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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 gradio as gr
from pathlib import Path
# Importing necessary components for the Gradio app
from app.config import config_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
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 event_handler_calculate_practical_task_blocks(
files,
practical_subtasks,
pt_scores,
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() == "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",
),
video_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]["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_POTENTIAL_CANDIDATES,
"single",
[".csv"],
config_data.OtherMessages_EXPORT_PG,
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),
)
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]["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),
)
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]["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(
colleague_type(practical_subtasks)
+ config_data.Filenames_COLLEAGUE_RANKING,
"single",
[".csv"],
config_data.OtherMessages_EXPORT_WT,
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),
)
elif (
practical_subtasks.lower() == "car characteristics"
or practical_subtasks.lower() == "mobile device application categories"
):
if practical_subtasks.lower() == "car characteristics":
df_correlation_coefficients = read_csv_file(
config_data.Links_CAR_CHARACTERISTICS,
["Style and performance", "Safety and practicality"],
)
if practical_subtasks.lower() == "mobile device application categories":
df_correlation_coefficients = read_csv_file(
config_data.Links_MDA_CATEGORIES
)
pt_scores_copy = pt_scores.iloc[:, 1:].copy()
preprocess_scores_df(pt_scores_copy, "Person ID")
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, "Person ID")
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]["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(
consumer_preferences(practical_subtasks),
"single",
[".csv"],
config_data.OtherMessages_EXPORT_CP,
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),
)
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