New columns and columns visibiity (#4)

- new columns and columns visibility (b18be2e9f3a75459196d2251528bdc1b569d34c0)
- use pills instead of multiselect (c8c7c168a744634eb05b811e1de5d609edcd09a3)
- streamlit version lock (7a7740d81fe4f5ebd6c3cb53a1427bb08a536408)
- New version of streamlit in README.md (e7f06bc2ec734178e78729d70d61aa4d887af304)

.gitignore

@@ -0,0 +1 @@
+venv

README.md

@@ -4,10 +4,10 @@ emoji: 🧠🦉🇵🇱🖋️
 colorFrom: pink
 colorTo: indigo
 sdk: streamlit
-sdk_version: 1.37.1
+sdk_version: 1.43.2
 app_file: app.py
 pinned: false
 license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -7,43 +7,70 @@ import plotly.express as px
 from st_social_media_links import SocialMediaIcons
 
 
+PARAMS_COLUMN_NAME = "Params"
 RESULTS_COLUMN_NAME = "Results"
 AVERAGE_COLUMN_NAME = "Average"
 SENTIMENT_COLUMN_NAME = "Sentiment"
 UNDERSTANDING_COLUMN_NAME = "Language understanding"
 PHRASEOLOGY_COLUMN_NAME = "Phraseology"
+TRICKY_QUESTIONS_COLUMN_NAME = "Tricky questions"
+IMPLICATURES_AVERAGE_COLUMN_NAME = "Implicatures average"
 
 # Function to load data from JSON file
+
+
 @st.cache_data
 def load_data(file_path):
     with open(file_path, 'r', encoding='utf-8') as file:
         data = json.load(file)
-    return pd.DataFrame(data)
+    df = pd.DataFrame(data)
+    df[AVERAGE_COLUMN_NAME] = df[['Sentiment',
+                                  'Language understanding', 'Phraseology', 'Tricky questions']].mean(axis=1)
+
+    df[IMPLICATURES_AVERAGE_COLUMN_NAME] = df[['Sentiment',
+                                               'Language understanding', 'Phraseology']].mean(axis=1)
+    return df
 
 # Function to style the DataFrame
+
+
 @st.cache_data
 def style_dataframe(df: pd.DataFrame):
-    df[RESULTS_COLUMN_NAME] = df.apply(lambda row: [row[SENTIMENT_COLUMN_NAME], row[UNDERSTANDING_COLUMN_NAME], row[PHRASEOLOGY_COLUMN_NAME]], axis=1)
-    # Insert the new column after the 'Average' column
+    df[RESULTS_COLUMN_NAME] = df.apply(lambda row: [
+                                       row[SENTIMENT_COLUMN_NAME], row[UNDERSTANDING_COLUMN_NAME], row[PHRASEOLOGY_COLUMN_NAME], row[TRICKY_QUESTIONS_COLUMN_NAME]], axis=1)
     cols = list(df.columns)
-    cols.insert(cols.index(AVERAGE_COLUMN_NAME) + 1, cols.pop(cols.index(RESULTS_COLUMN_NAME)))
+
+    # move average column
+    cols.insert(cols.index(PARAMS_COLUMN_NAME) + 1,
+                cols.pop(cols.index(AVERAGE_COLUMN_NAME)))
+
+    # move impicatures average column
+    cols.insert(cols.index(AVERAGE_COLUMN_NAME) + 1,
+                cols.pop(cols.index(IMPLICATURES_AVERAGE_COLUMN_NAME)))
+
+    # move results column
+    cols.insert(cols.index(IMPLICATURES_AVERAGE_COLUMN_NAME) + 1,
+                cols.pop(cols.index(RESULTS_COLUMN_NAME)))
+    # Insert the new column after the 'Average' column
     df = df[cols]
     # Create a color ramp using Seaborn
     return df
 
