v0.10.2 (#24)

- Summary (f6125144e9df803bac37023b6854f03c30d62a31)
- Merge branch 'main' of https://huggingface.co/spaces/ElenaRyumina/OCEANAI (2f050c06a94348ba17660fdf0dd257a7a30f2f9a)
- Summary (8f52d543a1de5a77b59164e6f4f721696fc64299)
- Merge branch 'main' of https://huggingface.co/spaces/ElenaRyumina/OCEANAI (fffa0062f0da04563ce8cd5b8c7e70e3ae783833)
- Summary (1717e06c53138f3eaf52e38f984035d5fbbceac5)


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

app.py

@@ -93,6 +93,6 @@ if __name__ == "__main__":
 
     create_gradio_app().queue(api_open=False).launch(
         share=False,
-        server_name=None,
-        server_port=None,
+        server_name=config_data.AppSettings_SERVER_NAME,
+        server_port=config_data.AppSettings_PORT,
     )

app/event_handlers/calculate_practical_tasks.py

@@ -6,9 +6,11 @@ License: MIT License
 """
 
 from app.oceanai_init import b5
+import pandas as pd
 import re
 import gradio as gr
 from pathlib import Path
+from bs4 import BeautifulSoup
 
 # Importing necessary components for the Gradio app
 from app.config import config_data
@@ -204,44 +206,106 @@ def event_handler_calculate_practical_task_blocks(
 
         preprocess_scores_df(pt_scores_copy, config_data.Dataframes_PT_SCORES[0][0])
 
-        b5._professional_match(
-            df_files=pt_scores_copy,
-            correlation_coefficients=df_correlation_coefficients,
-            personality_type=remove_parentheses(dropdown_mbti),
-            threshold=threshold_mbti,
-            out=False,
-        )
+        if type_modes == config_data.Settings_TYPE_MODES[0]:
+            b5._professional_match(
+                df_files=pt_scores_copy,
+                correlation_coefficients=df_correlation_coefficients,
+                personality_type=remove_parentheses(dropdown_mbti),
+                threshold=threshold_mbti,
+                out=False,
+            )
 
-        df = apply_rounding_and_rename_columns(b5.df_files_MBTI_job_match_)
+            df = apply_rounding_and_rename_columns(b5.df_files_MBTI_job_match_)
 
-        df_hidden = df.drop(
-            columns=config_data.Settings_SHORT_PROFESSIONAL_SKILLS
-            + config_data.Settings_DROPDOWN_MBTI_DEL_COLS
-        )
+            df_hidden = df.drop(
+                columns=config_data.Settings_SHORT_PROFESSIONAL_SKILLS
+                + config_data.Settings_DROPDOWN_MBTI_DEL_COLS
+            )
 
-        df_hidden.rename(
-            columns={
-                "Path": "Filename",
-                "MBTI": "Personality Type",
-                "MBTI_Score": "Personality Type Score",
-            },
-            inplace=True,
-        )
+            df_hidden.rename(
+                columns={
+                    "Path": "Filename",
+                    "MBTI": "Personality Type",
+                    "MBTI_Score": "Personality Type Score",
+                },
+                inplace=True,
+            )
 
-        df_hidden.to_csv(config_data.Filenames_MBTI_JOB)
+            df_copy = df_hidden.copy()
+            df_copy["Personality Type"] = df_copy["Personality Type"].apply(
+                lambda x: "".join(BeautifulSoup(x, "html.parser").stripped_strings)
+            )
+            df_copy.to_csv(config_data.Filenames_MBTI_JOB, index=False)
 
-        df_hidden.reset_index(inplace=True)
+            df_hidden.reset_index(inplace=True)
 
-        person_id = int(df_hidden.iloc[0][config_data.Dataframes_PT_SCORES[0][0]]) - 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()
+            short_mbti = extract_text_in_parentheses(dropdown_mbti)
+            mbti_values = df_hidden["Personality Type"].tolist()
 
-        df_hidden["Personality Type"] = [
-            compare_strings(short_mbti, mbti, False) for mbti in mbti_values
-        ]
+            df_hidden["Personality Type"] = [
+                compare_strings(short_mbti, mbti, False) for mbti in mbti_values
+            ]
 
