Summary

app/event_handlers/calculate_practical_tasks.py

@@ -193,7 +193,7 @@ def event_handler_calculate_practical_task_blocks(
 
         pt_scores_copy = pt_scores.iloc[:, 1:].copy()
 
-        preprocess_scores_df(pt_scores_copy, "Person ID")
+        preprocess_scores_df(pt_scores_copy, config_data.Dataframes_PT_SCORES[0][0])
 
         b5._professional_match(
             df_files=pt_scores_copy,
@@ -223,7 +223,7 @@ def event_handler_calculate_practical_task_blocks(
 
         df_hidden.reset_index(inplace=True)
 
-        person_id = int(df_hidden.iloc[0]["Person ID"]) - 1
+        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()
@@ -349,7 +349,9 @@ def event_handler_calculate_practical_task_blocks(
 
             df_hidden.reset_index(inplace=True)
 
-            person_id = int(df_hidden.iloc[0]["Person ID"]) - 1
+            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)
 
@@ -417,7 +419,7 @@ def event_handler_calculate_practical_task_blocks(
             by=[dropdown_professional_skills], ascending=False
         )
 
-        person_id = int(df_hidden.iloc[0]["Person ID"]) - 1
+        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)
 
@@ -487,7 +489,7 @@ def event_handler_calculate_practical_task_blocks(
 
         df_hidden.reset_index(inplace=True)
 
-        person_id = int(df_hidden.iloc[0]["Person ID"]) - 1
+        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)
 
@@ -544,7 +546,7 @@ def event_handler_calculate_practical_task_blocks(
 
         pt_scores_copy = pt_scores.iloc[:, 1:].copy()
 
-        preprocess_scores_df(pt_scores_copy, "Person ID")
+        preprocess_scores_df(pt_scores_copy, config_data.Dataframes_PT_SCORES[0][0])
 
         b5._priority_calculation(
             df_files=pt_scores_copy,
@@ -561,7 +563,7 @@ def event_handler_calculate_practical_task_blocks(
 
         df = apply_rounding_and_rename_columns(df_files_priority.iloc[:, 1:])
 
-        preprocess_scores_df(df, "Person ID")
+        preprocess_scores_df(df, config_data.Dataframes_PT_SCORES[0][0])
 
         df_hidden = df.drop(columns=config_data.Settings_SHORT_PROFESSIONAL_SKILLS)
 
@@ -569,7 +571,7 @@ def event_handler_calculate_practical_task_blocks(
 
         df_hidden.reset_index(inplace=True)
 
-        person_id = int(df_hidden.iloc[0]["Person ID"]) - 1
+        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)
 

app/event_handlers/calculate_pt_scores_blocks.py

@@ -11,7 +11,7 @@ import gradio as gr
 from app.oceanai_init import b5
 from app.config import config_data
 from app.description_steps import STEP_2
-from app.utils import read_csv_file, extract_profession_weights, get_language_settings
+from app.utils import get_language_settings
 from app.practical_tasks import supported_practical_tasks
 from app.components import (
     html_message,
@@ -48,7 +48,7 @@ def event_handler_calculate_pt_scores_blocks(language, files, evt_data: gr.Event
                 None,
                 "single",
                 [".csv"],
-                config_data.OtherMessages_EXPORT_PT_SCORES,
+                config_data.OtherMessages_EXPORT_PT_SCORES[lang_id],
                 True,
                 False,
                 False,
@@ -149,18 +149,12 @@ def event_handler_calculate_pt_scores_blocks(language, files, evt_data: gr.Event
     df_files = b5.df_files_.copy()
     df_files.reset_index(inplace=True)
 
-    df_traits_priority_for_professions = read_csv_file(config_data.Links_PROFESSIONS)
-    weights_professions, interactive_professions = extract_profession_weights(
-        df_traits_priority_for_professions,
-        config_data.Settings_DROPDOWN_CANDIDATES[0],
-    )
-
     return (
         html_message(
             config_data.InformationMessages_NOTI_VIDEOS[lang_id], False, False
         ),
         dataframe(
-            headers=df_files.columns.tolist(),
+            headers=(config_data.Dataframes_PT_SCORES[lang_id]),
             values=df_files.values.tolist(),
             visible=True,
         ),
@@ -168,7 +162,7 @@ def event_handler_calculate_pt_scores_blocks(language, files, evt_data: gr.Event
             config_data.Filenames_PT_SCORES,
             "single",
             [".csv"],
-            config_data.OtherMessages_EXPORT_PT_SCORES,
+            config_data.OtherMessages_EXPORT_PT_SCORES[lang_id],
             True,
             False,
             True,

app/event_handlers/clear_blocks.py

@@ -61,7 +61,7 @@ def event_handler_clear_blocks(language):
             None,
             "single",
             [".csv"],
-            config_data.OtherMessages_EXPORT_PT_SCORES,
+            config_data.OtherMessages_EXPORT_PT_SCORES[lang_id],
             True,
             False,
             False,

app/event_handlers/event_handlers.py

@@ -101,7 +101,7 @@ def setup_app_event_handlers(
     # Events
     languages.select(
         fn=event_handler_languages,
-        inputs=[languages, files, video, pt_scores],
+        inputs=[languages, files, video, pt_scores, csv_pt_scores],
         outputs=[
             description,
             step_1,
@@ -117,6 +117,9 @@ def setup_app_event_handlers(
             calculate_pt_scores,
             clear_app,
             notifications,
+            pt_scores,
+            csv_pt_scores,
+            step_2,
         ],
         queue=True,
     )

app/event_handlers/languages.py

@@ -10,7 +10,7 @@ from pathlib import Path
 
 # Importing necessary components for the Gradio app
 from app.description import DESCRIPTIONS
-from app.description_steps import STEP_1
+from app.description_steps import STEP_1, STEP_2
 from app.config import config_data
 from app.components import (
     files_create_ui,
@@ -18,11 +18,12 @@ from app.components import (
     dropdown_create_ui,
     button,
     html_message,
+    dataframe,
 )
 from app.utils import get_language_settings
 
