Update app.py

app.py

@@ -105,148 +105,6 @@
 # interactive Bubble plot
 
 
-import streamlit as st
-import pandas as pd
-import altair as alt
-
-# Set page layout to wide mode
-st.set_page_config(
-    page_title="PathVLMs Leaderboard",
-    page_icon="🏆",
-    layout="wide"
-)
-
-# Load the leaderboard data
-df = pd.read_csv("leaderboard.csv")
-
-# Add title and description
-st.title("PathVLMs Leaderboard 🏆")
-st.markdown("""
-Welcome to the **PathVLMs Leaderboard**! This leaderboard displays evaluation results for various Vision-Language Models (VLMs) in Pathology, focusing on multiple-choice questions (MCQs), answers, and explanations.  
-You can search, filter, and visualize metrics for better insights.
-""")
-
-# Sidebar Filters
-with st.sidebar:
-    st.subheader("Filters")
-    # Search by model name (fuzzy search)
-    search_query = st.text_input("Search for Model Name", value="", placeholder="e.g., llava")
-    
-    # Filter by model size
-    model_sizes = df['Params (B)'].unique()
-    selected_sizes = st.multiselect("Select Model Sizes", options=model_sizes, default=model_sizes)
-    
-    # Filter by model type
-    if 'Language Model' in df.columns:
-        model_types = df['Language Model'].unique()
-        selected_types = st.multiselect("Select Model Types", options=model_types, default=model_types)
-    else:
-        selected_types = []
-
-# Apply Filters
-filtered_df = df[
-    (df['Params (B)'].isin(selected_sizes)) &
-    (df['Language Model'].isin(selected_types) if selected_types else True) &
-    (df['Method'].str.contains(search_query, case=False, na=False))
-]
-
-# Main Leaderboard Section
-st.subheader("Leaderboard Table")
-if filtered_df.empty:
-    st.warning("No results found. Try adjusting the filters.")
-else:
-    # Display table in wide layout
-    st.dataframe(
-        filtered_df,
-        height=600,  # Adjust table height
-        width=1600  # Adjust table width
-    )
-
-    # Visualization of selected metric
-    st.subheader("Performance Metrics Visualization")
-    metric = st.selectbox("Select Metric to Visualize", options=filtered_df.columns[5:])
-
-    # Visualization Chart
-    chart = alt.Chart(filtered_df).mark_bar().encode(
-        x=alt.X('Method', sort=alt.EncodingSortField(field=metric, order='descending'), title="Model"),
-        y=alt.Y(metric, title=metric),
-        color='Method',
-        tooltip=['Method', metric]
-    ).properties(
-        width=1400,  # Full width
-        height=600   # Increased height
-    )
-    st.altair_chart(chart, use_container_width=True)
-
-    # Interactive Bubble Plot: Metric vs. Model Size
-    st.subheader("Interactive Bubble Plot: Metric vs. Model Size")
-    bubble_metric = st.selectbox("Select Metric for Bubble Plot", options=filtered_df.columns[5:], index=0)
-
-    if bubble_metric in filtered_df.columns and 'Params (B)' in filtered_df.columns:
-        interactive_bubble_chart = alt.Chart(filtered_df).mark_circle(size=200).encode(
-            x=alt.X('Params (B):Q', title="Model Size (in Billion Params)"),
-            y=alt.Y(f'{bubble_metric}:Q', title=bubble_metric),
-            size=alt.Size('Params (B):Q', legend=None),
-            color=alt.Color('Method:N', title="Model"),
-            tooltip=['Method', 'Params (B)', bubble_metric]
-        ).properties(
-            width=800,
-            height=600
-        )
-        st.altair_chart(interactive_bubble_chart, use_container_width=True)
-    else:
-        st.warning(f"Columns '{bubble_metric}' and 'Params (B)' are required for the bubble plot.")
-
-    # # Highlight Top N Models
-    # st.subheader("Highlight Top N Models")
-    # top_n = st.slider("Number of Top Models", min_value=1, max_value=len(filtered_df), value=5)
-    # top_models_df = filtered_df.nlargest(top_n, metric)
-
-    # top_chart = alt.Chart(top_models_df).mark_bar().encode(
-    #     x=alt.X('Method', title="Model"),
-    #     y=alt.Y(metric, title=metric),
-    #     color='Method',
-    #     tooltip=['Method', metric]
-    # ).properties(
-    #     width=1400,  # Full width
-    #     height=400   # Adjusted height for smaller chart
-    # )
-    # st.altair_chart(top_chart, use_container_width=True)
-
-     # Highlight Top N Models
-    st.subheader("Highlight Top N Models")
-    top_n = st.slider("Number of Top Models", min_value=1, max_value=len(filtered_df), value=5)
-    top_models_df = filtered_df.nlargest(top_n, metric)
-
-    top_chart = alt.Chart(top_models_df).mark_bar().encode(
-        x=alt.X('Method', title="Model"),
-        y=alt.Y(metric, title=metric),
-        color='Method',
-        tooltip=['Method', metric]
-    ).properties(
-        width=1400,  # Full width
-        height=400   # Adjusted height for smaller chart
-    )
-    st.altair_chart(top_chart, use_container_width=True)
-
-
-    # Download Button
-    @st.cache
-    def convert_df_to_csv(dataframe):
-        return dataframe.to_csv(index=False).encode('utf-8')
-
-    csv_data = convert_df_to_csv(filtered_df)
-    st.download_button(
-        label="Download Filtered Results",
-        data=csv_data,
-        file_name="filtered_leaderboard.csv",
-        mime="text/csv"
-    )
-
-
-
-# inteactive bubble plot and pagination 
-
 # import streamlit as st
 # import pandas as pd
 # import altair as alt
@@ -265,7 +123,7 @@ else:
 # st.title("PathVLMs Leaderboard 🏆")
 # st.markdown("""
 # Welcome to the **PathVLMs Leaderboard**! This leaderboard displays evaluation results for various Vision-Language Models (VLMs) in Pathology, focusing on multiple-choice questions (MCQs), answers, and explanations.  
-# You can search, filter, and sort the leaderboard dynamically by score for better insights.
+# You can search, filter, and visualize metrics for better insights.
 # """)
 
