v0.10.9

app/event_handlers/calculate_practical_tasks.py

@@ -576,12 +576,8 @@ def event_handler_calculate_practical_task_blocks(
     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,
+            df_files=pt_scores.iloc[:, 1:],
             correlation_coefficients=df_professional_skills,
             threshold=threshold_professional_skills,
             out=False,
@@ -611,13 +607,13 @@ def event_handler_calculate_practical_task_blocks(
             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.reset_index(drop=True, inplace=True)
 
         df_hidden.to_csv(config_data.Filenames_PT_SKILLS_SCORES)
 
@@ -666,83 +662,37 @@ def event_handler_calculate_practical_task_blocks(
     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_correlation_coefficients = read_csv_file(
+            config_data.Links_FINDING_COLLEAGUE, ["ID"]
+        )
 
-            df_hidden = df.drop(columns=config_data.Settings_SHORT_PROFESSIONAL_SKILLS)
+        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_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 = apply_rounding_and_rename_columns(b5.df_files_colleague_)
 
-            df_hidden = df[["Path", "Personality Type", "Match"]]
+        df_hidden = df.drop(columns=config_data.Settings_SHORT_PROFESSIONAL_SKILLS)
 
-            df_hidden.to_csv(config_data.Filenames_COLLEAGUE_RANKING)
+        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
-                        if practical_subtasks.lower()
-                        != "finding a suitable colleague by personality types"
-                        else 1
-                    )
-                ][config_data.Dataframes_PT_SCORES[0][0]]
-            )
-            - 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)
 
@@ -838,7 +788,7 @@ def event_handler_calculate_practical_task_blocks(
 
             df_hidden = df_hidden.reset_index()
 
-            df_hidden.columns = ["Priority", "Category"]
+            df_hidden.columns = ["Category", "Priority"]
 
         df_hidden.to_csv(consumer_preferences(practical_subtasks))
 

app/event_handlers/practical_subtasks.py

@@ -219,8 +219,6 @@ def event_handler_practical_subtasks(
     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"
     ):
         return (
             practical_subtasks_selected,
@@ -299,12 +297,7 @@ def event_handler_practical_subtasks(
                 minimum=0.0,
                 maximum=1.0,
                 step=0.01,
-                label=(
-                    config_data.Labels_THRESHOLD_TARGET_SCORE_LABEL
-                    if practical_subtasks.lower()
-                    == "finding a suitable colleague by personality types"
-                    else config_data.Labels_EQUAL_COEFFICIENT_LABEL
-                ),
+                label=config_data.Labels_EQUAL_COEFFICIENT_LABEL,
                 info=config_data.InformationMessages_VALUE_FROM_TO_INFO.format(0, 1.0),
                 show_label=True,
                 interactive=True,

app/event_handlers/practical_task_sorted.py

@@ -37,13 +37,7 @@ def event_handler_practical_task_sorted(
         label = ""
     label += " " + config_data.Dataframes_PT_SCORES[0][0]
 
-    try:
-        is_filename = Path(files[person_id]).name in video_metadata
-    except IndexError:
-        is_filename = False
-        person_id = 0
-
-    if is_filename:
+    if Path(files[person_id]).name in video_metadata:
         person_metadata_list = video_metadata[Path(files[person_id]).name]
 
         person_metadata = (

app/utils.py

@@ -7,7 +7,7 @@ License: MIT License
 
 import pandas as pd
 import subprocess
-import os
+from pathlib import Path
 
 # Importing necessary components for the Gradio app
 from app.config import config_data
@@ -49,7 +49,7 @@ def read_csv_file(file_path, drop_columns=[]):
 
 def round_numeric_values(x):
     if isinstance(x, (int, float)):
-        return round(x, 4)
+        return round(x, 3)
 
     return x
 
@@ -87,11 +87,26 @@ def extract_profession_weights(df, dropdown_candidates):
 
     return weights_professions, interactive_professions
 
-def webm2mp4(input_file: str) -> str:
-    path_save = os.path.splitext(input_file)[0] + ".mp4"
 
-    ff_video = "ffmpeg -i {} -c:v libx264 -c:a aac -strict experimental {}".format(input_file, path_save)
-
-    subprocess.run(ff_video, shell=True, check=True, capture_output=True, text=True)
-
-    return path_save
+def webm2mp4(input_file):
+    input_path = Path(input_file)
+    output_path = input_path.with_suffix(".mp4")
+
+    if not output_path.exists():
+        subprocess.run(
+            [
+                "ffmpeg",
+                "-i",
+                str(input_path),
+                "-c:v",
+                "copy",
+                "-c:a",
+                "aac",
+                "-strict",
+                "experimental",
+                str(output_path),
+            ],
+            check=True,
+        )
+
+    return str(output_path)

config.toml

@@ -1,5 +1,5 @@
 [AppSettings]
-APP_VERSION = "0.11.1"
+APP_VERSION = "0.10.8"
 SERVER_NAME = "127.0.0.1"
 PORT = 7860
 CSS_PATH = "app.css"
@@ -72,7 +72,6 @@ TARGET_SCORE_EXT_LABEL = "Extraversion target score"
 TARGET_SCORE_AGR_LABEL = "Agreeableness target score"
 TARGET_SCORE_NNEU_LABEL = "Non-Neuroticism target score"
 EQUAL_COEFFICIENT_LABEL = "Equal coefficient"
-THRESHOLD_TARGET_SCORE_LABEL = "Polarity traits threshold"
 NUMBER_PRIORITY_LABEL = "Priority number"
 NUMBER_IMPORTANCE_TRAITS_LABEL = "Importance traits number"
 NUMBER_IMPORTANCE_OPE_LABEL = "Openness weight"
@@ -160,10 +159,10 @@ TARGET_SCORES = [0.527886, 0.522337, 0.458468, 0.51761, 0.444649]
 0_100 = [0, 100]
 
