Update app.py

app.py

@@ -16,11 +16,25 @@ def load_model():
 
 model = load_model()
 
+# Map Class Labels
+CLASS_LABELS = {
+    0: 'Air Conditioner',
+    1: 'Car Horn',
+    2: 'Children Playing',
+    3: 'Dog Bark',
+    4: 'Drilling',
+    5: 'Engine Idling',
+    6: 'Gun Shot',
+    7: 'Jackhammer',
+    8: 'Siren',
+    9: 'Street Music'
+}
+
 # Preprocess audio into a spectrogram
 def preprocess_audio(file_path, n_mels=128, fixed_time_steps=128):
     try:
         y, sr = librosa.load(file_path, sr=None)
-        mel_spectrogram = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=n_mels, fmax=sr/2)
+        mel_spectrogram = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=n_mels, fmax=sr / 2)
         log_spectrogram = librosa.power_to_db(mel_spectrogram, ref=np.max)
         log_spectrogram = log_spectrogram / np.max(np.abs(log_spectrogram))
         if log_spectrogram.shape[1] < fixed_time_steps:
@@ -64,12 +78,13 @@ if uploaded_file is not None:
         st.write("Predicting...")
         spectrogram = np.expand_dims(spectrogram, axis=0)  # Add batch dimension
         predictions = model.predict(spectrogram)
-        predicted_class = np.argmax(predictions, axis=-1)  # Assuming classification
+        predicted_class_index = np.argmax(predictions, axis=-1)[0]
+        predicted_class_label = CLASS_LABELS.get(predicted_class_index, "Unknown")
 
         # Display the results
         st.write("Prediction Results:")
-        st.write(f"Predicted Class: {int(predicted_class[0])}")
-        st.write(f"Raw Model Output: {predictions}")
+        st.write(f"**Predicted Class:** {predicted_class_label} (Index: {predicted_class_index})")
+        st.write(f"**Raw Model Output:** {predictions}")
     else:
         st.write("Failed to process the audio file. Please try again with a different file.")