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"""
File: tabs.py
Author: Elena Ryumina and Dmitry Ryumin
Description: Gradio app tabs - Contains the definition of various tabs for the Gradio app interface.
License: MIT License
"""
import gradio as gr
# Importing necessary components for the Gradio app
from app.description import DESCRIPTIONS
from app.description_steps import STEP_1, STEP_2
from app.mbti_description import MBTI_DESCRIPTION, MBTI_DATA
from app.app import APP
from app.authors import AUTHORS
from app.data_init import weights_professions, interactive_professions
from app.requirements_app import read_requirements_to_df
from app.config import config_data
from app.practical_tasks import supported_practical_tasks
from app.utils import read_csv_file
from app.components import (
html_message,
files_create_ui,
video_create_ui,
button,
dataframe,
radio_create_ui,
number_create_ui,
dropdown_create_ui,
textbox_create_ui,
)
def app_tab():
description = gr.Markdown(
value=DESCRIPTIONS[config_data.AppSettings_DEFAULT_LANG_ID]
)
step_1 = gr.HTML(value=STEP_1[config_data.AppSettings_DEFAULT_LANG_ID])
with gr.Row(elem_classes="media-container"):
with gr.Column():
files = files_create_ui(
label="{} ({})".format(
config_data.OtherMessages_VIDEO_FILES[
config_data.AppSettings_DEFAULT_LANG_ID
],
", ".join(config_data.Settings_SUPPORTED_VIDEO_EXT),
),
file_types=[
f".{ext}" for ext in config_data.Settings_SUPPORTED_VIDEO_EXT
],
)
webcam = gr.Video(
label="{} ({})".format(
config_data.OtherMessages_VIDEO_FILES[
config_data.AppSettings_DEFAULT_LANG_ID
],
", ".join(config_data.Settings_SUPPORTED_VIDEO_EXT),
),
show_label=True,
interactive=False,
visible=False,
mirror_webcam=False,
include_audio=True,
elem_classes="webcam",
autoplay=False,
)
switching_modes = button(
config_data.OtherMessages_SWITCHEHG_MODES_ONLINE[
config_data.AppSettings_DEFAULT_LANG_ID
],
True,
1,
"./images/webcam.ico",
True,
"switching_modes",
)
type_modes = gr.Radio(
choices=config_data.Settings_TYPE_MODES,
value=config_data.Settings_TYPE_MODES[0],
label=None,
info=None,
show_label=False,
container=False,
interactive=False,
visible=False,
render=True,
elem_classes="type_modes",
)
video = video_create_ui()
with gr.Row():
examples = button(
config_data.OtherMessages_EXAMPLES_APP[
config_data.AppSettings_DEFAULT_LANG_ID
],
True,
1,
"./images/examples.ico",
True,
"examples_oceanai",
)
calculate_pt_scores = button(
config_data.OtherMessages_CALCULATE_PT_SCORES[
config_data.AppSettings_DEFAULT_LANG_ID
],
False,
3,
"./images/calculate_pt_scores.ico",
True,
"calculate_oceanai",
)
clear_app = button(
config_data.OtherMessages_CLEAR_APP[
config_data.AppSettings_DEFAULT_LANG_ID
],
False,
1,
"./images/clear.ico",
True,
"clear_oceanai",
)
notifications = html_message(
config_data.InformationMessages_NOTI_VIDEOS[
config_data.AppSettings_DEFAULT_LANG_ID
],
False,
"notifications",
)
pt_scores = dataframe(visible=False)
csv_pt_scores = files_create_ui(
None,
"single",
[".csv"],
config_data.OtherMessages_EXPORT_PT_SCORES[
config_data.AppSettings_DEFAULT_LANG_ID
],
True,
False,
False,
"csv-container",
)
step_2 = gr.HTML(
value=STEP_2[config_data.AppSettings_DEFAULT_LANG_ID], visible=False
)
first_practical_task = next(
iter(supported_practical_tasks[config_data.AppSettings_DEFAULT_LANG_ID])
)
with gr.Column(scale=1, visible=False, render=True) as practical_tasks_column:
practical_tasks = radio_create_ui(
first_practical_task,
config_data.Labels_PRACTICAL_TASKS_LABEL,
list(
map(
str,
supported_practical_tasks[
config_data.AppSettings_DEFAULT_LANG_ID
].keys(),
)
),
config_data.InformationMessages_PRACTICAL_TASKS_INFO[
config_data.AppSettings_DEFAULT_LANG_ID
],
True,
True,
)
practical_subtasks = radio_create_ui(
supported_practical_tasks[config_data.AppSettings_DEFAULT_LANG_ID][
first_practical_task
][0],
config_data.Labels_PRACTICAL_SUBTASKS_LABEL[
config_data.AppSettings_DEFAULT_LANG_ID
],
supported_practical_tasks[config_data.AppSettings_DEFAULT_LANG_ID][
first_practical_task
