ElenaRyumina DmitryRyumin commited on
Commit
52d84c5
1 Parent(s): d6b712b

- Summary (096ab960a7b3d1c3a70df90cadf556317f1a043c)


Co-authored-by: Dmitry Ryumin <DmitryRyumin@users.noreply.huggingface.co>

app/event_handlers/calculate_practical_tasks.py CHANGED
@@ -193,7 +193,7 @@ def event_handler_calculate_practical_task_blocks(
193
 
194
  pt_scores_copy = pt_scores.iloc[:, 1:].copy()
195
 
196
- preprocess_scores_df(pt_scores_copy, "Person ID")
197
 
198
  b5._professional_match(
199
  df_files=pt_scores_copy,
@@ -223,7 +223,7 @@ def event_handler_calculate_practical_task_blocks(
223
 
224
  df_hidden.reset_index(inplace=True)
225
 
226
- person_id = int(df_hidden.iloc[0]["Person ID"]) - 1
227
 
228
  short_mbti = extract_text_in_parentheses(dropdown_mbti)
229
  mbti_values = df_hidden["Personality Type"].tolist()
@@ -349,7 +349,9 @@ def event_handler_calculate_practical_task_blocks(
349
 
350
  df_hidden.reset_index(inplace=True)
351
 
352
- person_id = int(df_hidden.iloc[0]["Person ID"]) - 1
 
 
353
 
354
  person_metadata = create_person_metadata(person_id, files, video_metadata)
355
 
@@ -417,7 +419,7 @@ def event_handler_calculate_practical_task_blocks(
417
  by=[dropdown_professional_skills], ascending=False
418
  )
419
 
420
- person_id = int(df_hidden.iloc[0]["Person ID"]) - 1
421
 
422
  person_metadata = create_person_metadata(person_id, files, video_metadata)
423
 
@@ -487,7 +489,7 @@ def event_handler_calculate_practical_task_blocks(
487
 
488
  df_hidden.reset_index(inplace=True)
489
 
490
- person_id = int(df_hidden.iloc[0]["Person ID"]) - 1
491
 
492
  person_metadata = create_person_metadata(person_id, files, video_metadata)
493
 
@@ -544,7 +546,7 @@ def event_handler_calculate_practical_task_blocks(
544
 
545
  pt_scores_copy = pt_scores.iloc[:, 1:].copy()
546
 
547
- preprocess_scores_df(pt_scores_copy, "Person ID")
548
 
549
  b5._priority_calculation(
550
  df_files=pt_scores_copy,
@@ -561,7 +563,7 @@ def event_handler_calculate_practical_task_blocks(
561
 
562
  df = apply_rounding_and_rename_columns(df_files_priority.iloc[:, 1:])
563
 
564
- preprocess_scores_df(df, "Person ID")
565
 
566
  df_hidden = df.drop(columns=config_data.Settings_SHORT_PROFESSIONAL_SKILLS)
567
 
@@ -569,7 +571,7 @@ def event_handler_calculate_practical_task_blocks(
569
 
570
  df_hidden.reset_index(inplace=True)
571
 
572
- person_id = int(df_hidden.iloc[0]["Person ID"]) - 1
573
 
574
  person_metadata = create_person_metadata(person_id, files, video_metadata)
575
 
 
193
 
194
  pt_scores_copy = pt_scores.iloc[:, 1:].copy()
195
 
196
+ preprocess_scores_df(pt_scores_copy, config_data.Dataframes_PT_SCORES[0][0])
197
 
198
  b5._professional_match(
199
  df_files=pt_scores_copy,
 
223
 
224
  df_hidden.reset_index(inplace=True)
225
 
226
+ person_id = int(df_hidden.iloc[0][config_data.Dataframes_PT_SCORES[0][0]]) - 1
227
 
228
  short_mbti = extract_text_in_parentheses(dropdown_mbti)
229
  mbti_values = df_hidden["Personality Type"].tolist()
 
349
 
350
  df_hidden.reset_index(inplace=True)
351
 
352
+ person_id = (
353
+ int(df_hidden.iloc[0][config_data.Dataframes_PT_SCORES[0][0]]) - 1
354
+ )
355
 
356
  person_metadata = create_person_metadata(person_id, files, video_metadata)
357
 
 
419
  by=[dropdown_professional_skills], ascending=False
420
  )
421
 
422
+ person_id = int(df_hidden.iloc[0][config_data.Dataframes_PT_SCORES[0][0]]) - 1
423
 
424
  person_metadata = create_person_metadata(person_id, files, video_metadata)
425
 
 
489
 
490
  df_hidden.reset_index(inplace=True)
491
 
492
+ person_id = int(df_hidden.iloc[0][config_data.Dataframes_PT_SCORES[0][0]]) - 1
493
 
