"""
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 re
import gradio as gr
from pathlib import Path

# Importing necessary components for the Gradio app
from app.config import config_data
from app.video_metadata import video_metadata
from app.mbti_description import MBTI_DESCRIPTION, MBTI_DATA
from app.utils import (
    read_csv_file,
    apply_rounding_and_rename_columns,
    preprocess_scores_df,
)
from app.components import (
    html_message,
    dataframe,
    files_create_ui,
    video_create_ui,
    textbox_create_ui,
)


def colleague_type(subtask):
    return "minor" if "junior" in subtask.lower() else "major"


def consumer_preferences(subtask):
    return (
        config_data.Filenames_CAR_CHARACTERISTICS
        if "mobile device" in subtask.lower()
        else config_data.Filenames_MDA_CATEGORIES
    )


def remove_parentheses(s):
    return re.sub(r"\s*\([^)]*\)", "", s)


def extract_text_in_parentheses(s):
    result = re.search(r"\(([^)]+)\)", s)
    if result:
        return result.group(1)
    else:
        return None


def compare_strings(original, comparison, prev=False):
    result = []
    prev_class = None

    for orig_char, comp_char in zip(original, comparison):
        curr_class = "true" if orig_char == comp_char else "err"
        if not prev:
            result.append(f"<span class='{curr_class}'>{comp_char}</span>")
        else:
            if curr_class != prev_class:
                result.append("</span>" if prev_class else "")
                result.append(f"<span class='{curr_class}'>")
                prev_class = curr_class
            result.append(comp_char)

    return f"<span class='wrapper_mbti'>{''.join(result + [f'</span>' if prev_class else ''])}</span>"


def create_person_metadata(person_id, files, video_metadata):
    if (
        Path(files[person_id]).name in video_metadata
        and config_data.Settings_SHOW_VIDEO_METADATA
    ):
        person_metadata_list = video_metadata[Path(files[person_id]).name]
        return (
            gr.Column(visible=True),
            gr.Row(visible=True),
            gr.Row(visible=True),
            gr.Image(visible=True),
            textbox_create_ui(
                person_metadata_list[0],
                "text",
                "First name",
                None,
                None,
                1,
                True,
                False,
                True,
                False,
                1,
                False,
            ),
            gr.Row(visible=True),
            gr.Image(visible=True),
            textbox_create_ui(
                person_metadata_list[1],
                "text",
                "Last name",
                None,
                None,
                1,
                True,
                False,
                True,
                False,
                1,
                False,
            ),
            gr.Row(visible=True),
            gr.Row(visible=True),
            gr.Image(visible=True),
            textbox_create_ui(
                person_metadata_list[2],
                "email",
                "Email",
                None,
                None,
                1,
                True,
                False,
                True,
                False,
                1,
                False,
            ),
            gr.Row(visible=True),
            gr.Image(visible=True),
            textbox_create_ui(
                person_metadata_list[3],
                "text",
                "Phone number",
                None,
                None,
                1,
                True,
                False,
                True,
                False,
                1,
                False,
            ),
        )
    else:
        return (
            gr.Column(visible=False),
            gr.Row(visible=False),
            gr.Row(visible=False),
            gr.Image(visible=False),
            textbox_create_ui(visible=False),
            gr.Row(visible=False),
            gr.Image(visible=False),
            textbox_create_ui(visible=False),
            gr.Row(visible=False),
            gr.Row(visible=False),
            gr.Image(visible=False),
            textbox_create_ui(visible=False),
            gr.Row(visible=False),
            gr.Image(visible=False),
            textbox_create_ui(visible=False),
        )


def event_handler_calculate_practical_task_blocks(
    files,
    practical_subtasks,
    pt_scores,
    dropdown_mbti,
    threshold_mbti,
    threshold_professional_skills,
    dropdown_professional_skills,
    target_score_ope,
    target_score_con,
    target_score_ext,
    target_score_agr,
    target_score_nneu,
    equal_coefficient,
    number_priority,
    number_importance_traits,
    threshold_consumer_preferences,
    number_openness,
    number_conscientiousness,
    number_extraversion,
    number_agreeableness,
    number_non_neuroticism,
):
    if practical_subtasks.lower() == "16 personality types of mbti":
        df_correlation_coefficients = read_csv_file(config_data.Links_MBTI)

