import gradio as gr
import pandas as pd

from src.process_data import *


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


languages = ["All", "Bengali", "English", "French", "German", "Italian", "Polish", "Russian", "Spanish"]
datasets = ["All", "CaFE", "CREMA-D", "EMNS", "Emozionalmente", "eNTERFACE", "JL-Corpus", "MESD", "nEMO", "Oreau", "PAVOQUE", "RAVDESS", "RESD", "SUBESCO"]
emotions = [
    "All", "anger", "anxiety", "apology", "assertiveness", 
    "calm", "concern", "disgust", "encouragement", "enthusiasm", 
    "excitement", "fear", "happiness", "neutral", "poker", 
    "sadness", "sarcasm", "surprise",
]
metric = ["f1_macro", "accuracy", "weighted_f1"]


def app():
    with gr.Blocks(theme=gr.themes.Soft(primary_hue="pink", secondary_hue="purple")) as demo:
        gr.Markdown(open("pages/header.md", "r").read(), container=True)

        with gr.Tabs():
            with gr.Tab("🏆 Leaderboard", elem_classes='tab-item'):
                with gr.Tab("Overall Results", elem_classes='tab-item'):
                    gr.Markdown(open("pages/overall-results.md", "r").read(), elem_classes='tab-item', container=True)
                    overall_table = gr.Dataframe(show_row_numbers=True, pinned_columns=2)

                with gr.Tab("Results per Language", elem_classes='tab-item'):
                    gr.Markdown(open("pages/results-per-language.md", "r").read(), elem_classes='tab-item', container=True)
                    languages_filter = gr.CheckboxGroup(choices=languages, label="Select columns", value=languages)
                    select_lang_metric = gr.Radio(metric, value='f1_macro', label="Select metric")
                    lang_table = gr.Dataframe(show_row_numbers=True, pinned_columns=2)

                with gr.Tab("Results per Dataset", elem_classes='tab-item'):
                    gr.Markdown(open("pages/results-per-dataset.md", "r").read(), elem_classes='tab-item', container=True)
                    dataset_filter = gr.CheckboxGroup(choices=datasets, label="Select columns", value=datasets)
                    select_ds_metric = gr.Radio(metric, value='f1_macro', label="Select metric")
                    dataset_table = gr.Dataframe(show_row_numbers=True, pinned_columns=2)

                with gr.Tab("Results per Emotion", elem_classes='tab-item'):
                    gr.Markdown(open("pages/results-per-emotion.md", "r").read(), elem_classes='tab-item', container=True)
                    emo_filter = gr.CheckboxGroup(choices=emotions, label="Select columns", value=emotions)
                    emotion_table = gr.Dataframe(show_row_numbers=True, pinned_columns=2)

                df_state = gr.State()

                def update_leaderboards(languages=[], datasets=[], emotions=[], select_lang_metric="f1_macro", select_ds_metric="f1_macro"):
                    df = pd.read_json("results.jsonl", lines=True)
                    lang_dict = build_lang_dict(df)
                    ds_dict = build_ds_dict(df)
                    emo_dict = build_emo_dict(df)
                    overall = overall_leaderboard(df)
                    by_lang = leaderboard_per_group(lang_dict, languages, metric=select_lang_metric)
                    by_dataset = leaderboard_per_group(ds_dict, datasets, metric=select_ds_metric)
                    by_emotion = leaderboard_per_group(emo_dict, emotions)
                    return overall, by_lang, by_dataset, by_emotion, "Loaded successfully."

                demo.load(
                    update_leaderboards,
                    inputs=[languages_filter, dataset_filter, emo_filter],
                    outputs=[overall_table, lang_table, dataset_table, emotion_table, df_state]
                )

                def on_change(selected_languages, selected_lang_metric, selected_datasets, selected_ds_metric, selected_emotions):
                    return update_leaderboards(languages=selected_languages, select_lang_metric=selected_lang_metric, datasets=selected_datasets, select_ds_metric=selected_ds_metric, emotions=selected_emotions)

                languages_filter.change(on_change, [languages_filter, select_lang_metric, dataset_filter, select_ds_metric, emo_filter],
                                       [overall_table, lang_table, dataset_table, emotion_table])

                select_lang_metric.change(on_change, [languages_filter, select_lang_metric, dataset_filter, select_ds_metric, emo_filter],
                                       [overall_table, lang_table, dataset_table, emotion_table])

                dataset_filter.change(on_change, [languages_filter, select_lang_metric, dataset_filter, select_ds_metric, emo_filter],
                                       [overall_table, lang_table, dataset_table, emotion_table])

                select_ds_metric.change(on_change, [languages_filter, select_lang_metric, dataset_filter, select_ds_metric, emo_filter],
                                          [overall_table, lang_table, dataset_table, emotion_table])

                emo_filter.change(on_change, [languages_filter, select_lang_metric, dataset_filter, select_ds_metric, emo_filter],
                                       [overall_table, lang_table, dataset_table, emotion_table])
            
            with gr.Tab("📝 About", elem_classes='tab-item'):
                gr.Markdown(open("pages/about.md", "r").read(), elem_classes='tab-item')

            with gr.Tab("🔢 Evaluate your model", elem_classes='tab-item'):
                gr.Markdown(open("pages/evaluate.md", "r").read(), elem_classes='tab-item')
            
            with gr.Tab("📬 Submit here!", elem_classes='tab-item'):
                gr.Markdown(open("pages/submit.md", "r").read(), elem_classes='tab-item')
        
        with gr.Column():
            with gr.Accordion("📙 Citation", open=False, elem_classes='tab-item'):
                citation_button = gr.Textbox(
                    label="",
                    value=CITATION_TEXT,
                    lines=20,
                    elem_id="citation-button",
                    show_copy_button=True,
                )
    
    return demo

if __name__ == "__main__":
    demo = app()
    demo.launch()