Update app.py

app.py

@@ -247,107 +247,107 @@
 
 
 
-# import streamlit as st
-# import pandas as pd
-# import altair as alt
+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"
-# )
+# 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")
+# 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.
-# """)
+# 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")
+# 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 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)
+    # 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 = []
 
-#     # Download Button
-#     @st.cache
-#     def convert_df_to_csv(dataframe):
-#         return dataframe.to_csv(index=False).encode('utf-8')
+# 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
+    )
 
-#     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"
-#     )
+    # 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"
+    )
 
 
 
@@ -473,143 +473,143 @@
 
 
 
-import streamlit as st
-import pandas as pd
-import altair as alt
+# 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"
-)
+# # 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")
+# # Load the leaderboard data
+# df = pd.read_csv("leaderboard.csv")
 
-# Add custom CSS for alignment
-st.markdown(
-    """
-    <style>
-    .centered {
-        display: flex;
-        justify-content: center;
-        align-items: center;
-        flex-direction: column;
-    }
-    .search-bar {
-        width: 60%;
-        margin-bottom: 20px;
-    }
-    </style>
-    """,
-    unsafe_allow_html=True
-)
+# # Add custom CSS for alignment
+# st.markdown(
+#     """
+#     <style>
+#     .centered {
+#         display: flex;
+#         justify-content: center;
+#         align-items: center;
+#         flex-direction: column;
+#     }
+#     .search-bar {
+#         width: 60%;
+#         margin-bottom: 20px;
+#     }
+#     </style>
+#     """,
+#     unsafe_allow_html=True
+# )
 
-# 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.
-""")
+# # 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.
+# """)
 
-# Create a layout with two columns: main content (left) and filters (right)
-col1, col2 = st.columns([4, 1])  # Adjust column ratio
+# # Create a layout with two columns: main content (left) and filters (right)
+# col1, col2 = st.columns([4, 1])  # Adjust column ratio
 
-# Filters on the right
-with col2:
-    st.subheader("Filters")
+# # Filters on the right
+# with col2:
+#     st.subheader("Filters")
     
-    # Search by model name
-    search_query = st.text_input(
-        "Search for Model Name",
-        value="",
-        placeholder="e.g., llava",
-        key="search_bar",
-        help="Enter part of a model name to filter the leaderboard.",
-    )
+#     # Search by model name
+#     search_query = st.text_input(
+#         "Search for Model Name",
+#         value="",
+#         placeholder="e.g., llava",
+#         key="search_bar",
+#         help="Enter part of a model name to filter the leaderboard.",
+#     )
     
-    # 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 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 applicable)
-    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 = []
+#     # Filter by model type (if applicable)
+#     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 = []
 
-# Main content on the left
-with col1:
-    # Apply filters to the DataFrame
-    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:
-        # Display the filtered leaderboard table
-        st.markdown('<div class="centered">', unsafe_allow_html=True)
-        st.subheader("Leaderboard Table")
-        st.dataframe(filtered_df, height=600, width=1400)  # Adjust table size
-        st.markdown('</div>', unsafe_allow_html=True)
-
-        # Visualization Section
-        st.subheader("Performance Metrics Visualization")
-        metric = st.selectbox("Select Metric to Visualize", options=filtered_df.columns[5:])
-
-        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=1000,  # Adjust chart width
-            height=500   # Adjust chart 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=1000,
-            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"
-        )
+# # Main content on the left
+# with col1:
+#     # Apply filters to the DataFrame
+#     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:
+#         # Display the filtered leaderboard table
+#         st.markdown('<div class="centered">', unsafe_allow_html=True)
+#         st.subheader("Leaderboard Table")
+#         st.dataframe(filtered_df, height=600, width=1400)  # Adjust table size
+#         st.markdown('</div>', unsafe_allow_html=True)
+
+#         # Visualization Section
+#         st.subheader("Performance Metrics Visualization")
+#         metric = st.selectbox("Select Metric to Visualize", options=filtered_df.columns[5:])
+
+#         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=1000,  # Adjust chart width
+#             height=500   # Adjust chart 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=1000,
+#             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"
+#         )