chacge type of output

app.py

@@ -16,17 +16,15 @@ except Exception as e:
 def classify_code(text):
     embed = model._texts2vecs([text])
     probs = model.classifier_code.predict_proba(embed)
-    best_n = np.flip(np.argsort(probs, axis=1,)[0,-3:])
-    preds = [{model.classifier_code.classes_[i]: f"{probs[0][i]*100:.1f}"} for i in best_n]
-    #output_text = '\n'.join(preds)
+    best_n = np.flip(np.argsort(probs, axis=1,)[0])
+    preds = {model.classifier_code.classes_[i]: probs[0][i] for i in best_n}
     return preds
 
 def classify_group(text):
     embed = model._texts2vecs([text])
     probs = model.classifier_group.predict_proba(embed)
-    best_n = np.flip(np.argsort(probs, axis=1,)[0,-3:])
-    preds = [{model.classifier_group.classes_[i]: f"{probs[0][i]*100:.1f}"} for i in best_n]
-    # output_text = '\n'.join(preds)
+    best_n = np.flip(np.argsort(probs, axis=1,)[0])
+    preds = {model.classifier_group.classes_[i]: probs[0][i] for i in best_n}
     return preds
 
 def classify(text):
@@ -40,6 +38,6 @@ iface = gr.Interface(
     description="",
     fn=classify,
     inputs=[gr.Textbox(label="Input text", value=default_input_text)],
-    outputs=[gr.outputs.Textbox(label="Result class"), gr.outputs.Textbox(label="Result group")],
+    outputs=[gr.Label(num_top_classes=4, label="Result class"), gr.Label(num_top_classes=4, label="Result group")],
 )
 iface.launch()