 # # Sidebar Filters
@@ -292,30 +150,24 @@ else:
 #     (df['Method'].str.contains(search_query, case=False, na=False))
 # ]
 
-# # Add Sorting Option
-# st.subheader("Leaderboard Table with Dynamic Sorting")
+# # Main Leaderboard Section
+# st.subheader("Leaderboard Table")
 # if filtered_df.empty:
 #     st.warning("No results found. Try adjusting the filters.")
 # else:
-#     # Dropdown to select the score column to sort by
-#     sort_column = st.selectbox(
-#         "Sort by Score Column",
-#         options=filtered_df.columns[5:],  # Only score-related columns
-#         index=0  # Default to the first score column
+#     # Display table in wide layout
+#     st.dataframe(
+#         filtered_df,
+#         height=600,  # Adjust table height
+#         width=1600  # Adjust table width
 #     )
 
-#     # Sort the dataframe by the selected column in descending order
-#     sorted_df = filtered_df.sort_values(by=sort_column, ascending=False)
-
-#     # Display the sorted table
-#     st.dataframe(sorted_df, use_container_width=True)
-
 #     # Visualization of selected metric
 #     st.subheader("Performance Metrics Visualization")
 #     metric = st.selectbox("Select Metric to Visualize", options=filtered_df.columns[5:])
 
 #     # Visualization Chart
-#     chart = alt.Chart(sorted_df).mark_bar().encode(
+#     chart = alt.Chart(filtered_df).mark_bar().encode(
 #         x=alt.X('Method', sort=alt.EncodingSortField(field=metric, order='descending'), title="Model"),
 #         y=alt.Y(metric, title=metric),
 #         color='Method',
@@ -326,7 +178,7 @@ else:
 #     )
 #     st.altair_chart(chart, use_container_width=True)
 
