import gradio as gr
import pandas as pd
from clean import clean_data
from report import create_full_report, REPORT_DIR
import os
import tempfile


def clean_and_visualize(file, primary_key_column, progress=gr.Progress()):
    # Load the data
    df = pd.read_csv(file.name)

    # Remove duplicates from the primary key column
    df = df.drop_duplicates(subset=[primary_key_column], keep='first')

    # Clean the data
    cleaned_df = None
    nonconforming_cells_before = None
    process_times = None
    removed_columns = None
    removed_rows = None

    for progress_value, status_text in clean_data(df, primary_key_column):
        if isinstance(status_text, tuple):
            cleaned_df, nonconforming_cells_before, process_times, removed_columns, removed_rows = status_text
            progress(progress_value, desc="Cleaning completed")
        else:
            progress(progress_value, desc=status_text)

    # Generate full visualization report
    create_full_report(
        df,
        cleaned_df,
        nonconforming_cells_before,
        process_times,
        removed_columns,
        removed_rows,
        primary_key_column
    )

    # Save cleaned DataFrame to a temporary CSV file
    with tempfile.NamedTemporaryFile(delete=False, suffix='.csv') as tmp_file:
        cleaned_df.to_csv(tmp_file.name, index=False)
        cleaned_csv_path = tmp_file.name

    # Collect all generated images
    image_files = [os.path.join(REPORT_DIR, f) for f in os.listdir(REPORT_DIR) if f.endswith('.png')]

    return cleaned_csv_path, image_files


def launch_app():
    with gr.Blocks() as app:
        gr.Markdown("# AI Data Cleaner")

        with gr.Row():
            file_input = gr.File(label="Upload CSV File", file_count="single", file_types=[".csv"])

        with gr.Row():
            primary_key_dropdown = gr.Dropdown(label="Select Primary Key Column", choices=[], interactive=True)

        with gr.Row():
            clean_button = gr.Button("Start Cleaning")

        with gr.Row():
            progress_bar = gr.Progress()

        with gr.Row():
            cleaned_file_output = gr.File(label="Cleaned CSV", visible=True)

        with gr.Row():
            output_gallery = gr.Gallery(
                label="Visualization Results",
                show_label=True,
                elem_id="gallery",
                columns=[3],
                rows=[3],
                object_fit="contain",
                height="auto",
                visible=False
            )

        def update_primary_key_options(file):
            if file is not None:
                df = pd.read_csv(file.name)
                return gr.Dropdown(choices=df.columns.tolist())

        def process_and_show_results(file, primary_key_column):
            cleaned_csv_path, image_files = clean_and_visualize(file, primary_key_column, progress=progress_bar)
            return (
                cleaned_csv_path,
                gr.Gallery(visible=True, value=image_files)
            )

        file_input.change(
            fn=update_primary_key_options,
            inputs=file_input,
            outputs=primary_key_dropdown
        )

        clean_button.click(
            fn=process_and_show_results,
            inputs=[file_input, primary_key_dropdown],
            outputs=[cleaned_file_output, output_gallery]
        )

    app.launch()


if __name__ == "__main__":
    launch_app()