changes3

app.py

@@ -225,8 +225,8 @@ with gr.Blocks(title="🎤 Emotion Recognition from Audio", theme=gr.themes.Soft
         3. A trained neural network predicts the emotion
         4. View the results with detailed visualizations:
            - **Waveform**: Shows the audio signal over time
-           - **Mel Spectrogram**: Visual representation of the audio's frequency content
-           - **Radar Chart**: Probability distribution across all emotion categories
+           - **Mel Spectrogram**: Visual representation of the audio's frequency content 
+           - **Radar Chart**: Probability distribution across all diff emotion categories
         
         ### 🎭 Supported Emotions:
         Depending on your model training, this may include emotions like: Happy, Sad, Angry, Fear, Disgust, Surprise, Neutral, and others.