494
  person_metadata = create_person_metadata(person_id, files, video_metadata)
495
 
 
546
 
547
  pt_scores_copy = pt_scores.iloc[:, 1:].copy()
548
 
549
+ preprocess_scores_df(pt_scores_copy, config_data.Dataframes_PT_SCORES[0][0])
550
 
551
  b5._priority_calculation(
552
  df_files=pt_scores_copy,
 
563
 
564
  df = apply_rounding_and_rename_columns(df_files_priority.iloc[:, 1:])
565
 
566
+ preprocess_scores_df(df, config_data.Dataframes_PT_SCORES[0][0])
567
 
568
  df_hidden = df.drop(columns=config_data.Settings_SHORT_PROFESSIONAL_SKILLS)
569
 
 
571
 
572
  df_hidden.reset_index(inplace=True)
573
 
574
+ person_id = int(df_hidden.iloc[0][config_data.Dataframes_PT_SCORES[0][0]]) - 1
575
 
576
  person_metadata = create_person_metadata(person_id, files, video_metadata)
577
 
app/event_handlers/calculate_pt_scores_blocks.py CHANGED
@@ -11,7 +11,7 @@ import gradio as gr
11
  from app.oceanai_init import b5
12
  from app.config import config_data
13
  from app.description_steps import STEP_2
14
- from app.utils import read_csv_file, extract_profession_weights, get_language_settings
15
  from app.practical_tasks import supported_practical_tasks
16
  from app.components import (
17
  html_message,
@@ -48,7 +48,7 @@ def event_handler_calculate_pt_scores_blocks(language, files, evt_data: gr.Event
48
  None,
49
  "single",
50
  [".csv"],
51
- config_data.OtherMessages_EXPORT_PT_SCORES,
52
  True,
53
  False,
54
  False,
@@ -149,18 +149,12 @@ def event_handler_calculate_pt_scores_blocks(language, files, evt_data: gr.Event
149
  df_files = b5.df_files_.copy()
150
  df_files.reset_index(inplace=True)
151
 
152
- df_traits_priority_for_professions = read_csv_file(config_data.Links_PROFESSIONS)
153
- weights_professions, interactive_professions = extract_profession_weights(
154
- df_traits_priority_for_professions,
155
- config_data.Settings_DROPDOWN_CANDIDATES[0],
156
- )
157
-
158
  return (
159
  html_message(
160
  config_data.InformationMessages_NOTI_VIDEOS[lang_id], False, False
161
  ),
162
  dataframe(
163
- headers=df_files.columns.tolist(),
164
  values=df_files.values.tolist(),
165
  visible=True,
166
  ),
@@ -168,7 +162,7 @@ def event_handler_calculate_pt_scores_blocks(language, files, evt_data: gr.Event
168
  config_data.Filenames_PT_SCORES,
169
  "single",
170
  [".csv"],
171
- config_data.OtherMessages_EXPORT_PT_SCORES,
172
  True,
173
  False,
174
  True,
 
11
  from app.oceanai_init import b5
12
  from app.config import config_data
13
  from app.description_steps import STEP_2
14
+ from app.utils import get_language_settings
15
  from app.practical_tasks import supported_practical_tasks
16
  from app.components import (
17
  html_message,
 
48
  None,
49
  "single",
50
  [".csv"],
51
+ config_data.OtherMessages_EXPORT_PT_SCORES[lang_id],
52
  True,
53
  False,
54
  False,
 
149
  df_files = b5.df_files_.copy()
150
  df_files.reset_index(inplace=True)
151
 
 
 
 
 
 
 
152
  return (
153
  html_message(
154
  config_data.InformationMessages_NOTI_VIDEOS[lang_id], False, False
155
  ),
156
  dataframe(
157
+ headers=(config_data.Dataframes_PT_SCORES[lang_id]),
158
  values=df_files.values.tolist(),
159
  visible=True,
160
  ),
 
162
  config_data.Filenames_PT_SCORES,
163
  "single",
164
  [".csv"],
165
+ config_data.OtherMessages_EXPORT_PT_SCORES[lang_id],
166
  True,
167
  False,
168
  True,
app/event_handlers/clear_blocks.py CHANGED
@@ -61,7 +61,7 @@ def event_handler_clear_blocks(language):
61
  None,
62
  "single",
63
  [".csv"],
64
- config_data.OtherMessages_EXPORT_PT_SCORES,
65
  True,
66
  False,
67
  False,
 