 [Links]
-PROFESSIONAL_SKILLS = "https://drive.usercontent.google.com/download?id=1N7urgtAtkLEZ8z6asjsX4k_1jYQ0MWPd&export=download&authuser=2&confirm=t&uuid=b34efecb-e947-4436-b70a-35ccfed10da2&at=AN8xHorB8OfmTKKEJc3QAcGfn3_3:1758045333049"
-FINDING_COLLEAGUE = "https://drive.usercontent.google.com/download?id=19U8ZwxY-AxbhjyUgrKvcWEh9r3OhYV6W&export=download&authuser=2&confirm=t&uuid=f2b385d4-fc70-4466-bebe-e6986f78e703&at=AN8xHooMBZKlyfmuYWAdW8FS_jSr:1758045170622"
-CAR_CHARACTERISTICS = "https://drive.usercontent.google.com/download?id=1jIyM6ILWJ7J6MUG2EZU7F5lD0GjHvkS3&export=download&authuser=2&confirm=t&uuid=f7ed6f20-0041-4399-8199-0bec64b5ad09&at=AN8xHor_zNigUgyUOsZ8W01DqH1b:1758045115385"
-MDA_CATEGORIES = "https://drive.usercontent.google.com/download?id=1h0AR7w5KIrcEY-LspiKv13TIh66exRpA&export=download&authuser=2&confirm=t&uuid=b0c3a3a6-d85a-40b8-af33-4688fafae7fe&at=AN8xHopPoRZdLbt2wzODvv4p8iQO:1758045265467"
-CLOTHING_SC = "https://drive.usercontent.google.com/download?id=14Iw-_iNh2eSsKpeATbFKPo8wtTSZB1bi&export=download&authuser=2&confirm=t&uuid=fe5294e7-15db-4e29-9bfc-3d6ac2c1c6ec&at=AN8xHopRki9a44phSRHJhE1egreE:1758045197661"
-PROFESSIONS = "https://drive.usercontent.google.com/download?id=1cPSPpXm4cNNzfa1l-4wAeRlClvUA_NK2&export=download&authuser=2&confirm=t&uuid=73597028-1857-4d1b-8e09-4bf14c70c11e&at=AN8xHoqk1hMUcalNAz63KOUtnaJ6:1758045310738"
-MBTI = "https://drive.usercontent.google.com/download?id=10XpeLWe7Y0nq_6rg38Vsdu81P7_-o12f&export=download&authuser=2&confirm=t&uuid=a1eae137-0460-4f05-8b73-08cfeb77de1b&at=AN8xHoqX2FrkXF2Hl93CGu-MivCS:1758045233478"
+PROFESSIONAL_SKILLS = "https://download.sberdisk.ru/download/file/478678231?token=0qiZwliLtHWWYMv&filename=professional_skills.csv"
+FINDING_COLLEAGUE = "https://download.sberdisk.ru/download/file/478675819?token=LuB7L1QsEY0UuSs&filename=colleague_ranking.csv"
+CAR_CHARACTERISTICS = "https://download.sberdisk.ru/download/file/478675818?token=EjfLMqOeK8cfnOu&filename=auto_characteristics.csv"
+MDA_CATEGORIES = "https://download.sberdisk.ru/download/file/478676690?token=7KcAxPqMpWiYQnx&filename=divice_characteristics.csv"
+CLOTHING_SC = "https://download.sberdisk.ru/download/file/493644097?token=KGtSGMxjZtWXmBz&filename=df_%D1%81lothing_style_correlation.csv"
+PROFESSIONS = "https://download.sberdisk.ru/download/file/478675798?token=fF5fNZVpthQlEV0&filename=traits_priority_for_professions.csv"
+MBTI = "https://download.sberdisk.ru/download/file/493644095?token=EX7hFxNJhMoLumI&filename=df_mbti_correlation.csv"

practical_tasks_en.yaml

@@ -7,7 +7,6 @@
   subtasks:
     - "Finding a suitable junior colleague"
     - "Finding a suitable senior colleague"
-    - "Finding a suitable colleague by personality types"
 - task: "Predicting consumer preferences for industrial goods"
   subtasks:
     - "Car characteristics"

practical_tasks_ru.yaml

@@ -7,7 +7,6 @@
   subtasks:
     - "Поиск подходящего младшего коллеги"
     - "Поиск подходящего старшего коллеги"
-    - "Поиск подходящего коллеги по типам личности"
 - task: "Прогнозирование потребительских предпочтений в отношении промышленных товаров"
   subtasks:
     - "Характеристики автомобиля"

requirements.txt

@@ -1,7 +1,7 @@
 gradio==5.8.0
 PyYAML==6.0.2
 toml==0.10.2
-oceanai==1.0.0a48
+oceanai==1.0.0a46
 torch==2.2.2
 psutil==6.1.0
 beautifulsoup4==4.12.3