import pandas as pd
import os
import shutil

import gradio as gr

import utils_data_extraction
import utils_assessment

import importlib
importlib.reload(utils_data_extraction)
importlib.reload(utils_assessment)

"""### Function to load data

Data is loaded from a Roamler Excel file, from a sheet called "output".

- A subset of the Excel file is taken as reference data, and saved in the `outputs` directory as reference_data.csv
- A folder for storing photos is created

A n_rows parameter can be passed to load a subset of the data.
"""

def load_roamler_excel_file(filepath, n_rows=3):

    OUTPUT_DIR = 'outputs/'+os.path.basename(filepath)
    if not os.path.exists(OUTPUT_DIR):
        os.makedirs(OUTPUT_DIR)

    DATA_EXTRACTION_DIR=OUTPUT_DIR+'/data_extraction'
    if not os.path.exists(DATA_EXTRACTION_DIR):
        os.makedirs(DATA_EXTRACTION_DIR)

    df_review = pd.read_excel(filepath, sheet_name='Output')
    if n_rows is not None:
        df_review = df_review.sample(n=n_rows, random_state=42)

    df_products = df_review[['ID', 'Front photo', 'Nutritionals photo', 'Ingredients photo', 'EAN photo',
                           'Brand', 'Product name', 'Legal name', 'Barcode',
                           'Energy kJ', 'Energy kcal', 'Fat', 'Saturated fat', 'Carbohydrates', 'Sugars', 'Fibers', 'Proteins', 'Salt', 'Ingredients',
                           'Nutriscore','Allergens',
                           'Quantity per unit']].copy()

    df_products.to_csv(f'{OUTPUT_DIR}/data_extraction/reference_data.csv', index=False)

    PHOTO_DIR=OUTPUT_DIR+'/photos'
    if not os.path.exists(PHOTO_DIR):
        os.makedirs(PHOTO_DIR)

    df_brand_data, df_product_name_data, df_ingredients_data, df_nutritional_values_data = load_df_from_folder(OUTPUT_DIR)

    return df_products, OUTPUT_DIR, df_brand_data, df_product_name_data, df_ingredients_data, df_nutritional_values_data

def load_df_from_folder(OUTPUT_DIR):

    df_brand_data = pd.DataFrame(columns=['ID', 'Extracted_Text', 'Price', 'Processing time'])
    if os.path.exists(f'{OUTPUT_DIR}/data_extraction/brand.csv'):
        df_brand_data = pd.read_csv(f'{OUTPUT_DIR}/data_extraction/brand.csv')

    df_product_name_data = pd.DataFrame(columns=['ID', 'Extracted_Text', 'Price', 'Processing time'])
    if os.path.exists(f'{OUTPUT_DIR}/data_extraction/product_name.csv'):
        df_product_name_data = pd.read_csv(f'{OUTPUT_DIR}/data_extraction/product_name.csv')

    df_ingredients_data = pd.DataFrame(columns=['ID', 'Extracted_Text', 'Price', 'Processing time'])
    if os.path.exists(f'{OUTPUT_DIR}/data_extraction/ingredients.csv'):
        df_ingredients_data = pd.read_csv(f'{OUTPUT_DIR}/data_extraction/ingredients.csv')

    df_nutritional_values_data = pd.DataFrame(columns=['ID', 'Extracted_Text', 'Price', 'Processing time'])
    if os.path.exists(f'{OUTPUT_DIR}/data_extraction/nutritional_values.csv'):
        df_nutritional_values_data = pd.read_csv(f'{OUTPUT_DIR}/data_extraction/nutritional_values.csv')

    return df_brand_data, df_product_name_data, df_ingredients_data, df_nutritional_values_data

def load_csv_files(archive, OUTPUT_DIR):

    accepted_files = ['brand.csv', 'product_name.csv', 'ingredients.csv', 'nutritional_values.csv']

    for file in archive:
        print(os.path.basename(file))
        if os.path.basename(file) in accepted_files:
            shutil.copy(file, f'{OUTPUT_DIR}/data_extraction')

    df_brand_data, df_product_name_data, df_ingredients_data, df_nutritional_values_data = load_df_from_folder(OUTPUT_DIR)
    return df_brand_data, df_product_name_data, df_ingredients_data, df_nutritional_values_data

"""### Function to save data

This function is called when the user clicks on the "Generate data archive" button.

It creates a zip of all CSV files of the f'{OUTPUT_DIR}/data_extraction' folder, and return a download button to the archive.
"""

def generate_archive(OUTPUT_DIR):
    # Download all data

    archive_name = f'{OUTPUT_DIR}'
    shutil.make_archive(archive_name, 'zip', f'{OUTPUT_DIR}/data_extraction')

    return gr.DownloadButton(label=f"Download {archive_name}.zip", value=f'{archive_name}.zip', visible=True)

"""### Gradio UI"""

def toggle_row_visibility(show):
    if show:
        return gr.update(visible=True)
    else:
        return gr.update(visible=False)

language = 'French'

# Custom CSS to set max height for the rows
custom_css = """
.dataframe-wrap {
    max-height: 300px;  /* Set the desired height */
    overflow-y: scroll;
}
"""

