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 @@ df = pd.read_csv("leaderboard.csv")
 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 @@ filtered_df = df[
     (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