Update app.py

app.py

@@ -68,11 +68,15 @@ def predict(sentence1, sentence2):
     test_data = BertSemanticDataGenerator(
         sentence_pairs, labels=None, batch_size=1, shuffle=False, include_targets=False,
     )
-    proba = model.predict(test_data[0])[0]
-    idx = np.argmax(proba)
-    proba = f"{proba[idx]*100:.2f}%"
-    pred = labels[idx]
-    return f'The semantic similarity of two input sentences is {pred} with {proba} of probability'
+    probs　= model.predict(test_data[0])[0]
+    
+    labels_probs = {labels[i]: float(probs[i]) for i, _ in enumerate(labels)}
+    return labels_probs
+    
+    #idx = np.argmax(proba)
+    #proba = f"{proba[idx]*100:.2f}%"
+    #pred = labels[idx]
+    #return f'The semantic similarity of two input sentences is {pred} with {proba} of probability'
 
 inputs = [
          gr.Audio(source = "upload", label='Upload audio file', type="filepath"),
@@ -89,7 +93,8 @@ gr.Interface(
     description = "Natural Language Inference by fine-tuning BERT model on SNLI Corpus 📰",
     inputs=["text", "text"],
     examples=examples,
-    outputs=gr.Textbox(label='Prediction'),
+    #outputs=gr.Textbox(label='Prediction'),
+    outputs=gr.outputs.Label(num_top_classes=3, label='Semantic similarity score'),
     cache_examples=False,
     article = "Author: <a href=\"https://huggingface.co/vumichien\">Vu Minh Chien</a>. Based on the keras example from <a href=\"https://keras.io/examples/nlp/semantic_similarity_with_bert/\">Mohamad Merchant</a>",
 ).launch(debug=True, enable_queue=True)