-#     # Bubble Plot: Metric vs. Model Size
+#     # Interactive Bubble Plot: Metric vs. Model Size
 #     st.subheader("Interactive Bubble Plot: Metric vs. Model Size")
 #     bubble_metric = st.selectbox("Select Metric for Bubble Plot", options=filtered_df.columns[5:], index=0)
 
@@ -345,10 +197,26 @@ else:
 #     else:
 #         st.warning(f"Columns '{bubble_metric}' and 'Params (B)' are required for the bubble plot.")
 
-#     # Highlight Top N Models
+#     # # Highlight Top N Models
+#     # st.subheader("Highlight Top N Models")
+#     # top_n = st.slider("Number of Top Models", min_value=1, max_value=len(filtered_df), value=5)
+#     # top_models_df = filtered_df.nlargest(top_n, metric)
+
+#     # top_chart = alt.Chart(top_models_df).mark_bar().encode(
+#     #     x=alt.X('Method', title="Model"),
+#     #     y=alt.Y(metric, title=metric),
+#     #     color='Method',
+#     #     tooltip=['Method', metric]
+#     # ).properties(
+#     #     width=1400,  # Full width
+#     #     height=400   # Adjusted height for smaller chart
+#     # )
+#     # st.altair_chart(top_chart, use_container_width=True)
+
+#      # Highlight Top N Models
 #     st.subheader("Highlight Top N Models")
 #     top_n = st.slider("Number of Top Models", min_value=1, max_value=len(filtered_df), value=5)
-#     top_models_df = sorted_df.nlargest(top_n, metric)
+#     top_models_df = filtered_df.nlargest(top_n, metric)
 
 #     top_chart = alt.Chart(top_models_df).mark_bar().encode(
 #         x=alt.X('Method', title="Model"),
@@ -361,20 +229,152 @@ else:
 #     )
 #     st.altair_chart(top_chart, use_container_width=True)
 
+
 #     # Download Button
 #     @st.cache
 #     def convert_df_to_csv(dataframe):
 #         return dataframe.to_csv(index=False).encode('utf-8')
 
-#     csv_data = convert_df_to_csv(sorted_df)
+#     csv_data = convert_df_to_csv(filtered_df)
 #     st.download_button(
-#         label="Download Sorted Results",
+#         label="Download Filtered Results",
 #         data=csv_data,
-#         file_name="sorted_leaderboard.csv",
+#         file_name="filtered_leaderboard.csv",
 #         mime="text/csv"
 #     )
 