OUTPUT_DIR_value = ""
dummy_data = df_brand_data = df_product_name_data = df_ingredients_data = df_nutritional_values_data = pd.DataFrame()
#dummy_data, OUTPUT_DIR_value, df_brand_data, df_product_name_data, df_ingredients_data, df_nutritional_values_data = load_roamler_excel_file("FDL-Datasets3/FR - Review.xlsm", n_rows=3)

with gr.Blocks(css=custom_css) as fdl_data_extraction_ui:

    gr.HTML("<div align='center'><h1>Euroconsumers Food Data Lake</h1>")
    gr.HTML("<div align='center'><h2>Data extraction</h2>")

    OUTPUT_DIR = gr.State(value=OUTPUT_DIR_value)

    with gr.Row():
        with gr.Column():
            gr.HTML("<h2>Upload Roamler Excel file</h2>")
            load_roamler_excel_file_input = gr.File(label="Upload Roamler Excel file", type="filepath")

    with gr.Row(visible=False) as dataset_block:
        with gr.Column():
            gr.HTML("<h2>Dataset summary</h2>")

            # Display summary of the dataset - ID, Reference_brand, Reference_product_name, mean_accuracy_score
            with gr.Row(elem_classes="dataframe-wrap"):
                dataframe_component = gr.DataFrame(value=dummy_data, interactive=False)

    with gr.Row(visible=False) as product_detail_block:
        with gr.Column():
            # Section for product details
            gr.HTML("<h1>Data extraction</h1>")

            load_csv_files_input = gr.Files(label="Upload extracted data from CSV files")

            language = gr.Dropdown(label="Select language", choices=["French", "Dutch", "Spanish", "Italian", "Portuguese"], value="French")

            gr.HTML("<h3>Brand</h3>")
            extract_brand_button = gr.Button("Extract brand")
            df_brand = gr.Dataframe(label="Brand data", scale=2,
                               column_widths=["10%", "60%", "15%", "15%"],
                                wrap=True, value=df_brand_data)

            gr.HTML("<h3>Product name</h3>")
            extract_product_name_button = gr.Button("Extract product_name")
            df_product_name = gr.Dataframe(label="Product name data", scale=2,
                               column_widths=["10%", "60%", "15%", "15%"],
                                wrap=True, value=df_product_name_data)

            gr.HTML("<h3>Ingredients</h3>")
            extract_ingredients_button = gr.Button("Extract ingredients")
            df_ingredients = gr.Dataframe(label="Ingredients data", scale=2,
                               column_widths=["10%", "60%", "15%", "15%"],
                                wrap=True, value=df_ingredients_data)

            gr.HTML("<h3>Nutritional values</h3>")
            extract_nutritional_values_button = gr.Button("Extract nutritional values")
            df_nutritional_values = gr.Dataframe(label="Nutritional data", scale=2,
                               column_widths=["10%", "60%", "15%", "15%"],
                                wrap=True, value=df_nutritional_values_data)

            # Download
            gr.HTML("<h1>Data download</h1>")

            generate_merged_file_button = gr.Button("Generate merged file")
            generate_archive_button = gr.Button("Generate data archive")
            download_button = gr.DownloadButton("Download archive", visible=False)

    ### Control functions

    # Linking the select_dataset change event to update both the gradio DataFrame and product_ids dropdown
    load_roamler_excel_file_input.change(load_roamler_excel_file,
                                         inputs=load_roamler_excel_file_input,
                                         outputs=[dataframe_component, OUTPUT_DIR,
                                                  df_brand, df_product_name, df_ingredients, df_nutritional_values])

    # Toggle visibility of the dataset block
    load_roamler_excel_file_input.change(toggle_row_visibility, inputs=load_roamler_excel_file_input, outputs=dataset_block)
    load_roamler_excel_file_input.change(toggle_row_visibility, inputs=load_roamler_excel_file_input, outputs=product_detail_block)

    load_csv_files_input.change(load_csv_files,
                                inputs=[load_csv_files_input, OUTPUT_DIR],
                                outputs=[df_brand, df_product_name, df_ingredients, df_nutritional_values])

    # Data extraction
    extract_brand_button.click(utils_data_extraction.extract_brand,
                               inputs=[OUTPUT_DIR, dataframe_component, language],
                               outputs=df_brand)

    extract_product_name_button.click(utils_data_extraction.extract_product_name,
                                      inputs=[OUTPUT_DIR, dataframe_component, language],
                                      outputs=df_product_name)

    extract_ingredients_button.click(utils_data_extraction.extract_ingredients,
                                     inputs=[OUTPUT_DIR, dataframe_component, language],
                                     outputs=df_ingredients)

    extract_nutritional_values_button.click(utils_data_extraction.extract_nutritional_values,
                                            inputs=[OUTPUT_DIR, dataframe_component, language],
                                            outputs=df_nutritional_values)

    generate_merged_file_button.click(utils_assessment.merge_and_save_data, inputs=OUTPUT_DIR)

    generate_archive_button.click(generate_archive, inputs=OUTPUT_DIR, outputs=download_button)

fdl_data_extraction_ui.launch(debug=True)