        pt_scores_copy = pt_scores.iloc[:, 1:].copy()

        preprocess_scores_df(pt_scores_copy, config_data.Dataframes_PT_SCORES[0][0])

        b5._professional_match(
            df_files=pt_scores_copy,
            correlation_coefficients=df_correlation_coefficients,
            personality_type=remove_parentheses(dropdown_mbti),
            threshold=threshold_mbti,
            out=False,
        )

        df = apply_rounding_and_rename_columns(b5.df_files_MBTI_job_match_)

        df_hidden = df.drop(
            columns=config_data.Settings_SHORT_PROFESSIONAL_SKILLS
            + config_data.Settings_DROPDOWN_MBTI_DEL_COLS
        )

        df_hidden.rename(
            columns={
                "Path": "Filename",
                "MBTI": "Personality Type",
                "MBTI_Score": "Personality Type Score",
            },
            inplace=True,
        )

        df_hidden.to_csv(config_data.Filenames_MBTI_JOB)

        df_hidden.reset_index(inplace=True)

        person_id = int(df_hidden.iloc[0][config_data.Dataframes_PT_SCORES[0][0]]) - 1

        short_mbti = extract_text_in_parentheses(dropdown_mbti)
        mbti_values = df_hidden["Personality Type"].tolist()

        df_hidden["Personality Type"] = [
            compare_strings(short_mbti, mbti, False) for mbti in mbti_values
        ]

        person_metadata = create_person_metadata(person_id, files, video_metadata)

        existing_tuple = (
            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_MBTI_JOB,
                "single",
                [".csv"],
                config_data.OtherMessages_EXPORT_MBTI,
                True,
                False,
                True,
                "csv-container",
            ),
            gr.Accordion(
                label=config_data.Labels_NOTE_MBTI_LABEL,
                open=False,
                visible=True,
            ),
            gr.HTML(value=MBTI_DESCRIPTION, visible=True),
            dataframe(
                headers=MBTI_DATA.columns.tolist(),
                values=MBTI_DATA.values.tolist(),
                visible=True,
                elem_classes="mbti-dataframe",
            ),
            gr.Column(visible=True),
            video_create_ui(
                value=files[person_id],
                file_name=Path(files[person_id]).name,
                label="Best Person ID - " + str(person_id + 1),
                visible=True,
                elem_classes="video-sorted-container",
            ),
            html_message(config_data.InformationMessages_NOTI_IN_DEV, False, False),
        )

        return existing_tuple[:-1] + person_metadata + existing_tuple[-1:]
    elif practical_subtasks.lower() == "professional groups":
        sum_weights = sum(
            [
                number_openness,
                number_conscientiousness,
                number_extraversion,
                number_agreeableness,
                number_non_neuroticism,
            ]
        )

        if sum_weights != 100:
            gr.Warning(config_data.InformationMessages_SUM_WEIGHTS.format(sum_weights))

            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",
                ),
                gr.Accordion(visible=False),
                gr.HTML(visible=False),
                dataframe(visible=False),
                gr.Column(visible=False),
                video_create_ui(visible=False),
                gr.Column(visible=False),
                gr.Row(visible=False),
                gr.Row(visible=False),
                gr.Image(visible=False),
                textbox_create_ui(visible=False),
                gr.Row(visible=False),
                gr.Image(visible=False),
                textbox_create_ui(visible=False),
                gr.Row(visible=False),
                gr.Row(visible=False),
                gr.Image(visible=False),
                textbox_create_ui(visible=False),
                gr.Row(visible=False),
                gr.Image(visible=False),
                textbox_create_ui(visible=False),
                html_message(
                    config_data.InformationMessages_SUM_WEIGHTS.format(sum_weights),
                    False,
                    True,
                ),
            )
        else:
            b5._candidate_ranking(
                df_files=pt_scores.iloc[:, 1:],
                weigths_openness=number_openness,
                weigths_conscientiousness=number_conscientiousness,
                weigths_extraversion=number_extraversion,
                weigths_agreeableness=number_agreeableness,
                weigths_non_neuroticism=number_non_neuroticism,
                out=False,
            )

            df = apply_rounding_and_rename_columns(b5.df_files_ranking_)

