changes1

app.py

@@ -1,9 +1,13 @@
+import os
 import numpy as np
 import librosa
 import pickle
 import tensorflow as tf
 import gradio as gr
 
+# Optional: Suppress TensorFlow logging for cleaner output
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' 
+
 # Load model and label encoder
 model = tf.keras.models.load_model("ann_new_emotion_recognition_model.h5", compile=False)
 with open("new_label_encoder.pkl", "rb") as f:
@@ -41,8 +45,9 @@ def predict_emotion(audio_file):
     predicted_class = np.argmax(predictions[0])
     predicted_emotion = label_encoder.inverse_transform([predicted_class])[0]
 
+    # Output confidences as floats (0-100), not strings
     emotion_probabilities = {
-        label_encoder.inverse_transform([i])[0]: f"{pred * 100:.2f}%"
+        label_encoder.inverse_transform([i])[0]: float(pred * 100)
         for i, pred in enumerate(predictions[0])
     }
 
@@ -52,7 +57,10 @@ def predict_emotion(audio_file):
 iface = gr.Interface(
     fn=predict_emotion,
     inputs=gr.Audio(type="filepath"),
-    outputs=["text", "label"],
+    outputs=[
+        gr.Text(label="Predicted Emotion"),
+        gr.Label(label="Emotion Probabilities")
+    ],
     title="🎤 Emotion Recognition from Audio",
     description="Upload or record audio to identify the emotion being expressed."
 )