Update src/streamlit_app.py

src/streamlit_app.py

@@ -408,160 +408,165 @@ def create_summary_table(df):
     
     return df_summary
 
-# Header
-st.markdown("""
-<div class="header-container">
-    <h1>NaviTrace Leaderboard</h1>
-    <div class="links-container">
-        <a href="https://leggedrobotics.github.io/navitrace_webpage/">
-            🏠 Project
-        </a>
-        <a href="https://arxiv.org/abs/2510.26909">
-            📄 Paper
-        </a>
-        <a href="https://github.com/leggedrobotics/navitrace_evaluation">
-            💻 Code
-        </a>
-        <a href="https://huggingface.co/datasets/leggedrobotics/navitrace">
-            💾 Dataset
-        </a>
-    </div>
-</div>
-""", unsafe_allow_html=True)
-
-# Load data
-df = load_data()
-
-# Add user's model if it exists in session state
-if 'user_results' in st.session_state:
-    user_results = pd.DataFrame(st.session_state.user_results)
-    df = pd.concat([user_results, df], ignore_index=True)
-
-# View selector
-view_type = st.selectbox(
-    "Select View",
-    ["Total Score", "Per Embodiment", "Per Category"],
-)
+def main():
 
-# Display chart
-fig = create_bar_chart(df, view_type)
-st.plotly_chart(fig, use_container_width=True, config={
-    'displayModeBar': True,
-    'displaylogo': False,
-    'toImageButtonOptions': {
-        'format': 'png',
-        'filename': 'navitrace_leaderboard',
-        'height': 600,
-        'width': 1200,
-        'scale': 2
-    }
-})
-
-# Detailed table
-with st.expander("View Detailed Scores"):
-    # Create the summary table
-    df_summary = create_summary_table(df)
-    
-    # Display table
-    st.dataframe(
-        df_summary.style.background_gradient(
-            cmap="Blues", 
-            subset=[col for col in df_summary.columns if col != "model"]
-        ).format("{:.2f}", subset=[col for col in df_summary.columns if col != "model"]),
-        width="stretch",
-        hide_index=True,
-    )
-
-with st.expander("How to Test Your Model", expanded=True):
-    # Step 1
+    # Header
     st.markdown("""
-    <div class="instruction-item">
-        <div class="instruction-number">1</div>
-        <div class="instruction-content">
-            <div><b>Run Evaluation</b></div>
-            <div>
-                Download and run our evaluation notebook adjusted to your model. The notebook will generate a TSV file with your model's predictions on the test set.
-            </div>
+    <div class="header-container">
+        <h1>NaviTrace Leaderboard</h1>
+        <div class="links-container">
+            <a href="https://leggedrobotics.github.io/navitrace_webpage/">
+                🏠 Project
+            </a>
+            <a href="https://arxiv.org/abs/2510.26909">
+                📄 Paper
+            </a>
+            <a href="https://github.com/leggedrobotics/navitrace_evaluation">
+                💻 Code
+            </a>
+            <a href="https://huggingface.co/datasets/leggedrobotics/navitrace">
+                💾 Dataset
+            </a>
         </div>
     </div>
     """, unsafe_allow_html=True)
     
-    st.link_button("📓 Open Evaluation Notebook", "https://github.com/leggedrobotics/navitrace_evaluation", width="stretch")
+    # Load data
+    df = load_data()
     
-    # Step 2
-    st.markdown("""
-    <div class="instruction-item">
-        <div class="instruction-number">2</div>
-        <div class="instruction-content">
-            <div><b>Upload Results</b></div>
-            <div>
-                Upload the TSV file generated by the evaluation notebook.
-            </div>
-        </div>
-    </div>
-    """, unsafe_allow_html=True)
-
-    # Chunk uploaded file to circumvent HF limit
-    #uploaded_file = st.file_uploader("Upload your TSV file with results", type=['tsv', 'txt'], label_visibility="collapsed")
-    uploaded_file = uploader("Upload your TSV file with results", key="chunk_uploader", chunk_size=1)
-    
-    # Step 3
-    st.markdown("""
-    <div class="instruction-item">
-        <div class="instruction-number">3</div>
-        <div class="instruction-content">
-            <div><b>Calculate Score</b></div>
-            <div>
-                Click the button below to evaluate your predictions. Scores are calculated using hidden test set ground-truths.
-            </div>
-        </div>
-    </div>
-    """, unsafe_allow_html=True)
-    
-    if uploaded_file is not None:
-        if st.button("🧮 Calculate Score", width="stretch"):
-            # Validate format
-            with st.spinner("Validating format and calculating score..."):
-                is_valid, result = validate_tsv_format(uploaded_file)
-                if is_valid:
-                    # Calculate score using hidden ground-truth
-                    scores = calculate_score(result)
-                    if scores is not None:
-                        # Store in session state
-                        scores["model"] = "Your Model"
-                        st.session_state.user_results = scores.to_dict(orient='list')
-                        st.rerun()
-                else:
-                    st.error(f"��� Invalid file format: {result}")
-    else:
-        st.info("👆 Upload a TSV file to calculate your score")
-
-    # Allow download of results
+    # Add user's model if it exists in session state
     if 'user_results' in st.session_state:
         user_results = pd.DataFrame(st.session_state.user_results)
-        st.success(f"✅ Score calculated successfully: **{user_results['score'].mean():.1f}**")
-        st.info("👆 Scroll up to see your model on the leaderboard!")
-        tsv_data = convert_df_to_tsv(user_results)
-        st.download_button(
-            label="🏅 Download Score",
-            data=tsv_data,
-            file_name='scores.tsv',
-            mime='text/tab-separated-values',
+        df = pd.concat([user_results, df], ignore_index=True)
+    
+    # View selector
+    view_type = st.selectbox(
+        "Select View",
+        ["Total Score", "Per Embodiment", "Per Category"],
+    )
+    
+    # Display chart
+    fig = create_bar_chart(df, view_type)
+    st.plotly_chart(fig, use_container_width=True, config={
+        'displayModeBar': True,
+        'displaylogo': False,
+        'toImageButtonOptions': {
+            'format': 'png',
+            'filename': 'navitrace_leaderboard',
+            'height': 600,
+            'width': 1200,
+            'scale': 2
+        }
+    })
+    
+    # Detailed table
+    with st.expander("View Detailed Scores"):
+        # Create the summary table
+        df_summary = create_summary_table(df)
+        
+        # Display table
+        st.dataframe(
+            df_summary.style.background_gradient(
+                cmap="Blues", 
+                subset=[col for col in df_summary.columns if col != "model"]
+            ).format("{:.2f}", subset=[col for col in df_summary.columns if col != "model"]),
             width="stretch",
+            hide_index=True,
         )
     