            df_hidden = df.drop(columns=config_data.Settings_SHORT_PROFESSIONAL_SKILLS)

            df_hidden.to_csv(config_data.Filenames_POTENTIAL_CANDIDATES)

            df_hidden.reset_index(inplace=True)

            person_id = (
                int(df_hidden.iloc[0][config_data.Dataframes_PT_SCORES[0][0]]) - 1
            )

            person_metadata = create_person_metadata(person_id, files, video_metadata)

            existing_tuple = (
                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_POTENTIAL_CANDIDATES,
                    "single",
                    [".csv"],
                    config_data.OtherMessages_EXPORT_PG,
                    True,
                    False,
                    True,
                    "csv-container",
                ),
                gr.Accordion(visible=False),
                gr.HTML(visible=False),
                dataframe(visible=False),
                gr.Column(visible=True),
                video_create_ui(
                    value=files[person_id],
                    file_name=Path(files[person_id]).name,
                    label="Best Person ID - " + str(person_id + 1),
                    visible=True,
                    elem_classes="video-sorted-container",
                ),
                html_message(config_data.InformationMessages_NOTI_IN_DEV, False, False),
            )

            return existing_tuple[:-1] + person_metadata + existing_tuple[-1:]
    elif practical_subtasks.lower() == "professional skills":
        df_professional_skills = read_csv_file(config_data.Links_PROFESSIONAL_SKILLS)

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

        df = apply_rounding_and_rename_columns(b5.df_files_priority_skill_)

        professional_skills_list = (
            config_data.Settings_DROPDOWN_PROFESSIONAL_SKILLS.copy()
        )

        professional_skills_list.remove(dropdown_professional_skills)

        df_hidden = df.drop(
            columns=config_data.Settings_SHORT_PROFESSIONAL_SKILLS
            + 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][config_data.Dataframes_PT_SCORES[0][0]]) - 1

        person_metadata = create_person_metadata(person_id, files, video_metadata)

        existing_tuple = (
            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",
            ),
            gr.Accordion(visible=False),
            gr.HTML(visible=False),
            dataframe(visible=False),
            gr.Column(visible=True),
            video_create_ui(
                value=files[person_id],
                file_name=Path(files[person_id]).name,
                label="Best Person ID - " + str(person_id + 1),
                visible=True,
                elem_classes="video-sorted-container",
            ),
            html_message(config_data.InformationMessages_NOTI_IN_DEV, False, False),
        )

        return existing_tuple[:-1] + person_metadata + existing_tuple[-1:]
    elif (
        practical_subtasks.lower() == "finding a suitable junior colleague"
        or practical_subtasks.lower() == "finding a suitable senior colleague"
    ):
        df_correlation_coefficients = read_csv_file(
            config_data.Links_FINDING_COLLEAGUE, ["ID"]
        )

        b5._colleague_ranking(
            df_files=pt_scores.iloc[:, 1:],
            correlation_coefficients=df_correlation_coefficients,
            target_scores=[
                target_score_ope,
                target_score_con,
                target_score_ext,
                target_score_agr,
                target_score_nneu,
            ],
            colleague=colleague_type(practical_subtasks),
            equal_coefficients=equal_coefficient,
            out=False,
        )

        df = apply_rounding_and_rename_columns(b5.df_files_colleague_)

        df_hidden = df.drop(columns=config_data.Settings_SHORT_PROFESSIONAL_SKILLS)

        df_hidden.to_csv(
            colleague_type(practical_subtasks) + config_data.Filenames_COLLEAGUE_RANKING
        )

        df_hidden.reset_index(inplace=True)

        person_id = int(df_hidden.iloc[0][config_data.Dataframes_PT_SCORES[0][0]]) - 1

        person_metadata = create_person_metadata(person_id, files, video_metadata)

        existing_tuple = (
            gr.Row(visible=True),
            gr.Column(visible=True),
            dataframe(
                headers=df_hidden.columns.tolist(),
                values=df_hidden.values.tolist(),
                visible=True,
            ),
            files_create_ui(
                colleague_type(practical_subtasks)
                + config_data.Filenames_COLLEAGUE_RANKING,
                "single",
                [".csv"],
                config_data.OtherMessages_EXPORT_WT,
                True,
                False,
                True,
                "csv-container",
            ),
            gr.Accordion(visible=False),
            gr.HTML(visible=False),
            dataframe(visible=False),
            gr.Column(visible=True),
            video_create_ui(
                value=files[person_id],
                file_name=Path(files[person_id]).name,
                label="Best Person ID - " + str(person_id + 1),
                visible=True,
                elem_classes="video-sorted-container",
            ),
            html_message(config_data.InformationMessages_NOTI_IN_DEV, False, False),
        )

