Update app.py

app.py

@@ -1,3 +1,109 @@
+# 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)
+
+#     # 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
@@ -71,6 +177,23 @@ else:
     )
     st.altair_chart(chart, use_container_width=True)
 
+    # Bubble Plot for Accuracy vs. Model Size
+    st.subheader("Bubble Plot: Accuracy vs. Model Size")
+    if 'Accuracy' in filtered_df.columns and 'Params (B)' in filtered_df.columns:
+        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('Accuracy:Q', title="Accuracy"),
+            size=alt.Size('Params (B):Q', legend=None),
+            color=alt.Color('Method:N', title="Model"),
+            tooltip=['Method', 'Accuracy', 'Params (B)']
+        ).properties(
+            width=800,
+            height=600
+        )
+        st.altair_chart(bubble_chart, use_container_width=True)
+    else:
+        st.warning("Columns 'Accuracy' 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)
@@ -101,5 +224,3 @@ else:
     )
 
 
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