],
config_data.InformationMessages_PRACTICAL_SUBTASKS_INFO[
config_data.AppSettings_DEFAULT_LANG_ID
],
True,
True,
)
with gr.Row(
visible=False,
render=True,
variant="default",
elem_classes="settings-container",
) as settings_practical_tasks:
dropdown_mbti = dropdown_create_ui(
label=f"Potential candidates by Personality Type of MBTI ({len(config_data.Settings_DROPDOWN_MBTI)})",
info=config_data.InformationMessages_DROPDOWN_MBTI_INFO,
choices=config_data.Settings_DROPDOWN_MBTI,
value=config_data.Settings_DROPDOWN_MBTI[0],
visible=False,
elem_classes="dropdown-container",
)
threshold_mbti = number_create_ui(
value=0.5,
minimum=0.0,
maximum=1.0,
step=0.01,
label=config_data.Labels_THRESHOLD_MBTI_LABEL,
info=config_data.InformationMessages_VALUE_FROM_TO_INFO.format(0, 1.0),
show_label=True,
interactive=True,
visible=False,
render=True,
elem_classes="number-container",
)
threshold_professional_skills = number_create_ui(
value=0.5,
minimum=0.0,
maximum=1.0,
step=0.01,
label=config_data.Labels_THRESHOLD_PROFESSIONAL_SKILLS_LABEL,
info=config_data.InformationMessages_VALUE_FROM_TO_INFO.format(0, 1.0),
show_label=True,
interactive=True,
visible=False,
render=True,
elem_classes="number-container",
)
dropdown_professional_skills = dropdown_create_ui(
label=f"Professional skills ({len(config_data.Settings_DROPDOWN_PROFESSIONAL_SKILLS)})",
info=config_data.InformationMessages_DROPDOWN_PROFESSIONAL_SKILLS_INFO,
choices=config_data.Settings_DROPDOWN_PROFESSIONAL_SKILLS,
value=config_data.Settings_DROPDOWN_PROFESSIONAL_SKILLS[0],
visible=False,
elem_classes="dropdown-container",
)
target_score_ope = number_create_ui(
value=config_data.Values_TARGET_SCORES[0],
minimum=0.0,
maximum=1.0,
step=0.000001,
label=config_data.Labels_TARGET_SCORE_OPE_LABEL,
info=config_data.InformationMessages_VALUE_FROM_TO_INFO.format(0, 1.0),
show_label=True,
interactive=True,
visible=False,
render=True,
elem_classes="number-container",
)
target_score_con = number_create_ui(
value=config_data.Values_TARGET_SCORES[1],
minimum=0.0,
maximum=1.0,
step=0.000001,
label=config_data.Labels_TARGET_SCORE_CON_LABEL,
info=config_data.InformationMessages_VALUE_FROM_TO_INFO.format(0, 1.0),
show_label=True,
interactive=True,
visible=False,
render=True,
elem_classes="number-container",
)
target_score_ext = number_create_ui(
value=config_data.Values_TARGET_SCORES[2],
minimum=0.0,
maximum=1.0,
step=0.000001,
label=config_data.Labels_TARGET_SCORE_EXT_LABEL,
info=config_data.InformationMessages_VALUE_FROM_TO_INFO.format(0, 1.0),
show_label=True,
interactive=True,
visible=False,
render=True,
elem_classes="number-container",
)
target_score_agr = number_create_ui(
value=config_data.Values_TARGET_SCORES[3],
minimum=0.0,
maximum=1.0,
step=0.000001,
label=config_data.Labels_TARGET_SCORE_AGR_LABEL,
info=config_data.InformationMessages_VALUE_FROM_TO_INFO.format(0, 1.0),
show_label=True,
interactive=True,
visible=False,
render=True,
elem_classes="number-container",
)
target_score_nneu = number_create_ui(
value=config_data.Values_TARGET_SCORES[4],
minimum=0.0,
maximum=1.0,
step=0.000001,
label=config_data.Labels_TARGET_SCORE_NNEU_LABEL,
info=config_data.InformationMessages_VALUE_FROM_TO_INFO.format(0, 1.0),
show_label=True,
interactive=True,
visible=False,
render=True,
elem_classes="number-container",
)
equal_coefficient = number_create_ui(
value=0.5,
minimum=0.0,
maximum=1.0,
step=0.01,
label=config_data.Labels_EQUAL_COEFFICIENT_LABEL,
info=config_data.InformationMessages_VALUE_FROM_TO_INFO.format(0, 1.0),
show_label=True,
interactive=True,
visible=False,
render=True,
elem_classes="number-container",
)
df_correlation_coefficients = read_csv_file(
config_data.Links_CAR_CHARACTERISTICS,
["Trait", "Style and performance", "Safety and practicality"],
)
number_priority = number_create_ui(
value=1,
minimum=1,
maximum=df_correlation_coefficients.columns.size,
step=1,
label=config_data.Labels_NUMBER_PRIORITY_LABEL,
info=config_data.InformationMessages_VALUE_FROM_TO_INFO.format(