        return existing_tuple[:-1] + person_metadata + existing_tuple[-1:]
    elif (
        practical_subtasks.lower() == "car characteristics"
        or practical_subtasks.lower() == "mobile device application categories"
        or practical_subtasks.lower() == "clothing style correlation"
    ):
        if practical_subtasks.lower() == "car characteristics":
            df_correlation_coefficients = read_csv_file(
                config_data.Links_CAR_CHARACTERISTICS,
                ["Style and performance", "Safety and practicality"],
            )
        elif practical_subtasks.lower() == "mobile device application categories":
            df_correlation_coefficients = read_csv_file(
                config_data.Links_MDA_CATEGORIES
            )
        elif practical_subtasks.lower() == "clothing style correlation":
            df_correlation_coefficients = read_csv_file(config_data.Links_CLOTHING_SC)

        pt_scores_copy = pt_scores.iloc[:, 1:].copy()

        preprocess_scores_df(pt_scores_copy, config_data.Dataframes_PT_SCORES[0][0])

        b5._priority_calculation(
            df_files=pt_scores_copy,
            correlation_coefficients=df_correlation_coefficients,
            col_name_ocean="Trait",
            threshold=threshold_consumer_preferences,
            number_priority=number_priority,
            number_importance_traits=number_importance_traits,
            out=False,
        )

        df_files_priority = b5.df_files_priority_.copy()
        df_files_priority.reset_index(inplace=True)

        df = apply_rounding_and_rename_columns(df_files_priority.iloc[:, 1:])

        preprocess_scores_df(df, config_data.Dataframes_PT_SCORES[0][0])

        df_hidden = df.drop(columns=config_data.Settings_SHORT_PROFESSIONAL_SKILLS)

        df_hidden.to_csv(consumer_preferences(practical_subtasks))

        df_hidden.reset_index(inplace=True)

        person_id = int(df_hidden.iloc[0][config_data.Dataframes_PT_SCORES[0][0]]) - 1

        person_metadata = create_person_metadata(person_id, files, video_metadata)

        existing_tuple = (
            gr.Row(visible=True),
            gr.Column(visible=True),
            dataframe(
                headers=df_hidden.columns.tolist(),
                values=df_hidden.values.tolist(),
                visible=True,
            ),
            files_create_ui(
                consumer_preferences(practical_subtasks),
                "single",
                [".csv"],
                config_data.OtherMessages_EXPORT_CP,
                True,
                False,
                True,
                "csv-container",
            ),
            gr.Accordion(visible=False),
            gr.HTML(visible=False),
            dataframe(visible=False),
            gr.Column(visible=True),
            video_create_ui(
                value=files[person_id],
                file_name=Path(files[person_id]).name,
                label="Best Person ID - " + str(person_id + 1),
                visible=True,
                elem_classes="video-sorted-container",
            ),
            html_message(config_data.InformationMessages_NOTI_IN_DEV, False, False),
        )

        return existing_tuple[:-1] + person_metadata + existing_tuple[-1:]
    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",
            ),
            gr.Accordion(visible=False),
            gr.HTML(visible=False),
            dataframe(visible=False),
            gr.Column(visible=False),
            video_create_ui(visible=False),
            gr.Column(visible=False),
            gr.Row(visible=False),
            gr.Row(visible=False),
            gr.Image(visible=False),
            textbox_create_ui(visible=False),
            gr.Row(visible=False),
            gr.Image(visible=False),
            textbox_create_ui(visible=False),
            gr.Row(visible=False),
            gr.Row(visible=False),
            gr.Image(visible=False),
            textbox_create_ui(visible=False),
            gr.Row(visible=False),
            gr.Image(visible=False),
            textbox_create_ui(visible=False),
            html_message(config_data.InformationMessages_NOTI_IN_DEV, False, True),
        )