Update src/streamlit_app.py

src/streamlit_app.py

@@ -232,21 +232,45 @@ def create_bar_chart(df, view_type):
     
     return fig
 
+# Serve only the chart as JSON if parameter "only_chart" is set
+# E.g. https://huggingface.co/spaces/leggedrobotics/navitrace_leaderboard/?only_chart=total_score
+params = st.query_params()
+if "only_chart" in params:
+    if params["only_chart"] == "total_score":
+        view_type = "Total Score"
+    elif params["only_chart"] == "per_embodiment":
+        view_type = "Per Embodiment"
+    elif params["only_chart"] == "per_category":
+        view_type = "Per Category"
+    else:
+        break
+
+    # Create chart
+    df = load_sample_data()
+    fig = create_bar_chart(df, view_type)
+
+    # Only output JSON
+    st.write(json.dumps(fig.to_dict()))
+    st.stop()
+
 # Main content
 st.title("NaviTrace Leaderboard")
 
 # Links
 st.markdown("""
 <div class="links-container">
+    <a href="https://leggedrobotics.github.io/navitrace_webpage/" target="_blank">
+        <i class="fas fa-house"></i> Project
+    </a>
     <a href="https://your-paper-website.com" target="_blank">
         <i class="fas fa-file-pdf"></i> Paper
     </a>
-    <a href="https://huggingface.co/datasets/your-username/navitrace" target="_blank">
-        <i class="fas fa-database"></i> Dataset
-    </a>
     <a href="https://github.com/your-username/navitrace" target="_blank">
         <i class="fab fa-github"></i> Code
     </a>
+    <a href="https://huggingface.co/datasets/your-username/navitrace" target="_blank">
+        <i class="fas fa-database"></i> Dataset
+    </a>
     <a href="https://your-demo-link.com" target="_blank">
         <i class="far fa-images"></i> Demo
     </a>
@@ -304,7 +328,7 @@ with st.expander("How to Test Your Model", expanded=True):
         <div class="instruction-content">
             <div><b>Run Evaluation</b></div>
             <div>
-                Download and run our evaluation notebook on your model. The notebook will generate a TSV file with your model's predictions on the test set.
+                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>
@@ -319,13 +343,13 @@ with st.expander("How to Test Your Model", expanded=True):
         <div class="instruction-content">
             <div><b>Upload Results</b></div>
             <div>
-                Upload the TSV file generated by the evaluation notebook. Your predictions will be evaluated against our private test set.
+                Upload the TSV file generated by the evaluation notebook.
             </div>
         </div>
     </div>
     """, unsafe_allow_html=True)
     
-    uploaded_file = st.file_uploader("Choose a TSV file", type=['tsv', 'txt'])
+    uploaded_file = st.file_uploader("", type=['tsv', 'txt'])
     
     # Step 3
     st.markdown("""
@@ -334,19 +358,19 @@ with st.expander("How to Test Your Model", expanded=True):
         <div class="instruction-content">
             <div><b>Calculate Score</b></div>
             <div>
-                Click the button below to evaluate your predictions. Scores are calculated using the hidden test set ground truth.
+                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", use_container_width=False):
+        if st.button("🧮 Calculate Score", use_container_width=True):
             with st.spinner("Validating and calculating scores..."):
                 # Validate format
                 is_valid, result = validate_tsv_format(uploaded_file)
                 if is_valid:
-                    # Calculate score using private ground truth
+                    # Calculate score using hidden ground-truth
                     scores = calculate_score(result)
                     if scores is not None:
                         st.success(f"✅ Score calculated successfully: **{scores['Total Score']:.1f}**")