import time

import pandas as pd

from app.backend.constant import Navigation, ModelProvider, EvaluationMetric, EmbdDtype, EmbdDim, Similarity
from app.backend.data_engine import DataEngine
from app.ui.component.filter_component import FilterComponent
from app.ui.component.subtabs_component import SubtabsComponent
from app.ui.static import HOME_CSS

import gradio as gr

NUM_DATASETS = 1
NUM_SCORES = 2
NUM_MODELS = 3
HANDLING = False


def init_home():
    """
    Initialize the home page
    """
    data_engine = DataEngine()

    with gr.Blocks(css=HOME_CSS) as block:
        gr.Markdown(f"""
            [Voyageai] Massive Text Embedding Benchmark (MTEB) Leaderboard.
            """)

        filter_area = FilterComponent(
            data_engine,
            [element.value for element in Navigation],
            [element.value for element in ModelProvider],
            [element.value for element in EvaluationMetric],
            [element.value for element in EmbdDtype],
            [element.value for element in EmbdDim],
            [element.value for element in Similarity],
        )

        navigations, model_provides, evaluation_metrics, embd_dtypes, embd_dims, similarities, max_tokens = filter_area.show()

        sub_tabs = SubtabsComponent(data_engine)
        columns = sub_tabs.show()

        # df_area = DataFrameComponent(data_engine)
        # df_display = df_area.show(pd.DataFrame(columns=[element.value for element in Navigation]))

        block.load(sub_tabs.show,
                   inputs=[navigations, model_provides, evaluation_metrics, embd_dtypes, embd_dims, similarities,
                           max_tokens], outputs=columns)

        navigations.change(trigger_mode="once", fn=sub_tabs.show,
                           inputs=[navigations, model_provides, evaluation_metrics, embd_dtypes, embd_dims,
                                   similarities,
                                   max_tokens], outputs=columns)
        model_provides.change(trigger_mode="once", fn=sub_tabs.show,
                              inputs=[navigations, model_provides, evaluation_metrics, embd_dtypes, embd_dims,
                                      similarities,
                                      max_tokens], outputs=columns)
        evaluation_metrics.change(trigger_mode="once", fn=sub_tabs.show,
                                  inputs=[navigations, model_provides, evaluation_metrics, embd_dtypes, embd_dims,
                                          similarities,
                                          max_tokens], outputs=columns)
        embd_dtypes.change(trigger_mode="once", fn=sub_tabs.show,
                           inputs=[navigations, model_provides, evaluation_metrics, embd_dtypes, embd_dims,
                                   similarities,
                                   max_tokens], outputs=columns)
        embd_dims.change(trigger_mode="once", fn=sub_tabs.show,
                         inputs=[navigations, model_provides, evaluation_metrics, embd_dtypes, embd_dims, similarities,
                                 max_tokens], outputs=columns)

        similarities.change(trigger_mode="once", fn=sub_tabs.show,
                            inputs=[navigations, model_provides, evaluation_metrics, embd_dtypes, embd_dims,
                                    similarities,
                                    max_tokens], outputs=columns)
        max_tokens.change(trigger_mode="always_last", fn=sub_tabs.show,
                          inputs=[navigations, model_provides, evaluation_metrics, embd_dtypes, embd_dims, similarities,
                                  max_tokens], outputs=columns)

    block.queue(max_size=1)
    return block