+
 def styler(df: pd.DataFrame):
     palette = sns.color_palette("RdYlGn", as_cmap=True)
     # Apply reverse color gradient to the "Params" column
-    params_palette = sns.color_palette("RdYlGn_r", as_cmap=True)  # Reversed RdYlGn palette
-    styled_df = df.style.background_gradient(cmap=palette, subset=[AVERAGE_COLUMN_NAME, SENTIMENT_COLUMN_NAME, PHRASEOLOGY_COLUMN_NAME, UNDERSTANDING_COLUMN_NAME]
-                                            ).background_gradient(cmap=params_palette, subset=["Params"]
-                                            ).set_properties(**{'text-align': 'center'}, subset=[AVERAGE_COLUMN_NAME, SENTIMENT_COLUMN_NAME, PHRASEOLOGY_COLUMN_NAME, UNDERSTANDING_COLUMN_NAME]
-                                            ).format("{:.2f}".center(10), subset=[AVERAGE_COLUMN_NAME, SENTIMENT_COLUMN_NAME, PHRASEOLOGY_COLUMN_NAME, UNDERSTANDING_COLUMN_NAME]
-                                            ).format("{:.1f}".center(10), subset=["Params"])
+    params_palette = sns.color_palette(
+        "RdYlGn_r", as_cmap=True)  # Reversed RdYlGn palette
+    styled_df = df.style.background_gradient(cmap=palette, subset=[AVERAGE_COLUMN_NAME, SENTIMENT_COLUMN_NAME, PHRASEOLOGY_COLUMN_NAME, UNDERSTANDING_COLUMN_NAME, TRICKY_QUESTIONS_COLUMN_NAME, IMPLICATURES_AVERAGE_COLUMN_NAME]
+                                             ).background_gradient(cmap=params_palette, subset=["Params"]
+                                                                   ).set_properties(**{'text-align': 'center'}, subset=[AVERAGE_COLUMN_NAME, SENTIMENT_COLUMN_NAME, PHRASEOLOGY_COLUMN_NAME, UNDERSTANDING_COLUMN_NAME, TRICKY_QUESTIONS_COLUMN_NAME, IMPLICATURES_AVERAGE_COLUMN_NAME]
+                                                                                    ).format("{:.2f}".center(10), subset=[AVERAGE_COLUMN_NAME, SENTIMENT_COLUMN_NAME, PHRASEOLOGY_COLUMN_NAME, UNDERSTANDING_COLUMN_NAME, TRICKY_QUESTIONS_COLUMN_NAME, IMPLICATURES_AVERAGE_COLUMN_NAME]
+                                                                                             ).format("{:.1f}".center(10), subset=["Params"])
     return styled_df
 
 
-### Streamlit app
+# Streamlit app
 st.set_page_config(layout="wide")
 
 st.markdown("""
@@ -58,7 +85,7 @@ st.markdown("""
         </style>
         """, unsafe_allow_html=True)
 
-### Prepare layout
+# Prepare layout
 
 st.markdown("""
 <style>
@@ -87,7 +114,7 @@ st.markdown("""
 # ----------------------------------------------------------
 st.markdown("""<br>""", unsafe_allow_html=True)
 
-### Row: 1 --> Title + links to SpeakLeash.org website / GitHub / X (Twitter)
+# Row: 1 --> Title + links to SpeakLeash.org website / GitHub / X (Twitter)
 social_media_links = [
     "https://discord.com/invite/ZJwCMrxwT7",
     "https://github.com/speakleash",
@@ -110,7 +137,8 @@ social_media_links_colors = [
     links_color
 ]
 
-social_media_icons = SocialMediaIcons(social_media_links, social_media_links_colors)
+social_media_icons = SocialMediaIcons(
+    social_media_links, social_media_links_colors)
 social_media_icons.render(justify_content='right')
 
 st.markdown("""
@@ -140,23 +168,24 @@ with tab1:
 
     # Prepare data
     data = load_data('data.json')
-    
+
     data['Params'] = pd.to_numeric(
-    data['Params'].str.replace('B', ''),
-    errors='coerce'
-)
+        data['Params'].str.replace('B', ''),
+        errors='coerce'
+    )
     data = data.sort_values(by=AVERAGE_COLUMN_NAME, ascending=False)
 
