Update app.py

app.py

@@ -24,21 +24,21 @@ bat_classifier_model.compile(optimizer=optimizer, loss='mse', metrics=['accuracy
 # Load the saved model
 bat_classifier_model = tf.keras.models.load_model("bat_classifier_model")
 
+# Preprocess the image
+def preprocess_image(image):
+    processed_image = np.expand_dims(image, axis=0)
+    return processed_image
+
 # Gradio interface function
 def classify_bat(image):
     processed_image = preprocess_image(image)
     prediction = bat_classifier_model.predict(processed_image)[0]
-    class_names = ["Class 1", "Class 2", "Class 3", "Class 4"]
+    class_names = ["Pipistrellus pygmaus", "Noctula nyctalus "]
     return {class_names[i]: float(prediction[i]) for i in range(len(class_names))}
 
-# Preprocess the image
-def preprocess_image(image):
-    # Add your preprocessing steps here
-    processed_image = image  # Placeholder for actual preprocessing
-    processed_image = np.expand_dims(processed_image, axis=0)
-    return processed_image
+inputs = gr.inputs.Image(label="Upload an image")
+outputs = gr.outputs.Label(num_top_classes=2, label="Classification")
+
+gr.Interface(fn=classify_bat, inputs=inputs, outputs=outputs).launch()
+
 
-# Gradio interface
-inputs = gr.inputs.Image()
-outputs = gr.outputs.Label(num_top_classes=4)
-gr.Interface(fn=classify_bat, inputs=inputs, outputs=outputs).launch()