1, df_correlation_coefficients.columns.size
),
show_label=True,
interactive=True,
visible=False,
render=True,
elem_classes="number-container",
)
number_importance_traits = number_create_ui(
value=1,
minimum=1,
maximum=5,
step=1,
label=config_data.Labels_NUMBER_IMPORTANCE_TRAITS_LABEL,
info=config_data.InformationMessages_VALUE_FROM_TO_INFO.format(1, 5),
show_label=True,
interactive=True,
visible=False,
render=True,
elem_classes="number-container",
)
threshold_consumer_preferences = number_create_ui(
value=0.55,
minimum=0.0,
maximum=1.0,
step=0.01,
label=config_data.Labels_THRESHOLD_CONSUMER_PREFERENCES_LABEL,
info=config_data.InformationMessages_VALUE_FROM_TO_INFO.format(0, 1.0),
show_label=True,
interactive=True,
visible=False,
render=True,
elem_classes="number-container",
)
dropdown_candidates = dropdown_create_ui(
label=f"Potential candidates by professional responsibilities ({len(config_data.Settings_DROPDOWN_CANDIDATES)})",
info=config_data.InformationMessages_DROPDOWN_CANDIDATES_INFO,
choices=config_data.Settings_DROPDOWN_CANDIDATES,
value=config_data.Settings_DROPDOWN_CANDIDATES[0],
visible=False,
elem_classes="dropdown-container",
)
number_openness = number_create_ui(
value=weights_professions[0],
minimum=config_data.Values_0_100[0],
maximum=config_data.Values_0_100[1],
step=1,
label=config_data.Labels_NUMBER_IMPORTANCE_OPE_LABEL,
info=config_data.InformationMessages_VALUE_FROM_TO_INFO.format(
config_data.Values_0_100[0], config_data.Values_0_100[1]
),
show_label=True,
interactive=interactive_professions,
visible=False,
render=True,
elem_classes="number-container",
)
number_conscientiousness = number_create_ui(
value=weights_professions[1],
minimum=config_data.Values_0_100[0],
maximum=config_data.Values_0_100[1],
step=1,
label=config_data.Labels_NUMBER_IMPORTANCE_CON_LABEL,
info=config_data.InformationMessages_VALUE_FROM_TO_INFO.format(
config_data.Values_0_100[0], config_data.Values_0_100[1]
),
show_label=True,
interactive=interactive_professions,
visible=False,
render=True,
elem_classes="number-container",
)
number_extraversion = number_create_ui(
value=weights_professions[2],
minimum=config_data.Values_0_100[0],
maximum=config_data.Values_0_100[1],
step=1,
label=config_data.Labels_NUMBER_IMPORTANCE_EXT_LABEL,
info=config_data.InformationMessages_VALUE_FROM_TO_INFO.format(
config_data.Values_0_100[0], config_data.Values_0_100[1]
),
show_label=True,
interactive=interactive_professions,
visible=False,
render=True,
elem_classes="number-container",
)
number_agreeableness = number_create_ui(
value=weights_professions[3],
minimum=config_data.Values_0_100[0],
maximum=config_data.Values_0_100[1],
step=1,
label=config_data.Labels_NUMBER_IMPORTANCE_AGR_LABEL,
info=config_data.InformationMessages_VALUE_FROM_TO_INFO.format(
config_data.Values_0_100[0], config_data.Values_0_100[1]
),
show_label=True,
interactive=interactive_professions,
visible=False,
render=True,
elem_classes="number-container",
)
number_non_neuroticism = number_create_ui(
value=weights_professions[4],
minimum=config_data.Values_0_100[0],
maximum=config_data.Values_0_100[1],
step=1,
label=config_data.Labels_NUMBER_IMPORTANCE_NNEU_LABEL,
info=config_data.InformationMessages_VALUE_FROM_TO_INFO.format(
config_data.Values_0_100[0], config_data.Values_0_100[1]
),
show_label=True,
interactive=interactive_professions,
visible=False,
render=True,
elem_classes="number-container",
)
calculate_practical_task = button(
config_data.OtherMessages_CALCULATE_PRACTICAL_TASK,
True,
1,
"./images/pt.ico",
False,
"calculate_practical_task",
)
with gr.Row(
visible=False,
render=True,
variant="default",
) as sorted_videos:
with gr.Column(scale=1, visible=False, render=True) as sorted_videos_column:
practical_task_sorted = dataframe(visible=False)
with gr.Accordion(
label=config_data.Labels_NOTE_MBTI_LABEL,
open=False,
visible=False,
) as mbti_accordion:
mbti_description = gr.HTML(value=MBTI_DESCRIPTION, visible=False)
mbti_description_data = dataframe(
headers=MBTI_DATA.columns.tolist(),
