + train_df['file_name'].apply(os.path.basename)
# val_df['file_name'] = 'images/validation/' + val_df['file_name'].apply(os.path.basename)
# test_df['file_name'] = 'images/test/' + test_df['file_name'].apply(os.path.basename)

# # Combine all three datasets
# combined_df = pd.concat([train_df, val_df, test_df], ignore_index=True)

# # Save the combined dataset
# combined_df.to_csv('metadata.csv', index=False)