Update app.py

app.py

@@ -165,7 +165,7 @@ def _create_comparison_table(probs, preds):
 </div>"""
     return table_html
 
-def predict_emotion(text, show_radar=True, show_table=True):
+def predict_emotion(text, show_table=True):
     if not state.ready:
         return "<div style='padding:40px;text-align:center;background:#7f1d1d;border:2px solid #ef4444;border-radius:16px;'><div style='font-size:64px;'>⚠️</div><h2 style='color:#fecaca;'>Model Not Loaded</h2><p style='color:#fca5a5;'>Click Load Model button</p></div>", "", "", "{}", _create_controls_panel()
     
@@ -308,7 +308,6 @@ def create_interface():
                             analyze = gr.Button("🔍 Analyze Emotions", variant="primary", size="lg")
                             clear = gr.ClearButton([inp], value="🗑️ Clear", size="lg")
                         with gr.Row():
-                            radar = gr.Checkbox(label="Show Radar Chart", value=True)
                             table = gr.Checkbox(label="Show Table", value=True)
                         gr.Examples([
                             ["I'm absolutely thrilled and overjoyed! This is amazing!"],
@@ -343,8 +342,8 @@ def create_interface():
         gr.HTML("<div style='margin-top:30px;padding:20px;background:#1f2937;border-radius:12px;border:2px solid #374151;'><h2 style='color:#f9fafb;margin-top:0;'>📚 Model Documentation</h2><p style='color:#d1d5db;line-height:1.8;'><strong>Architecture:</strong> RoBERTa-base (125M parameters) fine-tuned for multi-label emotion classification</p><p style='color:#d1d5db;line-height:1.8;'><strong>Classes:</strong> Anger, Fear, Joy, Sadness, Surprise</p><p style='color:#d1d5db;line-height:1.8;'><strong>Performance:</strong> F1 Score 0.872 on validation set</p></div>")
         
         load_btn.click(load_model, outputs=[status, controls])
-        analyze.click(predict_emotion, inputs=[inp, radar, table], outputs=[summ, details, tbl, json_out, controls])
-        inp.submit(predict_emotion, inputs=[inp, radar, table], outputs=[summ, details, tbl, json_out, controls])
+        analyze.click(predict_emotion, inputs=[inp, table], outputs=[summ, details, tbl, json_out, controls])
+        inp.submit(predict_emotion, inputs=[inp, table], outputs=[summ, details, tbl, json_out, controls])
         clear_res.click(lambda: ("", "", "", "{}", _create_controls_panel()), outputs=[summ, details, tbl, json_out, controls])
         batch_btn.click(batch_predict, inputs=[batch_inp], outputs=[batch_out])
         hist_btn.click(get_history, outputs=[hist_out])