61
  None,
62
  "single",
63
  [".csv"],
64
+ config_data.OtherMessages_EXPORT_PT_SCORES[lang_id],
65
  True,
66
  False,
67
  False,
app/event_handlers/event_handlers.py CHANGED
@@ -101,7 +101,7 @@ def setup_app_event_handlers(
101
  # Events
102
  languages.select(
103
  fn=event_handler_languages,
104
- inputs=[languages, files, video, pt_scores],
105
  outputs=[
106
  description,
107
  step_1,
@@ -117,6 +117,9 @@ def setup_app_event_handlers(
117
  calculate_pt_scores,
118
  clear_app,
119
  notifications,
 
 
 
120
  ],
121
  queue=True,
122
  )
 
101
  # Events
102
  languages.select(
103
  fn=event_handler_languages,
104
+ inputs=[languages, files, video, pt_scores, csv_pt_scores],
105
  outputs=[
106
  description,
107
  step_1,
 
117
  calculate_pt_scores,
118
  clear_app,
119
  notifications,
120
+ pt_scores,
121
+ csv_pt_scores,
122
+ step_2,
123
  ],
124
  queue=True,
125
  )
app/event_handlers/languages.py CHANGED
@@ -10,7 +10,7 @@ from pathlib import Path
10
 
11
  # Importing necessary components for the Gradio app
12
  from app.description import DESCRIPTIONS
13
- from app.description_steps import STEP_1
14
  from app.config import config_data
15
  from app.components import (
16
  files_create_ui,
@@ -18,11 +18,12 @@ from app.components import (
18
  dropdown_create_ui,
19
  button,
20
  html_message,
 
21
  )
22
  from app.utils import get_language_settings
23
 
24
 
25
- def event_handler_languages(languages, files, video, pt_scores):
26
  lang_id, choices = get_language_settings(languages)
27
 
28
  if not video:
@@ -42,6 +43,29 @@ def event_handler_languages(languages, files, video, pt_scores):
42
  False if pt_scores.shape[1] >= 7 else True,
43
  )
44
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
45
  return (
46
  gr.Markdown(value=DESCRIPTIONS[lang_id]),
47
  gr.HTML(value=STEP_1[lang_id]),
@@ -95,4 +119,7 @@ def event_handler_languages(languages, files, video, pt_scores):
95
  "clear_oceanai",
96
  ),
97
  noti_videos,
 
 
 
98
  )
 
10
 
11
  # Importing necessary components for the Gradio app
12
  from app.description import DESCRIPTIONS
13
+ from app.description_steps import STEP_1, STEP_2
14
  from app.config import config_data
15
  from app.components import (
16
  files_create_ui,
 
18
  dropdown_create_ui,
19
  button,
20
  html_message,
21
+ dataframe,
22
  )
23
  from app.utils import get_language_settings
24
 
25
 
26
+ def event_handler_languages(languages, files, video, pt_scores, csv_pt_scores):
27
  lang_id, choices = get_language_settings(languages)
28
 
29
  if not video:
 
43
  False if pt_scores.shape[1] >= 7 else True,
44
  )
45
 
46
+ csv_pt_scores = files_create_ui(
47
+ csv_pt_scores if pt_scores.shape[1] >= 7 else None,
48
+ "single",
49
+ [".csv"],
50
+ config_data.OtherMessages_EXPORT_PT_SCORES[lang_id],
51
+ True,
52
+ False,
53
+ True if pt_scores.shape[1] >= 7 else False,
54
+ "csv-container",
55
+ )
56
+ step_2 = gr.HTML(
57
+ value=STEP_2[lang_id], visible=True if pt_scores.shape[1] >= 7 else False
58
+ )
59
+
60
+ if pt_scores.shape[1] >= 7:
61
+ pt_scores = dataframe(
62
+ headers=(config_data.Dataframes_PT_SCORES[lang_id]),
63
+ values=pt_scores.values.tolist(),
64
+ visible=True,
65
+ )
66
+ else:
67
+ pt_scores = dataframe(visible=False)
68
+
69
  return (
70
  gr.Markdown(value=DESCRIPTIONS[lang_id]),
71
  gr.HTML(value=STEP_1[lang_id]),
 
119
  "clear_oceanai",
120
  ),
121
  noti_videos,
122
+ pt_scores,
123
+ csv_pt_scores,
124
+ step_2,
125
  )
app/event_handlers/practical_task_sorted.py CHANGED
@@ -9,6 +9,7 @@ import gradio as gr
9
  from pathlib import Path
10
 
11
  # Importing necessary components for the Gradio app
 
12
  from app.video_metadata import video_metadata
13
  from app.components import video_create_ui, textbox_create_ui
14
 
@@ -16,13 +17,20 @@ from app.components import video_create_ui, textbox_create_ui
16
  def event_handler_practical_task_sorted(
17
  files, practical_task_sorted, evt_data: gr.SelectData
18
  ):
19
- person_id = int(practical_task_sorted.iloc[evt_data.index[0]]["Person ID"]) - 1
 