 
+
+# inteactive bubble plot and pagination 
+
+import streamlit as st
+import pandas as pd
+import altair as alt
+
+# Set page layout to wide mode
+st.set_page_config(
+    page_title="PathVLMs Leaderboard",
+    page_icon="🏆",
+    layout="wide"
+)
+
+# Load the leaderboard data
+df = pd.read_csv("leaderboard.csv")
+
+# Add title and description
+st.title("PathVLMs Leaderboard 🏆")
+st.markdown("""
+Welcome to the **PathVLMs Leaderboard**! This leaderboard displays evaluation results for various Vision-Language Models (VLMs) in Pathology, focusing on multiple-choice questions (MCQs), answers, and explanations.  
+You can search, filter, and sort the leaderboard dynamically by score for better insights.
+""")
+
+# Sidebar Filters
+with st.sidebar:
+    st.subheader("Filters")
+    # Search by model name (fuzzy search)
+    search_query = st.text_input("Search for Model Name", value="", placeholder="e.g., llava")
+    
+    # Filter by model size
+    model_sizes = df['Params (B)'].unique()
+    selected_sizes = st.multiselect("Select Model Sizes", options=model_sizes, default=model_sizes)
+    
+    # Filter by model type
+    if 'Language Model' in df.columns:
+        model_types = df['Language Model'].unique()
+        selected_types = st.multiselect("Select Model Types", options=model_types, default=model_types)
+    else:
+        selected_types = []
+
+# Apply Filters
+filtered_df = df[
+    (df['Params (B)'].isin(selected_sizes)) &
+    (df['Language Model'].isin(selected_types) if selected_types else True) &
+    (df['Method'].str.contains(search_query, case=False, na=False))
+]
+
+# Add Sorting Option
+st.subheader("Leaderboard Table with Dynamic Sorting")
+if filtered_df.empty:
+    st.warning("No results found. Try adjusting the filters.")
+else:
+    # Dropdown to select the score column to sort by
+    sort_column = st.selectbox(
+        "Sort by Score Column",
+        options=filtered_df.columns[5:],  # Only score-related columns
+        index=0  # Default to the first score column
+    )
+
+    # Sort the dataframe by the selected column in descending order
+    sorted_df = filtered_df.sort_values(by=sort_column, ascending=False)
+
+    # Display the sorted table
+    st.dataframe(sorted_df, use_container_width=True)
+
+    # Visualization of selected metric
+    st.subheader("Performance Metrics Visualization")
+    metric = st.selectbox("Select Metric to Visualize", options=filtered_df.columns[5:])
+
+    # Visualization Chart
+    chart = alt.Chart(sorted_df).mark_bar().encode(
+        x=alt.X('Method', sort=alt.EncodingSortField(field=metric, order='descending'), title="Model"),
+        y=alt.Y(metric, title=metric),
+        color='Method',
+        tooltip=['Method', metric]
+    ).properties(
+        width=1400,  # Full width
+        height=600   # Increased height
+    )
+    st.altair_chart(chart, use_container_width=True)
+
+    # Bubble Plot: Metric vs. Model Size
+    st.subheader("Interactive Bubble Plot: Metric vs. Model Size")
+    bubble_metric = st.selectbox("Select Metric for Bubble Plot", options=filtered_df.columns[5:], index=0)
+
+    if bubble_metric in filtered_df.columns and 'Params (B)' in filtered_df.columns:
+        interactive_bubble_chart = alt.Chart(filtered_df).mark_circle(size=200).encode(
+            x=alt.X('Params (B):Q', title="Model Size (in Billion Params)"),
+            y=alt.Y(f'{bubble_metric}:Q', title=bubble_metric),
+            size=alt.Size('Params (B):Q', legend=None),
+            color=alt.Color('Method:N', title="Model"),
+            tooltip=['Method', 'Params (B)', bubble_metric]
+        ).properties(
+            width=800,
+            height=600
+        )
+        st.altair_chart(interactive_bubble_chart, use_container_width=True)
+    else:
+        st.warning(f"Columns '{bubble_metric}' and 'Params (B)' are required for the bubble plot.")
+
+    # Highlight Top N Models
+    st.subheader("Highlight Top N Models")
+    top_n = st.slider("Number of Top Models", min_value=1, max_value=len(filtered_df), value=5)
+    top_models_df = sorted_df.nlargest(top_n, metric)
+
+    top_chart = alt.Chart(top_models_df).mark_bar().encode(
+        x=alt.X('Method', title="Model"),
+        y=alt.Y(metric, title=metric),
+        color='Method',
+        tooltip=['Method', metric]
+    ).properties(
+        width=1400,  # Full width
+        height=400   # Adjusted height for smaller chart
+    )
+    st.altair_chart(top_chart, use_container_width=True)
+
+    # Download Button
+    @st.cache
+    def convert_df_to_csv(dataframe):
+        return dataframe.to_csv(index=False).encode('utf-8')
+
+    csv_data = convert_df_to_csv(sorted_df)
+    st.download_button(
+        label="Download Sorted Results",
+        data=csv_data,
+        file_name="sorted_leaderboard.csv",
+        mime="text/csv"
+    )
+
+
     # with bubble plot
 
 # import streamlit as st