     # Closing filters in a expander
     with st.expander("Filtering benchmark data", icon='🔍'):
         # Filtering data, e.g. slider for params, average score, etc.
-        col_filter_params, col_filter_average, col_filter_sentiment, col_filter_understanding, col_filter_phraseology = st.columns(5, gap='medium')
-    
+        col_filter_params, col_filter_average, col_filter_implicatures_average, col_filter_sentiment, col_filter_understanding, col_filter_phraseology, col_filter_tricky_questions = st.columns(
+            7, gap='medium')
+
         with col_filter_params:
             max_params = data['Params'].max(skipna=True)
             if pd.isna(max_params):
                 max_params = 0.0
-    
+
             params_slider = st.slider(
                 "Models Size [B]",
                 min_value=0.0,
@@ -174,20 +203,40 @@ with tab1:
             ]
 
         with col_filter_average:
-            average_slider = st.slider("Average score", step=0.1, min_value=0.0, max_value=5.0, value=(0.0, 5.0))
-            data = data[(data[AVERAGE_COLUMN_NAME] >= average_slider[0]) & (data[AVERAGE_COLUMN_NAME] <= average_slider[1])]
+            average_slider = st.slider(
+                "Average score", step=0.1, min_value=0.0, max_value=5.0, value=(0.0, 5.0))
+            data = data[(data[AVERAGE_COLUMN_NAME] >= average_slider[0]) & (
+                data[AVERAGE_COLUMN_NAME] <= average_slider[1])]
+
+        with col_filter_implicatures_average:
+            implicatures_average_slider = st.slider(
+                "Implicatures average", step=0.1, min_value=0.0, max_value=5.0, value=(0.0, 5.0))
+            data = data[(data[IMPLICATURES_AVERAGE_COLUMN_NAME] >= implicatures_average_slider[0]) & (
+                data[IMPLICATURES_AVERAGE_COLUMN_NAME] <= implicatures_average_slider[1])]
 
         with col_filter_sentiment:
-            sentiment_slider = st.slider("Sentiment score", step=0.1, min_value=0.0, max_value=5.0, value=(0.0, 5.0))
-            data = data[(data[SENTIMENT_COLUMN_NAME] >= sentiment_slider[0]) & (data[SENTIMENT_COLUMN_NAME] <= sentiment_slider[1])]
+            sentiment_slider = st.slider(
+                "Sentiment score", step=0.1, min_value=0.0, max_value=5.0, value=(0.0, 5.0))
+            data = data[(data[SENTIMENT_COLUMN_NAME] >= sentiment_slider[0]) & (
+                data[SENTIMENT_COLUMN_NAME] <= sentiment_slider[1])]
 
         with col_filter_understanding:
-            understanding_slider = st.slider("Understanding score", step=0.1, min_value=0.0, max_value=5.0, value=(0.0, 5.0))
-            data = data[(data[UNDERSTANDING_COLUMN_NAME] >= understanding_slider[0]) & (data[UNDERSTANDING_COLUMN_NAME] <= understanding_slider[1])]
+            understanding_slider = st.slider(
+                "Understanding score", step=0.1, min_value=0.0, max_value=5.0, value=(0.0, 5.0))
+            data = data[(data[UNDERSTANDING_COLUMN_NAME] >= understanding_slider[0]) & (
+                data[UNDERSTANDING_COLUMN_NAME] <= understanding_slider[1])]
 
         with col_filter_phraseology:
-            phraseology_slider = st.slider("Phraseology score", step=0.1, min_value=0.0, max_value=5.0, value=(0.0, 5.0))
-            data = data[(data[PHRASEOLOGY_COLUMN_NAME] >= phraseology_slider[0]) & (data[PHRASEOLOGY_COLUMN_NAME] <= phraseology_slider[1])]
+            phraseology_slider = st.slider(
+                "Phraseology score", step=0.1, min_value=0.0, max_value=5.0, value=(0.0, 5.0))
+            data = data[(data[PHRASEOLOGY_COLUMN_NAME] >= phraseology_slider[0]) & (
+                data[PHRASEOLOGY_COLUMN_NAME] <= phraseology_slider[1])]
+
+        with col_filter_tricky_questions:
+            tricky_questions_slider = st.slider(
+                "Tricky questions score", step=0.1, min_value=0.0, max_value=5.0, value=(0.0, 5.0))
+            data = data[(data[TRICKY_QUESTIONS_COLUMN_NAME] >= tricky_questions_slider[0]) & (
+                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()
@@ -195,31 +244,92 @@ with tab1:
         # Filter data based on selected providers
         data = data[data["Model"].apply(lambda x: x.split('/')[0].lower()).isin(selected_providers)]
 
