import gradio as gr
from apscheduler.schedulers.background import BackgroundScheduler

from src.css_html_js import custom_css
from src.envs import API, REPO_ID
from src.process_leaderboard_data import Leaderboard


CITATION_TEXT = open(f"pages/citation.bib", "r").read()


def restart_space():
    API.restart_space(repo_id=REPO_ID)


app = gr.Blocks(css=custom_css)


with app:
    # Init class Leaderboard
    leaderboard = Leaderboard()

    # Custom CSS styling for tab-item components
    app.css = """
    .tab-item {
        font-size: 10px;
        padding: 10px 20px;
    }
    """

    # Title and Description of the Leaderboard
    gr.HTML(open("pages/description.html", "r").read())

    # LEADERBOARD
    with gr.Tabs(elem_classes="tab-buttons") as tabs:
        with gr.TabItem("🏅 Leaderboard", elem_id="Leaderboard", id=0, elem_classes="tab-item"):

            # OVERALL
            with gr.TabItem("Overall", elem_id="Overall", id=1, elem_classes="tab-item"):
                gr.Markdown(open("pages/overall.md", "r").read())

                # Create and display leaderboard table
                leaderboard_dataframe = leaderboard.create_leaderboard_data('All', 'wavlm', 'emotion')
                leaderboard_table = gr.DataFrame(leaderboard_dataframe,
                                                 datatype= ["markdown" if col == "Model" else "str" for col in leaderboard_dataframe.columns],
                                                 interactive=False,
                )

            # EMOTIONS
            with gr.TabItem("Emotions", elem_id="Emotions", id=2, elem_classes="tab-item"):
                gr.Markdown(open("pages/emotions.md", "r").read())

                # UI for selecting dataset and emotion options
                with gr.Row():
                    with gr.Column():
                        dataset = gr.Radio(
                                choices = leaderboard.get_emotional_dataset_list(),
                                value = None,
                                label = "Dataset",
                            )
                    with gr.Column(min_width=750):
                        emotion = gr.Radio(
                                choices = leaderboard.get_emotion_list(),
                                value = "Anger",
                                label = "Emotion",
                            )
                        
                # Create and display leaderboard table
                leaderboard_dataframe = leaderboard.create_leaderboard_data(emotion.value, 'wavlm', 'emotion')
                leaderboard_table = gr.DataFrame(leaderboard_dataframe,
                                                 datatype= ["markdown" if col == "Model" else "str" for col in leaderboard_dataframe.columns],
                                                 interactive=False,
                )

                # Update leaderboard table based on user selection changes in emotion or dataset options.
                dataset.change(leaderboard.update_leaderboard_data_in_emotion_section,
                    [dataset, gr.State('dataset'), leaderboard_table],
                    [emotion, leaderboard_table]
                )
                emotion.change(leaderboard.update_leaderboard_data_in_emotion_section,
                    [emotion, gr.State('emotion'), leaderboard_table],
                    [dataset, leaderboard_table]
                )


            # FEATURES
            with gr.TabItem("Features", elem_id="Features", id=3, elem_classes="tab-item"):
                gr.Markdown(open("pages/features.md", "r").read())

                # UI for selecting dataset, emotion, and feature options
                with gr.Row():
                    with gr.Column():
                        dataset = gr.Radio(
                                choices = leaderboard.get_emotional_dataset_list(),
                                value = None,
                                elem_id = "dataset_features",
                                label = "Dataset",
                            )
                    with gr.Column(min_width=750):
                        emotion = gr.Radio(
                                choices = leaderboard.get_emotion_list(),
                                value = "All",
                                elem_id = "emotion_features",
                                label = "Emotion",
                            )
                with gr.Row():
                        hidden_columns = ['model', 'dataset', 'emotion', 'wavlm', "Unnamed: 0"]
                        feature = gr.Radio(
                            choices = [kol for kol in list(leaderboard.get_models_performance_dataframe_column()) if kol not in hidden_columns],
                            value = "pitch",
                            label = "Feature",
                        )

                # Create and display leaderboard table
                leaderboard_dataframe = leaderboard.create_leaderboard_data(emotion.value, feature.value, 'emotion')
                leaderboard_table = gr.DataFrame(leaderboard_dataframe,
                                                 datatype= ["markdown" if col == "Model" else "str" for col in leaderboard_dataframe.columns],
                                                 interactive=False,
                )

                # Update leaderboard table based on user selection changes in emotion, dataset, or feature options.
                emotion.change(leaderboard.update_leaderboard_data_in_feature_section,
                    [emotion, feature, gr.State('emotion'), leaderboard_table],
                    [dataset, leaderboard_table]
                )
                dataset.change(leaderboard.update_leaderboard_data_in_feature_section,
                    [dataset, feature, gr.State('dataset'), leaderboard_table],
                    [emotion, leaderboard_table]
                )
                feature.change(leaderboard.update_leaderboard_data_by_feature,
                    [emotion, dataset, feature],
                    [leaderboard_table]
                )

        # ABOUT
        with gr.TabItem("📝 About", elem_id="About", id=4):
            gr.Markdown(open("pages/about.md", "r").read())

        # SUBMIT HERE
        with gr.TabItem("🚀 Submit here! ", elem_id="Submit", id=5):
            gr.Markdown(open("pages/submit.md", "r").read())

    with gr.Column():
        with gr.Accordion("📙 Citation", open=False):
            citation_button = gr.Textbox(
                label="",
                value=CITATION_TEXT,
                lines=66,
                elem_id="citation-button",
                show_copy_button=True,
            )


scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", seconds=1800)
scheduler.start()
app.queue(default_concurrency_limit=40).launch()