 
-def event_handler_languages(languages, files, video, pt_scores):
+def event_handler_languages(languages, files, video, pt_scores, csv_pt_scores):
     lang_id, choices = get_language_settings(languages)
 
     if not video:
@@ -42,6 +43,29 @@ def event_handler_languages(languages, files, video, pt_scores):
             False if pt_scores.shape[1] >= 7 else True,
         )
 
+    csv_pt_scores = files_create_ui(
+        csv_pt_scores if pt_scores.shape[1] >= 7 else None,
+        "single",
+        [".csv"],
+        config_data.OtherMessages_EXPORT_PT_SCORES[lang_id],
+        True,
+        False,
+        True if pt_scores.shape[1] >= 7 else False,
+        "csv-container",
+    )
+    step_2 = gr.HTML(
+        value=STEP_2[lang_id], visible=True if pt_scores.shape[1] >= 7 else False
+    )
+
+    if pt_scores.shape[1] >= 7:
+        pt_scores = dataframe(
+            headers=(config_data.Dataframes_PT_SCORES[lang_id]),
+            values=pt_scores.values.tolist(),
+            visible=True,
+        )
+    else:
+        pt_scores = dataframe(visible=False)
+
     return (
         gr.Markdown(value=DESCRIPTIONS[lang_id]),
         gr.HTML(value=STEP_1[lang_id]),
@@ -95,4 +119,7 @@ def event_handler_languages(languages, files, video, pt_scores):
             "clear_oceanai",
         ),
         noti_videos,
+        pt_scores,
+        csv_pt_scores,
+        step_2,
     )

app/event_handlers/practical_task_sorted.py

@@ -9,6 +9,7 @@ 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.components import video_create_ui, textbox_create_ui
 
@@ -16,13 +17,20 @@ from app.components import video_create_ui, textbox_create_ui
 def event_handler_practical_task_sorted(
     files, practical_task_sorted, evt_data: gr.SelectData
 ):
-    person_id = int(practical_task_sorted.iloc[evt_data.index[0]]["Person ID"]) - 1
+    person_id = (
+        int(
+            practical_task_sorted.iloc[evt_data.index[0]][
+                config_data.Dataframes_PT_SCORES[0][0]
+            ]
+        )
+        - 1
+    )
 
     if evt_data.index[0] == 0:
         label = "Best"
     else:
         label = ""
-    label += " Person ID"
+    label += " " + config_data.Dataframes_PT_SCORES[0][0]
 
     if Path(files[person_id]).name in video_metadata:
         person_metadata_list = video_metadata[Path(files[person_id]).name]

app/tabs.py

@@ -95,7 +95,9 @@ def app_tab():
         None,
         "single",
         [".csv"],
-        config_data.OtherMessages_EXPORT_PT_SCORES,
+        config_data.OtherMessages_EXPORT_PT_SCORES[
+            config_data.AppSettings_DEFAULT_LANG_ID
+        ],
         True,
         False,
         False,

config.toml

@@ -1,5 +1,5 @@
 [AppSettings]
-APP_VERSION = "0.9.1"
+APP_VERSION = "0.9.2"
 CSS_PATH = "app.css"
 DEFAULT_LANG_ID = 0
 
@@ -36,7 +36,9 @@ CALCULATE_PT_SCORES_ERR = "Personality traits scores have not been calculated. T
 CALCULATE_PRACTICAL_TASK = "Solving practical task"
 CLEAR_APP = ["Clear", "Сброс"]
 EXAMPLES_APP = ["Examples", "Примеры"]
-EXPORT_PT_SCORES = "Export Big Five personality traits to a CSV file"
+EXPORT_PT_SCORES = [
+    "Export Big Five personality traits to a CSV file",
+    "Экспорт показателей Большой пятерки персональных качеств личности человека в CSV файл"]
 EXPORT_PG = "Export ranking professional groups results to a CSV file"
 EXPORT_PS = "Export ranking professional skill results to a CSV file"
 EXPORT_WT = "Export ranking effective work teams results to a CSV file"
@@ -86,6 +88,17 @@ MDA_CATEGORIES = "divice_characteristics_priorities.csv"
 POTENTIAL_CANDIDATES = "potential_candidates.csv"
 MBTI_JOB = "mbti_job_match.csv"
 
+[Dataframes]
+PT_SCORES = [
+    [
+        "Person ID", "Path", "Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Non-Neuroticism"
+    ],
+    [
+        "Идентификатор", "Имя файла",
+        "Открытость к опыту", "Добросовестность", "Экстроверсия", "Доброжелательность", "Эмоциональная стабильность"
+    ]
+]
+
 [Images]
 LANGUAGES = ["UK.png", "RU.png"]