-    ### Display data
+    
+    # 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
+
+    # Display data
     styled_df_show = style_dataframe(data)
     styled_df_show = styler(styled_df_show)
 
-    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(
+    # 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(
             "Bar chart of results", help="Summary of the results of each task",
-            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'),
-        }, hide_index=True, disabled=True, height=500)
+            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)
 
     # 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
-    default_models = list(data.sort_values(AVERAGE_COLUMN_NAME, ascending=False)['Model'].head(3))
-    bielik_model = data[data['Model'].str.contains('Bielik')].sort_values(AVERAGE_COLUMN_NAME, ascending=False)['Model'].iloc[0]
+    default_models = list(data.sort_values(
+        AVERAGE_COLUMN_NAME, ascending=False)['Model'].head(3))
+    bielik_model = data[data['Model'].str.contains('Bielik')].sort_values(
+        AVERAGE_COLUMN_NAME, ascending=False)['Model'].iloc[0]
     if bielik_model not in default_models:
         default_models.append(bielik_model)
-    selected_models = st.multiselect("Select models to compare", data["Model"].unique(), default=default_models)
+    selected_models = st.multiselect(
+        "Select models to compare", data["Model"].unique(), default=default_models)
     selected_data = data[data["Model"].isin(selected_models)]
-    categories = [AVERAGE_COLUMN_NAME, SENTIMENT_COLUMN_NAME, PHRASEOLOGY_COLUMN_NAME, UNDERSTANDING_COLUMN_NAME]
+    categories = [AVERAGE_COLUMN_NAME, SENTIMENT_COLUMN_NAME,
+                  PHRASEOLOGY_COLUMN_NAME, UNDERSTANDING_COLUMN_NAME, TRICKY_QUESTIONS_COLUMN_NAME]
 
     if selected_models:
         # Kolorki do wyboru:
@@ -228,7 +338,8 @@ with tab1:
 
         fig_bars = go.Figure()
         for model, color in zip(selected_models, colors):
-            values = selected_data[selected_data['Model'] == model][categories].values.flatten().tolist()
+            values = selected_data[selected_data['Model'] ==
+                                   model][categories].values.flatten().tolist()
             fig_bars.add_trace(go.Bar(
                 x=categories,
                 y=values,
@@ -239,7 +350,8 @@ with tab1:
         # Update layout to use a custom color scale
         fig_bars.update_layout(
             showlegend=True,
-            legend=dict(orientation="h", yanchor="top", y=-0.3, xanchor="center", x=0.5),
+            legend=dict(orientation="h", yanchor="top",
+                        y=-0.3, xanchor="center", x=0.5),
             title="Comparison of Selected Models",
             yaxis_title="Score",
             template="plotly_dark"
@@ -248,7 +360,7 @@ with tab1:
         st.plotly_chart(fig_bars)
 
 
-### Zakładka 2 --> Opis
+# Zakładka 2 --> Opis
 with tab2:
     st.markdown("""
 ### <span style='text-decoration: #FDA428 wavy underline;'>**Cause of Creation**</span>
@@ -321,9 +433,10 @@ st.markdown("""
 - [Remigiusz Kinas](https://www.linkedin.com/in/remigiusz-kinas/) - methodological support
 - [Krzysztof Wróbel](https://www.linkedin.com/in/wrobelkrzysztof/) - engineering, methodological support
 - [Szymon Baczyński](https://www.linkedin.com/in/szymon-baczynski/) - front-end / streamlit assistant
+- [Artur Słomowski](https://www.linkedin.com/in/arturslomowski/) - front-end / streamlit assistant
 - [Maria Filipkowska](https://www.linkedin.com/in/maria-filipkowska/) - writing text, linguistic support
 """)
 
 st.divider()
 
