Update app.py

app.py

@@ -147,18 +147,26 @@ 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:.2f})"
+        return f"Predicted emotion: {predicted_emotion} (Confidence: {confidence*100:.4f})"
     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)
+- It returns the highest chance of the emotion and its confidence level.
+- This tool is built with CNN Architecture combined with LSTM Architecture.
+"""
+
 iface = gr.Interface(
     fn=predict,
     inputs=gr.Audio(sources="microphone", type="filepath"),
     outputs="text",
     live=True,
     title="Speech Emotion Recognition",
-    description="Record your voice and get the predicted emotion."
+    description="Record your voice and get the predicted emotion.",
+    article=article
 )
 
 iface.launch()