"""
File: calculate_practical_tasks.py
Author: Elena Ryumina and Dmitry Ryumin
Description: Event handler for Gradio app to calculate practical tasks.
License: MIT License
"""

from app.oceanai_init import b5
import gradio as gr
import pandas as pd
from pathlib import Path

# Importing necessary components for the Gradio app
from app.config import config_data
from app.components import html_message, dataframe, files_create_ui, video_create_ui


def event_handler_calculate_practical_task_blocks(
    files,
    practical_subtasks,
    pt_scores,
    threshold_professional_skills,
    dropdown_professional_skills,
):
    if practical_subtasks.lower() == "professional skills":
        df_professional_skills = pd.read_csv(config_data.Links_PROFESSIONAL_SKILLS)

        df_professional_skills.index.name = "ID"
        df_professional_skills.index += 1
        df_professional_skills.index = df_professional_skills.index.map(str)

        b5._priority_skill_calculation(
            df_files=pt_scores.iloc[:, 1:],
            correlation_coefficients=df_professional_skills,
            threshold=threshold_professional_skills,
            out=True,
        )

        # Optional
        df = b5.df_files_priority_skill_.rename(
            columns={
                "Openness": "OPE",
                "Conscientiousness": "CON",
                "Extraversion": "EXT",
                "Agreeableness": "AGR",
                "Non-Neuroticism": "NNEU",
            }
        )
        columns_to_round = df.columns[1:]
        df[columns_to_round] = df[columns_to_round].apply(
            lambda x: [round(i, 3) for i in x]
        )

        professional_skills_list = (
            config_data.Settings_DROPDOWN_PROFESSIONAL_SKILLS.copy()
        )

        professional_skills_list.remove(dropdown_professional_skills)

        professional_skills_list = [
            "OPE",
            "CON",
            "EXT",
            "AGR",
            "NNEU",
        ] + professional_skills_list

        df_hidden = df.drop(columns=professional_skills_list)

        df_hidden.to_csv(config_data.Filenames_PT_SKILLS_SCORES)

        df_hidden.reset_index(inplace=True)

        df_hidden = df_hidden.sort_values(
            by=[dropdown_professional_skills], ascending=False
        )

        person_id = int(df_hidden.iloc[0]["Person ID"]) - 1

        return (
            gr.Row(visible=True),
            gr.Column(visible=True),
            dataframe(
                headers=df_hidden.columns.tolist(),
                values=df_hidden.values.tolist(),
                visible=True,
            ),
            files_create_ui(
                config_data.Filenames_PT_SKILLS_SCORES,
                "single",
                [".csv"],
                config_data.OtherMessages_EXPORT_PS,
                True,
                False,
                True,
                "csv-container",
            ),
            video_create_ui(
                value=files[person_id],
                file_name=Path(files[person_id]).name,
                label="Best Person ID - " + str(person_id + 1),
                visible=True,
            ),
            html_message(config_data.InformationMessages_NOTI_IN_DEV, False, False),
        )
    else:
        gr.Info(config_data.InformationMessages_NOTI_IN_DEV)

        return (
            gr.Row(visible=False),
            gr.Column(visible=False),
            dataframe(visible=False),
            files_create_ui(
                None,
                "single",
                [".csv"],
                config_data.OtherMessages_EXPORT_PS,
                True,
                False,
                False,
                "csv-container",
            ),
            video_create_ui(visible=False),
            html_message(config_data.InformationMessages_NOTI_IN_DEV, False, True),
        )