Update app.py

app.py

@@ -157,116 +157,16 @@
 
 
 
-# import streamlit as st
-# import pandas as pd
-# import altair as alt
-
-# # Load the leaderboard data
-# df = pd.read_csv("leaderboard.csv")
-
-# # Add title and description
-# st.title("PathVLMs Leaderboard")
-# st.write("Interactive leaderboard for Pathology Language Model-based Methods (LMMs).")
-
-# # 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 available)
-#     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))
-# ]
-
-# # Warning if no results
-# if filtered_df.empty:
-#     st.warning("No results found. Try adjusting the filters.")
-# else:
-#     # Leaderboard Table
-#     st.subheader("Filtered Leaderboard Table")
-#     st.dataframe(filtered_df)
-
-#     # Select Metric to Visualize
-#     st.subheader("Visualization")
-#     metric = st.selectbox("Select Metric to Visualize", options=filtered_df.columns[5:])
-
-#     # Visualization
-#     chart = alt.Chart(filtered_df).mark_bar().encode(
-#         x=alt.X('Method', sort='-y', title="Model"),
-#         y=alt.Y(metric, title=metric),
-#         color='Method',
-#         tooltip=['Method', metric]
-#     ).properties(
-#         width=800,
-#         height=400
-#     )
-#     st.altair_chart(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=800,
-#         height=400
-#     )
-#     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"
-#     )
-
-
-
 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 models in Pathology Language-Model-based Methods (LMMs).
-You can search, filter, and visualize metrics for better insights.
-""")
+st.title("PathVLMs Leaderboard")
+st.write("Interactive leaderboard for Pathology Language Model-based Methods (LMMs).")
 
 # Sidebar Filters
 with st.sidebar:
@@ -278,7 +178,7 @@ with st.sidebar:
     model_sizes = df['Params (B)'].unique()
     selected_sizes = st.multiselect("Select Model Sizes", options=model_sizes, default=model_sizes)
     
-    # Filter by model type
+    # Filter by model type (if available)
     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)
@@ -292,31 +192,27 @@ filtered_df = df[
     (df['Method'].str.contains(search_query, case=False, na=False))
 ]
 
-# Main Leaderboard Section
-st.subheader("Leaderboard Table")
+# Warning if no results
 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
-    )
+    # Leaderboard Table
+    st.subheader("Filtered Leaderboard Table")
+    st.dataframe(filtered_df)
 
-    # Visualization of selected metric
-    st.subheader("Performance Metrics Visualization")
+    # Select Metric to Visualize
+    st.subheader("Visualization")
     metric = st.selectbox("Select Metric to Visualize", options=filtered_df.columns[5:])
 
-    # Visualization Chart
+    # Visualization
     chart = alt.Chart(filtered_df).mark_bar().encode(
-        x=alt.X('Method', sort=alt.EncodingSortField(field=metric, order='descending'), title="Model"),
+        x=alt.X('Method', sort='-y', title="Model"),
         y=alt.Y(metric, title=metric),
         color='Method',
         tooltip=['Method', metric]
     ).properties(
-        width=1400,  # Full width
-        height=600   # Increased height
+        width=800,
+        height=400
     )
     st.altair_chart(chart, use_container_width=True)
 
@@ -331,8 +227,8 @@ else:
         color='Method',
         tooltip=['Method', metric]
     ).properties(
-        width=1400,  # Full width
-        height=400   # Adjusted height for smaller chart
+        width=800,
+        height=400
     )
     st.altair_chart(top_chart, use_container_width=True)
 
@@ -351,6 +247,110 @@ else:
 
 
 
+# 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 models in Pathology Language-Model-based Methods (LMMs).
+# 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)
+
+#     # 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"
+#     )
+
+
+
 # import streamlit as st
 # import pandas as pd
 # import altair as alt