Tricky questions and avarage calcs

app.py

@@ -239,83 +239,30 @@ with tab1:
                 data[TRICKY_QUESTIONS_COLUMN_NAME] <= tricky_questions_slider[1])]
 
         # Extract unique provider names from the "Model" column
-        providers = data["Model"].apply(lambda x: x.split('/')[0].lower()).unique()
-        selected_providers = st.multiselect("Model providers", providers, default=providers)
+        providers = data["Model"].apply(
+            lambda x: x.split('/')[0].lower()).unique()
+        selected_providers = st.multiselect(
+            "Model providers", providers, default=providers)
         # Filter data based on selected providers
-        data = data[data["Model"].apply(lambda x: x.split('/')[0].lower()).isin(selected_providers)]
-
-    
-    # Define all possible columns
-    all_columns = {
-        "Model": "Model",
-        "Params": "Params",
-        AVERAGE_COLUMN_NAME: "Average",
-        IMPLICATURES_AVERAGE_COLUMN_NAME: "Impl. Avg",
-        SENTIMENT_COLUMN_NAME: "Sentiment",
-        UNDERSTANDING_COLUMN_NAME: "Understanding",
-        PHRASEOLOGY_COLUMN_NAME: "Phraseology",
-        TRICKY_QUESTIONS_COLUMN_NAME: "Tricky Questions"
-    }
-    
-    # By default, all columns are selected
-    default_columns = list(all_columns.keys())
-    
-    # Use pills to select visible columns in multi-selection mode
-    selected_column_labels = st.pills(
-        label="Visible columns",
-        options=list(all_columns.values()),
-        default=list(all_columns.values()),  # Set all columns as default
-        selection_mode="multi",  # Enable multi-selection mode
-        key="visible_columns_pills"
-    )
-    
-    # Map selected labels back to column names
-    reverse_mapping = {v: k for k, v in all_columns.items()}
-    selected_columns = [reverse_mapping[label] for label in selected_column_labels]
-    
-    # If nothing is selected, show all columns
-    if not selected_columns:
-        selected_columns = default_columns
+        data = data[data["Model"].apply(lambda x: x.split(
+            '/')[0].lower()).isin(selected_providers)]
 
     # Display data
     styled_df_show = style_dataframe(data)
     styled_df_show = styler(styled_df_show)
 
-    # Customize column_config based on selected columns
-    column_config = {}
-    
-    # Set configuration for all columns
-    if "Model" in styled_df_show.columns:
-        column_config["Model"] = st.column_config.TextColumn("Model", help="Model name", width="large") if "Model" in selected_columns else None
-    
-    if "Params" in styled_df_show.columns:
-        column_config["Params"] = st.column_config.NumberColumn("Params [B]") if "Params" in selected_columns else None
-    
-    if AVERAGE_COLUMN_NAME in styled_df_show.columns:
-        column_config[AVERAGE_COLUMN_NAME] = st.column_config.NumberColumn(AVERAGE_COLUMN_NAME) if AVERAGE_COLUMN_NAME in selected_columns else None
-    
-    if IMPLICATURES_AVERAGE_COLUMN_NAME in styled_df_show.columns:
-        column_config[IMPLICATURES_AVERAGE_COLUMN_NAME] = st.column_config.NumberColumn(IMPLICATURES_AVERAGE_COLUMN_NAME) if IMPLICATURES_AVERAGE_COLUMN_NAME in selected_columns else None
-    
-    if RESULTS_COLUMN_NAME in styled_df_show.columns:
-        # Show Results only if Average is selected
-        column_config[RESULTS_COLUMN_NAME] = st.column_config.BarChartColumn(
+    st.data_editor(styled_df_show, column_config={
+        "Model": st.column_config.TextColumn("Model", help="Model name", width="large"),
+        "Params": st.column_config.NumberColumn("Params [B]"),
+        AVERAGE_COLUMN_NAME: st.column_config.NumberColumn(AVERAGE_COLUMN_NAME),
+        RESULTS_COLUMN_NAME: st.column_config.BarChartColumn(
             "Bar chart of results", help="Summary of the results of each task",
-            y_min=0, y_max=5) if AVERAGE_COLUMN_NAME in selected_columns else None
-    
-    if SENTIMENT_COLUMN_NAME in styled_df_show.columns:
-        column_config[SENTIMENT_COLUMN_NAME] = st.column_config.NumberColumn(SENTIMENT_COLUMN_NAME, help='Ability to analyze sentiment') if SENTIMENT_COLUMN_NAME in selected_columns else None
-    
-    if UNDERSTANDING_COLUMN_NAME in styled_df_show.columns:
-        column_config[UNDERSTANDING_COLUMN_NAME] = st.column_config.NumberColumn(UNDERSTANDING_COLUMN_NAME, help='Ability to understand language') if UNDERSTANDING_COLUMN_NAME in selected_columns else None
-    
-    if PHRASEOLOGY_COLUMN_NAME in styled_df_show.columns:
-        column_config[PHRASEOLOGY_COLUMN_NAME] = st.column_config.NumberColumn(PHRASEOLOGY_COLUMN_NAME, help='Ability to understand phraseological compounds') if PHRASEOLOGY_COLUMN_NAME in selected_columns else None
-    
-    if TRICKY_QUESTIONS_COLUMN_NAME in styled_df_show.columns:
-        column_config[TRICKY_QUESTIONS_COLUMN_NAME] = st.column_config.NumberColumn(TRICKY_QUESTIONS_COLUMN_NAME, help='Ability to understand tricky questions') if TRICKY_QUESTIONS_COLUMN_NAME in selected_columns else None
-    
-    st.data_editor(styled_df_show, column_config=column_config, hide_index=True, disabled=True, height=500)
+            y_min=0, y_max=5,),
+        SENTIMENT_COLUMN_NAME: st.column_config.NumberColumn(SENTIMENT_COLUMN_NAME, help='Ability to analyze sentiment'),
+        UNDERSTANDING_COLUMN_NAME: st.column_config.NumberColumn(UNDERSTANDING_COLUMN_NAME, help='Ability to understand language'),
+        PHRASEOLOGY_COLUMN_NAME: st.column_config.NumberColumn(PHRASEOLOGY_COLUMN_NAME, help='Ability to understand phraseological compounds'),
+        TRICKY_QUESTIONS_COLUMN_NAME: st.column_config.NumberColumn(TRICKY_QUESTIONS_COLUMN_NAME, help='Ability to understand tricky questions'),
+    }, hide_index=True, disabled=True, height=500)
 
     # Add selection for models and create a bar chart for selected models using the AVERAGE_COLUMN_NAME, SENTIMENT_COLUMN_NAME, PHRASEOLOGY_COLUMN_NAME, UNDERSTANDING_COLUMN_NAME
     # Add default selection of 3 best models from AVERAGE_COLUMN_NAME and 1 best model with "Bielik" in Model column

requirements.txt

@@ -1,5 +1,5 @@
 pandas
 seaborn
 plotly
-streamlit==1.43
-st_social_media_links
+streamlit
+st_social_media_links