Update app.py

app.py

@@ -8,9 +8,6 @@ model = tf.keras.models.load_model('capuchin_bird_audio.h5')
 class_names = ['This Is Not A Capuchin bird','It is a capuchin Bird']
 # Function to preprocess input for the model
 def test_preprocess_1(file_path):
-    _, file_extension = os.path.splitext(file_path)
-    
-    if file_extension.lower() == '.wav':
         file_contents = tf.io.read_file(file_path)
         wav, sample_rate = tf.audio.decode_wav(file_contents, desired_channels=1)
         wav = tf.squeeze(wav, axis=-1)
@@ -24,24 +21,19 @@ def test_preprocess_1(file_path):
         spectrogram = tf.expand_dims(spectrogram, axis=2)
         spectrogram = tf.expand_dims(spectrogram, axis=0)
         return spectrogram
-    else:
-        return False
 
 # Function to make predictions
 def predict_audio(wav):
     input_data = test_preprocess_1(wav)
-    if input_data:
-        prediction = model.predict(input_data)
-    
-        # Threshold logic
-        if prediction > 0.5:
-            result = class_names[1]
-        else:
-            result = class_names[0]
-    
-        return result
+    prediction = model.predict(input_data)
+
+    # Threshold logic
+    if prediction > 0.5:
+        result = class_names[1]
     else:
-        return "please upload a wav format"
+        result = class_names[0]
+
+    return result
 
 
 # Gradio Interface
@@ -54,5 +46,4 @@ iface = gr.Interface(
 )
 
 # Launch the interface on localhost
-iface.launch(share=True)
-
+iface.launch(share=True)