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| """ | |
| 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 pandas as pd | |
| import re | |
| import gradio as gr | |
| from pathlib import Path | |
| from bs4 import BeautifulSoup | |
| # 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.data_init import df_traits_priority_for_professions | |
| from app.utils import ( | |
| read_csv_file, | |
| apply_rounding_and_rename_columns, | |
| preprocess_scores_df, | |
| get_language_settings, | |
| extract_profession_weights, | |
| ) | |
| 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( | |
| language, | |
| type_modes, | |
| files, | |
| video, | |
| 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, | |
| ): | |
| lang_id, _ = get_language_settings(language) | |
| if type_modes == config_data.Settings_TYPE_MODES[1]: | |
| files = [video] | |
| 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]) | |
| if type_modes == config_data.Settings_TYPE_MODES[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_copy = df_hidden.copy() | |
| df_copy["Personality Type"] = df_copy["Personality Type"].apply( | |
| lambda x: "".join(BeautifulSoup(x, "html.parser").stripped_strings) | |
| ) | |
| df_copy.to_csv(config_data.Filenames_MBTI_JOB, index=False) | |
| 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) | |
| elif type_modes == config_data.Settings_TYPE_MODES[1]: | |
| all_hidden_dfs = [] | |
| for dropdown_mbti in config_data.Settings_DROPDOWN_MBTI: | |
| 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 | |
| + config_data.Settings_DROPDOWN_MBTI_DEL_COLS_WEBCAM | |
| ) | |
| df_hidden.insert(0, "Popular Occupations", dropdown_mbti) | |
| df_hidden.rename( | |
| columns={ | |
| "MBTI": "Personality Type", | |
| "MBTI_Score": "Personality Type Score", | |
| }, | |
| inplace=True, | |
| ) | |
| 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 | |
| ] | |
| all_hidden_dfs.append(df_hidden) | |
| df_hidden = pd.concat(all_hidden_dfs, ignore_index=True) | |
| df_hidden = df_hidden.sort_values( | |
| by="Personality Type Score", ascending=False | |
| ) | |
| df_hidden.reset_index(drop=True, inplace=True) | |
| df_copy = df_hidden.copy() | |
| df_copy["Personality Type"] = df_copy["Personality Type"].apply( | |
| lambda x: "".join(BeautifulSoup(x, "html.parser").stripped_strings) | |
| ) | |
| df_copy.to_csv(config_data.Filenames_MBTI_JOB, index=False) | |
| person_id = 0 | |
| 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": | |
| if type_modes == config_data.Settings_TYPE_MODES[0]: | |
| 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 | |
| ) | |
| elif type_modes == config_data.Settings_TYPE_MODES[1]: | |
| all_hidden_dfs = [] | |
| for dropdown_candidate in config_data.Settings_DROPDOWN_CANDIDATES[:-1]: | |
| weights, _ = extract_profession_weights( | |
| df_traits_priority_for_professions, | |
| dropdown_candidate, | |
| ) | |
| sum_weights = sum(weights) | |
| 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=weights[0], | |
| weigths_conscientiousness=weights[1], | |
| weigths_extraversion=weights[2], | |
| weigths_agreeableness=weights[3], | |
| weigths_non_neuroticism=weights[4], | |
| out=False, | |
| ) | |
| df = apply_rounding_and_rename_columns(b5.df_files_ranking_) | |
| df_hidden = df.drop( | |
| columns=config_data.Settings_SHORT_PROFESSIONAL_SKILLS | |
| + config_data.Settings_DROPDOWN_MBTI_DEL_COLS_WEBCAM | |
| ) | |
| df_hidden.insert(0, "Professional Group", dropdown_candidate) | |
| all_hidden_dfs.append(df_hidden) | |
| df_hidden = pd.concat(all_hidden_dfs, ignore_index=True) | |
| df_hidden.rename( | |
| columns={ | |
| "Candidate score": "Summary Score", | |
| }, | |
| inplace=True, | |
| ) | |
| df_hidden = df_hidden.sort_values(by="Summary Score", ascending=False) | |
| df_hidden.reset_index(drop=True, inplace=True) | |
| df_hidden.to_csv( | |
| config_data.Filenames_POTENTIAL_CANDIDATES, index=False | |
| ) | |
| person_id = 0 | |
| 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) | |
| pt_scores_copy = pt_scores.iloc[:, 1:].copy() | |
| preprocess_scores_df(pt_scores_copy, config_data.Dataframes_PT_SCORES[0][0]) | |
| b5._priority_skill_calculation( | |
| df_files=pt_scores_copy, | |
| correlation_coefficients=df_professional_skills, | |
| threshold=threshold_professional_skills, | |
| out=False, | |
| ) | |
| df = apply_rounding_and_rename_columns(b5.df_files_priority_skill_) | |