 
 
 
 
 
 
20
 
21
  if evt_data.index[0] == 0:
22
  label = "Best"
23
  else:
24
  label = ""
25
- label += " Person ID"
26
 
27
  if Path(files[person_id]).name in video_metadata:
28
  person_metadata_list = video_metadata[Path(files[person_id]).name]
 
9
  from pathlib import Path
10
 
11
  # Importing necessary components for the Gradio app
12
+ from app.config import config_data
13
  from app.video_metadata import video_metadata
14
  from app.components import video_create_ui, textbox_create_ui
15
 
 
17
  def event_handler_practical_task_sorted(
18
  files, practical_task_sorted, evt_data: gr.SelectData
19
  ):
20
+ person_id = (
21
+ int(
22
+ practical_task_sorted.iloc[evt_data.index[0]][
23
+ config_data.Dataframes_PT_SCORES[0][0]
24
+ ]
25
+ )
26
+ - 1
27
+ )
28
 
29
  if evt_data.index[0] == 0:
30
  label = "Best"
31
  else:
32
  label = ""
33
+ label += " " + config_data.Dataframes_PT_SCORES[0][0]
34
 
35
  if Path(files[person_id]).name in video_metadata:
36
  person_metadata_list = video_metadata[Path(files[person_id]).name]
app/tabs.py CHANGED
@@ -95,7 +95,9 @@ def app_tab():
95
  None,
96
  "single",
97
  [".csv"],
98
- config_data.OtherMessages_EXPORT_PT_SCORES,
 
 
99
  True,
100
  False,
101
  False,
 
95
  None,
96
  "single",
97
  [".csv"],
98
+ config_data.OtherMessages_EXPORT_PT_SCORES[
99
+ config_data.AppSettings_DEFAULT_LANG_ID
100
+ ],
101
  True,
102
  False,
103
  False,
config.toml CHANGED
@@ -1,5 +1,5 @@
1
  [AppSettings]
2
- APP_VERSION = "0.9.1"
3
  CSS_PATH = "app.css"
4
  DEFAULT_LANG_ID = 0
5
 
@@ -36,7 +36,9 @@ CALCULATE_PT_SCORES_ERR = "Personality traits scores have not been calculated. T
36
  CALCULATE_PRACTICAL_TASK = "Solving practical task"
37
  CLEAR_APP = ["Clear", "Сброс"]
38
  EXAMPLES_APP = ["Examples", "Примеры"]
39
- EXPORT_PT_SCORES = "Export Big Five personality traits to a CSV file"
 
 
40
  EXPORT_PG = "Export ranking professional groups results to a CSV file"
41
  EXPORT_PS = "Export ranking professional skill results to a CSV file"
42
  EXPORT_WT = "Export ranking effective work teams results to a CSV file"
@@ -86,6 +88,17 @@ MDA_CATEGORIES = "divice_characteristics_priorities.csv"
86
  POTENTIAL_CANDIDATES = "potential_candidates.csv"
87
  MBTI_JOB = "mbti_job_match.csv"
88
 
 
 
 
 
 
 
 
 
 
 
 
89
  [Images]
90
  LANGUAGES = ["UK.png", "RU.png"]
91
 
 
1
  [AppSettings]
2
+ APP_VERSION = "0.9.2"
3
  CSS_PATH = "app.css"
4
  DEFAULT_LANG_ID = 0
5
 
 
36
  CALCULATE_PRACTICAL_TASK = "Solving practical task"
37
  CLEAR_APP = ["Clear", "Сброс"]
38
  EXAMPLES_APP = ["Examples", "Примеры"]
39
+ EXPORT_PT_SCORES = [
40
+ "Export Big Five personality traits to a CSV file",
41
+ "Экспорт показателей Большой пятерки персональных качеств личности человека в CSV файл"]
42
  EXPORT_PG = "Export ranking professional groups results to a CSV file"
43
  EXPORT_PS = "Export ranking professional skill results to a CSV file"
44
  EXPORT_WT = "Export ranking effective work teams results to a CSV file"
 
88
  POTENTIAL_CANDIDATES = "potential_candidates.csv"
89
  MBTI_JOB = "mbti_job_match.csv"
90
 
91
+ [Dataframes]
92
+ PT_SCORES = [
93
+ [
94
+ "Person ID", "Path", "Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Non-Neuroticism"
95
+ ],
96
+ [
97
+ "Идентификатор", "Имя файла",
98
+ "Открытость к опыту", "Добросовестность", "Экстроверсия", "Доброжелательность", "Эмоциональная стабильность"
99
+ ]
100
+ ]
101
+
102
  [Images]
103
  LANGUAGES = ["UK.png", "RU.png"]
104