Update app.py

app.py

@@ -147,16 +147,17 @@ def predict(wave):
         with torch.no_grad():
             prediction = model(wave)
         predicted_emotion, confidence = decode_emotion_prediction(prediction, le)
-        return f"Predicted emotion: {predicted_emotion} (Confidence: {confidence*100:.4f})"
+        return f"Your emotion is: {predicted_emotion} with {confidence*100:.4f}% confidence level."
     except Exception as e:
         return f'Error in processing audio: {str(e)}'
 
 # Gradio Interface
 article = """
 ### How It Works
-- The model classifies the speech emotion given into 6 emotions (Angry, Happy, Sad, Disgusting, Fear, Neutral)
+- The model classifies the speech emotion given into 6 emotions (Angry, Happy, Sad, Disgusting, Fear, Neutral).
 - It returns the highest chance of the emotion and its confidence level.
-- This tool is built with CNN Architecture combined with LSTM Architecture.
+- This model is built with CNN Architecture combined with LSTM Architecture.
+- Please use English to record your voice.
 """
 
 iface = gr.Interface(
@@ -165,7 +166,7 @@ iface = gr.Interface(
     outputs="text",
     live=True,
     title="Speech Emotion Recognition",
-    description="Record your voice to express an emotion and get the predicted emotion. The model only support English",
+    description="Record your voice to express an emotion and get the predicted emotion. The model only support English. Record it about 2-3 s",
     article=article
 )