| if type_modes == config_data.Settings_TYPE_MODES[0]: | |
| professional_skills_list = ( | |
| config_data.Settings_DROPDOWN_PROFESSIONAL_SKILLS.copy() | |
| ) | |
| professional_skills_list.remove(dropdown_professional_skills) | |
| del_cols = [] | |
| elif type_modes == config_data.Settings_TYPE_MODES[1]: | |
| professional_skills_list = [] | |
| del_cols = config_data.Settings_DROPDOWN_MBTI_DEL_COLS_WEBCAM | |
| df_hidden = df.drop( | |
| columns=config_data.Settings_SHORT_PROFESSIONAL_SKILLS | |
| + professional_skills_list | |
| + del_cols | |
| ) | |
| if type_modes == config_data.Settings_TYPE_MODES[0]: | |
| df_hidden = df_hidden.sort_values( | |
| by=[dropdown_professional_skills], ascending=False | |
| ) | |
| df_hidden.reset_index(inplace=True) | |
| elif type_modes == config_data.Settings_TYPE_MODES[1]: | |
| df_hidden = df_hidden.melt( | |
| var_name="Professional Skill", value_name="Summary Score" | |
| ) | |
| df_hidden = df_hidden.sort_values(by=["Summary Score"], ascending=False) | |
| df_hidden.reset_index(drop=True, inplace=True) | |
| df_hidden.to_csv(config_data.Filenames_PT_SKILLS_SCORES) | |
| if type_modes == config_data.Settings_TYPE_MODES[0]: | |
| person_id = ( | |
| int(df_hidden.iloc[0][config_data.Dataframes_PT_SCORES[0][0]]) - 1 | |
| ) | |
| elif type_modes == config_data.Settings_TYPE_MODES[1]: | |
| person_id = 0 | |
| 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" | |
| or practical_subtasks.lower() | |
| == "finding a suitable colleague by personality types" | |
| ): | |
| pt_scores_copy = pt_scores.iloc[:, 1:].copy() | |
| preprocess_scores_df(pt_scores_copy, config_data.Dataframes_PT_SCORES[0][0]) | |
| if ( | |
| practical_subtasks.lower() | |
| != "finding a suitable colleague by personality types" | |
| ): | |
| df_correlation_coefficients = read_csv_file( | |
| config_data.Links_FINDING_COLLEAGUE, ["ID"] | |
| ) | |
| b5._colleague_ranking( | |
| df_files=pt_scores_copy, | |
| 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 | |
| ) | |
| else: | |
| b5._colleague_personality_type_match( | |
| df_files=pt_scores_copy, | |
| correlation_coefficients=None, | |
| target_scores=[ | |
| target_score_ope, | |
| target_score_con, | |
| target_score_ext, | |
| target_score_agr, | |
| target_score_nneu, | |
| ], | |
| threshold=equal_coefficient, | |
| out=False, | |
| ) | |
| df = b5.df_files_MBTI_colleague_match_.rename( | |
| columns={ | |
| "MBTI": "Personality Type", | |
| "MBTI_Score": "Personality Type Score", | |
| } | |
| ) | |
| df_hidden = df[["Path", "Personality Type", "Match"]] | |
| df_hidden.to_csv(config_data.Filenames_COLLEAGUE_RANKING) | |
| df_hidden.reset_index(inplace=True) | |
| person_id = ( | |
| int( | |
| df_hidden.iloc[ | |
| ( | |
| 0 | |
| if practical_subtasks.lower() | |
| != "finding a suitable colleague by personality types" | |
| else 1 | |
| ) | |
| ][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 styles" | |
| ): | |
| 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 styles": | |
| df_correlation_coefficients = read_csv_file(config_data.Links_CLOTHING_SC) | |
| if type_modes == config_data.Settings_TYPE_MODES[1]: | |
| number_priority = df_correlation_coefficients.columns.size - 1 | |
| number_importance_traits = 5 | |
| 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]) | |
| if type_modes == config_data.Settings_TYPE_MODES[0]: | |
| del_cols = [] | |
| elif type_modes == config_data.Settings_TYPE_MODES[1]: | |
| del_cols = config_data.Settings_DROPDOWN_MBTI_DEL_COLS_WEBCAM | |
| df_hidden = df.drop( | |
| columns=config_data.Settings_SHORT_PROFESSIONAL_SKILLS + del_cols | |
| ) | |
| if type_modes == config_data.Settings_TYPE_MODES[1]: | |
| df_hidden = df_hidden.T | |
| df_hidden = df_hidden.head(-number_importance_traits) | |
| df_hidden = df_hidden.reset_index() | |
| df_hidden.columns = ["Priority", "Category"] | |
| df_hidden.to_csv(consumer_preferences(practical_subtasks)) | |
| df_hidden.reset_index( | |
| drop=True if type_modes == config_data.Settings_TYPE_MODES[1] else False, | |
| inplace=True, | |
| ) | |
| if type_modes == config_data.Settings_TYPE_MODES[0]: | |
| person_id = ( | |
| int(df_hidden.iloc[0][config_data.Dataframes_PT_SCORES[0][0]]) - 1 | |
| ) | |
| elif type_modes == config_data.Settings_TYPE_MODES[1]: | |
| person_id = 0 | |
| 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), | |
| ) | |