values=MBTI_DATA.values.tolist(),
visible=False,
elem_classes="mbti-dataframe",
)
csv_practical_task_sorted = files_create_ui(
None,
"single",
[".csv"],
config_data.OtherMessages_EXPORT_PS,
True,
False,
False,
"csv-container",
)
with gr.Column(
scale=1,
visible=False,
render=True,
elem_classes="video-column-container",
) as video_sorted_column:
video_sorted = video_create_ui(
visible=False, elem_classes="video-sorted-container"
)
with gr.Column(scale=1, visible=False, render=True) as metadata:
with gr.Row(
visible=False, render=True, variant="default"
) as metadata_1:
with gr.Row(
visible=False,
render=True,
variant="default",
elem_classes="name-container",
) as name_row:
name_logo = gr.Image(
value="images/name.svg",
container=False,
interactive=False,
show_label=False,
visible=False,
show_download_button=False,
elem_classes="metadata_name-logo",
show_fullscreen_button=False,
)
name = textbox_create_ui(
"First name",
"text",
"First name",
None,
None,
1,
True,
False,
False,
False,
1,
False,
)
with gr.Row(
visible=False,
render=True,
variant="default",
elem_classes="surname-container",
) as surname_row:
surname_logo = gr.Image(
value="images/name.svg",
container=False,
interactive=False,
show_label=False,
visible=False,
show_download_button=False,
elem_classes="metadata_surname-logo",
show_fullscreen_button=False,
)
surname = textbox_create_ui(
"Last name",
"text",
"Last name",
None,
None,
1,
True,
False,
False,
False,
1,
False,
)
with gr.Row(
visible=False, render=True, variant="default"
) as metadata_2:
with gr.Row(
visible=False,
render=True,
variant="default",
elem_classes="email-container",
) as email_row:
email_logo = gr.Image(
value="images/email.svg",
container=False,
interactive=False,
show_label=False,
visible=False,
show_download_button=False,
elem_classes="metadata_email-logo",
show_fullscreen_button=False,
)
email = textbox_create_ui(
"[email protected]",
"email",
"Email",
None,
None,
1,
True,
False,
False,
False,
1,
False,
)
with gr.Row(
visible=False,
render=True,
variant="default",
elem_classes="phone-container",
) as phone_row:
phone_logo = gr.Image(
value="images/phone.svg",
container=False,
interactive=False,
show_label=False,
visible=False,
show_download_button=False,
elem_classes="metadata_phone-logo",
show_fullscreen_button=False,
)
phone = textbox_create_ui(
"+1 (555) 123-4567",
"text",
"Phone number",
None,
None,
1,
True,
False,
False,
False,
1,
False,
)
practical_subtasks_selected = gr.JSON(
value={
str(task): supported_practical_tasks[index].get(task, [None])[0]
for index in range(len(supported_practical_tasks))
for task in supported_practical_tasks[index].keys()
},
visible=False,
render=True,
)
in_development = html_message(
config_data.InformationMessages_NOTI_IN_DEV, False, False
)
return (
description,
step_1,
notifications,
files,
webcam,
switching_modes,
type_modes,
video,
examples,
calculate_pt_scores,
clear_app,
pt_scores,
csv_pt_scores,
step_2,
practical_tasks,
practical_subtasks,
settings_practical_tasks,
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,
dropdown_candidates,
number_openness,
number_conscientiousness,
number_extraversion,
number_agreeableness,
number_non_neuroticism,
calculate_practical_task,
practical_subtasks_selected,
practical_tasks_column,
sorted_videos,
sorted_videos_column,
practical_task_sorted,
csv_practical_task_sorted,
mbti_accordion,
mbti_description,
mbti_description_data,
video_sorted_column,
video_sorted,
metadata,
metadata_1,
name_row,
name_logo,
name,
surname_row,
surname_logo,
surname,
metadata_2,
email_row,
email_logo,
email,
phone_row,
phone_logo,
phone,
in_development,
)
def about_app_tab():
return gr.HTML(value=APP)
def about_authors_tab():
return gr.HTML(value=AUTHORS)
def requirements_app_tab():
requirements_df = read_requirements_to_df()
return dataframe(
headers=requirements_df.columns.tolist(),
values=requirements_df.values.tolist(),
visible=True,
elem_classes="requirements-dataframe",
)
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