-    # Step 4
-    st.markdown("""
-    <div class="instruction-item">
-        <div class="instruction-number">4</div>
-        <div class="instruction-content">
-            <div><b>Submit to Official Leaderboard</b></div>
-            <div>
-                Happy with your score? Submit your model to appear on the official leaderboard.
-                Fill out the form below with your model details and results.
+    with st.expander("How to Test Your Model", expanded=True):
+        # Step 1
+        st.markdown("""
+        <div class="instruction-item">
+            <div class="instruction-number">1</div>
+            <div class="instruction-content">
+                <div><b>Run Evaluation</b></div>
+                <div>
+                    Download and run our evaluation notebook adjusted to your model. The notebook will generate a TSV file with your model's predictions on the test set.
+                </div>
             </div>
         </div>
-    </div>
-    """, unsafe_allow_html=True)
+        """, unsafe_allow_html=True)
+        
+        st.link_button("📓 Open Evaluation Notebook", "https://github.com/leggedrobotics/navitrace_evaluation", width="stretch")
+        
+        # Step 2
+        st.markdown("""
+        <div class="instruction-item">
+            <div class="instruction-number">2</div>
+            <div class="instruction-content">
+                <div><b>Upload Results</b></div>
+                <div>
+                    Upload the TSV file generated by the evaluation notebook.
+                </div>
+            </div>
+        </div>
+        """, unsafe_allow_html=True)
+    
+        # Chunk uploaded file to circumvent HF limit
+        #uploaded_file = st.file_uploader("Upload your TSV file with results", type=['tsv', 'txt'], label_visibility="collapsed")
+        uploaded_file = uploader("", key="chunk_uploader", chunk_size=1)
+        
+        # Step 3
+        st.markdown("""
+        <div class="instruction-item">
+            <div class="instruction-number">3</div>
+            <div class="instruction-content">
+                <div><b>Calculate Score</b></div>
+                <div>
+                    Click the button below to evaluate your predictions. Scores are calculated using hidden test set ground-truths.
+                </div>
+            </div>
+        </div>
+        """, unsafe_allow_html=True)
+        
+        if uploaded_file is not None:
+            if st.button("🧮 Calculate Score", width="stretch"):
+                # Validate format
+                with st.spinner("Validating format and calculating score..."):
+                    is_valid, result = validate_tsv_format(uploaded_file)
+                    if is_valid:
+                        # Calculate score using hidden ground-truth
+                        scores = calculate_score(result)
+                        if scores is not None:
+                            # Store in session state
+                            scores["model"] = "Your Model"
+                            st.session_state.user_results = scores.to_dict(orient='list')
+                            st.rerun()
+                    else:
+                        st.error(f"❌ Invalid file format: {result}")
+        else:
+            st.info("👆 Upload a TSV file to calculate your score")
     
-    st.link_button("🗳️ Submit Model", "https://docs.google.com/forms/d/e/1FAIpQLSfcAQ6JW7eey-8OFSAz2ea_StCezxJK1dt6mjW_wR-9jCHnXg/viewform?usp=dialog", width="stretch")
+        # Allow download of results
+        if 'user_results' in st.session_state:
+            user_results = pd.DataFrame(st.session_state.user_results)
+            st.success(f"✅ Score calculated successfully: **{user_results['score'].mean():.1f}**")
+            st.info("👆 Scroll up to see your model on the leaderboard!")
+            tsv_data = convert_df_to_tsv(user_results)
+            st.download_button(
+                label="🏅 Download Score",
+                data=tsv_data,
+                file_name='scores.tsv',
+                mime='text/tab-separated-values',
+                width="stretch",
+            )
+        
+        # Step 4
+        st.markdown("""
+        <div class="instruction-item">
+            <div class="instruction-number">4</div>
+            <div class="instruction-content">
+                <div><b>Submit to Official Leaderboard</b></div>
+                <div>
+                    Happy with your score? Submit your model to appear on the official leaderboard.
+                    Fill out the form below with your model details and results.
+                </div>
+            </div>
+        </div>
+        """, unsafe_allow_html=True)
+        
+        st.link_button("🗳️ Submit Model", "https://docs.google.com/forms/d/e/1FAIpQLSfcAQ6JW7eey-8OFSAz2ea_StCezxJK1dt6mjW_wR-9jCHnXg/viewform?usp=dialog", width="stretch")
+
+if __name__ == "__main__":
+    main()