-# Run the app with `streamlit run your_script.py`
+# Run the app with `streamlit run your_script.py`

data.json

@@ -2,497 +2,497 @@
     {
         "Model": "mistralai/Mistral-Large-Instruct-2407",
         "Params": "123B",
-        "Average": 4.03025641025641,
         "Sentiment": 4.230769230769231,
         "Language understanding": 4.0,
-        "Phraseology": 3.86
+        "Phraseology": 3.86,
+        "Tricky questions": 3.9
     },
     {
         "Model": "alpindale/WizardLM-2-8x22B",
         "Params": "141B",
-        "Average": 3.9133760683760683,
         "Sentiment": 3.7051282051282053,
         "Language understanding": 3.815,
-        "Phraseology": 4.22
+        "Phraseology": 4.22,
+        "Tricky questions": 3.9
     },
     {
         "Model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
         "Params": "70.6B",
-        "Average": 3.828974358974359,
         "Sentiment": 4.326923076923077,
         "Language understanding": 3.91,
-        "Phraseology": 3.25
+        "Phraseology": 3.25,
+        "Tricky questions": 3.9
     },
     {
         "Model": "meta-llama/Meta-Llama-3-70B-Instruct",
         "Params": "70.6B",
-        "Average": 3.806538461538462,
         "Sentiment": 4.134615384615385,
         "Language understanding": 3.82,
-        "Phraseology": 3.465
+        "Phraseology": 3.465,
+        "Tricky questions": 3.9
     },
     {
         "Model": "speakleash/Bielik-11B-v2.3-Instruct",
         "Params": "11.2B",
-        "Average": 3.7697863247863252,
         "Sentiment": 3.9743589743589745,
         "Language understanding": 3.785,
-        "Phraseology": 3.55
+        "Phraseology": 3.55,
+        "Tricky questions": 3.9
     },
     {
         "Model": "mistralai/Mixtral-8x22B-Instruct-v0.1",
         "Params": "141B",
-        "Average": 3.6690170940170943,
         "Sentiment": 3.782051282051282,
         "Language understanding": 3.675,
-        "Phraseology": 3.55
+        "Phraseology": 3.55,
+        "Tricky questions": 3.9
     },
     {
         "Model": "speakleash/Bielik-11B-v2.1-Instruct",
         "Params": "11.2B",
-        "Average": 3.6583760683760684,
         "Sentiment": 3.9551282051282053,
         "Language understanding": 3.915,
-        "Phraseology": 3.105
+        "Phraseology": 3.105,
+        "Tricky questions": 3.9
     },
     {
         "Model": "Qwen/Qwen2-72B-Instruct",
         "Params": "72.7B",
-        "Average": 3.6442735042735044,
         "Sentiment": 3.7628205128205128,
         "Language understanding": 3.89,
-        "Phraseology": 3.28
+        "Phraseology": 3.28,
+        "Tricky questions": 3.9
     },
     {
         "Model": "speakleash/Bielik-11B-v2.0-Instruct",
         "Params": "11.2B",
-        "Average": 3.614786324786325,
         "Sentiment": 3.9743589743589745,
         "Language understanding": 3.745,
-        "Phraseology": 3.125
+        "Phraseology": 3.125,
+        "Tricky questions": 3.9
     },
     {
         "Model": "speakleash/Bielik-11B-v2.2-Instruct",
         "Params": "11.2B",
-        "Average": 3.565982905982906,
         "Sentiment": 3.717948717948718,
         "Language understanding": 3.73,
-        "Phraseology": 3.25
+        "Phraseology": 3.25,
+        "Tricky questions": 3.9
     },
     {
         "Model": "Qwen/Qwen1.5-72B-Chat",
         "Params": "72.3B",
-        "Average": 3.3214529914529916,
         "Sentiment": 3.4743589743589745,
         "Language understanding": 3.515,
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-]
+]

requirements.txt

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