-        person_metadata = create_person_metadata(person_id, files, video_metadata)
+            person_metadata = create_person_metadata(person_id, files, video_metadata)
+        elif type_modes == config_data.Settings_TYPE_MODES[1]:
+            all_hidden_dfs = []
+
+            for dropdown_mbti in config_data.Settings_DROPDOWN_MBTI:
+                b5._professional_match(
+                    df_files=pt_scores_copy,
+                    correlation_coefficients=df_correlation_coefficients,
+                    personality_type=remove_parentheses(dropdown_mbti),
+                    threshold=threshold_mbti,
+                    out=False,
+                )
+
+                df = apply_rounding_and_rename_columns(b5.df_files_MBTI_job_match_)
+
+                df_hidden = df.drop(
+                    columns=config_data.Settings_SHORT_PROFESSIONAL_SKILLS
+                    + config_data.Settings_DROPDOWN_MBTI_DEL_COLS_WEBCAM
+                )
+
+                df_hidden.insert(0, "Personality Type (Dropdown)", dropdown_mbti)
+
+                df_hidden.rename(
+                    columns={
+                        "MBTI": "Personality Type",
+                        "MBTI_Score": "Personality Type Score",
+                    },
+                    inplace=True,
+                )
+
+                short_mbti = extract_text_in_parentheses(dropdown_mbti)
+                mbti_values = df_hidden["Personality Type"].tolist()
+
+                df_hidden["Personality Type"] = [
+                    compare_strings(short_mbti, mbti, False) for mbti in mbti_values
+                ]
+
+                all_hidden_dfs.append(df_hidden)
+
+            df_hidden = pd.concat(all_hidden_dfs, ignore_index=True)
+
+            df_hidden = df_hidden.sort_values(
+                by="Personality Type Score", ascending=False
+            )
+
+            df_hidden.reset_index(drop=True, inplace=True)
+
+            df_copy = df_hidden.copy()
+            df_copy["Personality Type"] = df_copy["Personality Type"].apply(
+                lambda x: "".join(BeautifulSoup(x, "html.parser").stripped_strings)
+            )
+            df_copy.to_csv(config_data.Filenames_MBTI_JOB, index=False)
+
+            person_id = 0
+
+            person_metadata = create_person_metadata(person_id, files, video_metadata)
 
         existing_tuple = (
             gr.Row(visible=True),

app/event_handlers/event_handlers.py

@@ -539,7 +539,7 @@ def setup_app_event_handlers(
     )
     practical_task_sorted.select(
         event_handler_practical_task_sorted,
-        [files, practical_task_sorted],
+        [type_modes, files, video, practical_task_sorted],
         [
             video_sorted_column,
             video_sorted,

app/event_handlers/practical_task_sorted.py

@@ -15,16 +15,21 @@ from app.components import video_create_ui, textbox_create_ui
 
 
 def event_handler_practical_task_sorted(
-    files, practical_task_sorted, evt_data: gr.SelectData
+    type_modes, files, video, practical_task_sorted, evt_data: gr.SelectData
 ):
-    person_id = (
-        int(
-            practical_task_sorted.iloc[evt_data.index[0]][
-                config_data.Dataframes_PT_SCORES[0][0]
-            ]
+    if type_modes == config_data.Settings_TYPE_MODES[0]:
+        person_id = (
+            int(
+                practical_task_sorted.iloc[evt_data.index[0]][
+                    config_data.Dataframes_PT_SCORES[0][0]
+                ]
+            )
+            - 1
         )
-        - 1
-    )
+    elif type_modes == config_data.Settings_TYPE_MODES[1]:
+        files = [video]
+
+        person_id = 0
 
     if evt_data.index[0] == 0:
         label = "Best"

config.toml

@@ -1,5 +1,5 @@
 [AppSettings]
-APP_VERSION = "0.10.1"
+APP_VERSION = "0.10.2"
 SERVER_NAME = "127.0.0.1"
 PORT = 7860
 CSS_PATH = "app.css"
@@ -148,6 +148,7 @@ DROPDOWN_MBTI = [
     "The Commander (ENTJ): Construction Supervisor, Health Services Administrator, Financial Accountant, Auditor, Lawyer, School Principal, Chemical Engineer, Database Manager, etc.",
 ]
 DROPDOWN_MBTI_DEL_COLS = ["EI", "SN", "TF", "JP", "Match"]
+DROPDOWN_MBTI_DEL_COLS_WEBCAM = ["EI", "SN", "TF", "JP", "Match", "Path"]
 SHOW_VIDEO_METADATA = true
 SUPPORTED_VIDEO_EXT = ["mp4", "mov", "avi", "flv"]
 TYPE_MODES = ["Files", "Web"]

requirements.txt

@@ -4,3 +4,4 @@ toml==0.10.2
 oceanai==1.0.0a46
 torch==2.2.2
 psutil==6.1.0
+beautifulsoup4==4.12.3