Update visuals

app.py

@@ -6,6 +6,7 @@ import plotly.graph_objects as go
 import plotly.express as px
 from st_social_media_links import SocialMediaIcons
 
+
 AVERAGE_COLUMN_NAME = "Average"
 SENTIMENT_COLUMN_NAME = "Sentiment"
 RESULTS_COLUMN_NAME = "Results"
@@ -152,12 +153,21 @@ social_media_links = [
     "https://www.facebook.com/Speakleash/"
 ]
 
+light_orange = "#FDA428"
+dark_orange = "#A85E00"
+white_color = "#FFFFFF"
+black_color = "#000000"
+links_color = white_color
+
+# if theme.get('background_color') == white_color:
+#     links_color = black_color
+
 social_media_links_colors = [
-    "#FFFFFF",
-    "#FFFFFF",
-    "#FFFFFF",
-    "#FFFFFF",
-    "#FFFFFF"
+    dark_orange,
+    dark_orange,
+    dark_orange,
+    dark_orange,
+    dark_orange
 ]
 
 social_media_icons = SocialMediaIcons(social_media_links, social_media_links_colors)
@@ -166,6 +176,7 @@ social_media_icons.render(justify_content='right')
 # Add logo, title, and subheader in a flexible container with equal spacing
 st.markdown("""
     <div class="header-container">
+        <br>
         <img src="https://speakleash.org/wp-content/uploads/2023/09/SpeakLeash_logo.svg" alt="SpeakLeash Logo">
         <hr>
         <div class="title-container">
@@ -189,55 +200,34 @@ with tab1:
     styled_df_show = styler(styled_df_show)
 
     st.data_editor(styled_df_show, column_config={
-                    "Params": st.column_config.NumberColumn("Params [B]", format="%.1f"),
-                    AVERAGE_COLUMN_NAME: st.column_config.NumberColumn(AVERAGE_COLUMN_NAME),
-                    RESULTS_COLUMN_NAME: st.column_config.BarChartColumn(
-                        RESULTS_COLUMN_NAME, 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'),
-                    PHRASEOLOGY_COLUMN_NAME: st.column_config.NumberColumn(PHRASEOLOGY_COLUMN_NAME, help='Ability to understand phraseological compounds'),
-                    UNDERSTANDING_COLUMN_NAME: st.column_config.NumberColumn(UNDERSTANDING_COLUMN_NAME, help='Ability to understand language'),
-                    }, hide_index=True, disabled=True, height=500)
-
-    st.divider()
+        "Model": st.column_config.TextColumn("Model", help="Model name", width="large"),
+        "Params": st.column_config.NumberColumn("Params [B]", format="%.1f"),
+        AVERAGE_COLUMN_NAME: st.column_config.NumberColumn(AVERAGE_COLUMN_NAME),
+        RESULTS_COLUMN_NAME: st.column_config.BarChartColumn(
+            RESULTS_COLUMN_NAME, 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'),
+        PHRASEOLOGY_COLUMN_NAME: st.column_config.NumberColumn(PHRASEOLOGY_COLUMN_NAME, help='Ability to understand phraseological compounds'),
+        UNDERSTANDING_COLUMN_NAME: st.column_config.NumberColumn(UNDERSTANDING_COLUMN_NAME, help='Ability to understand language'),
+        }, hide_index=True, disabled=True, height=500)
+
+    # st.divider()
 
     # 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
-    selected_models = st.multiselect("Select models to compare", data["Model"].unique())
+    # 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]
+    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())
     selected_data = data[data["Model"].isin(selected_models)]
     categories = [AVERAGE_COLUMN_NAME, SENTIMENT_COLUMN_NAME, PHRASEOLOGY_COLUMN_NAME, UNDERSTANDING_COLUMN_NAME]
 
     if selected_models:
         # Kolorki do wyboru:
         # colors = px.colors.sample_colorscale("viridis", len(selected_models)+1)
-        colors = px.colors.qualitative.G10[:len(selected_models)]
-
-        # Create a chart with lines for each model for each category
-        fig = go.Figure()
-        for model, color in zip(selected_models, colors):
-            values = selected_data[selected_data['Model'] == model][categories].values.flatten().tolist()
-            values += values[:1]  # Repeat the first value to close the polygon
-
-            fig.add_trace(go.Scatterpolar(
-            r=values,
-            theta=categories + [categories[0]],  # Repeat the first category to close the polygon
-            name=model,
-            line_color=color,
-            fillcolor=color
-            ))
-
-        fig.update_layout(
-            polar=dict(
-                radialaxis=dict(
-                visible=True,
-                range=[0, 5]
-            )),
-        showlegend=True,
-        legend=dict(orientation="h", yanchor="top", y=-0.2, xanchor="center", x=0.5),
-        title="Comparison of Selected Models",
-        template="plotly_dark"
-        )
-        st.plotly_chart(fig)
-    
+        colors = px.colors.qualitative.D3[:len(selected_models)]
         # Create a chart with bars for each model for each category
         fig_bars = go.Figure()
         for model, color in zip(selected_models, colors):
@@ -259,6 +249,8 @@ with tab1:
         )
         st.plotly_chart(fig_bars)
 
+
+### Zakładka 2 --> Opis
 with tab2:
     st.header("Opis")
     st.write("Tutaj znajduje się trochę tekstu jako wypełniacz.")
@@ -276,4 +268,7 @@ st.markdown("""
 - [Krzysztof Wróbel](https://www.linkedin.com/in/wrobelkrzysztof/) - engineering, methodological support
 - [Szymon Baczyński](https://www.linkedin.com/in/szymon-baczynski/) - front-end / streamlit assistant
 """)
+
+st.divider()
+
 # Run the app with `streamlit run your_script.py`