create app.py

app.py

@@ -0,0 +1,44 @@
+def complete_model_pipeline(image):
+    """
+    Complete model pipeline for deploying the model using Gradio.
+
+    This function takes an image as input, saves it temporarily to a file,
+    loads the image as a test sample, performs prediction using the model,
+    and then removes the temporarily saved image. The function returns the
+    predicted dog breed label.
+
+    Args:
+        image (np.ndarray): The NumPy array representing the input image.
+
+    Returns:
+        str: The predicted dog breed label.
+
+    Parameters:
+        - image: A NumPy array representing the input image.
+
+    Note: This function is designed to be used with Gradio to deploy the model
+    and obtain predictions interactively.
+
+    """
+
+    # Define the file path to temporarily save the image
+    image_file_path = '/content/drive/MyDrive/Colab Notebooks/dog-vision-project/custom_images/myImage.jpg'
+
+    # Convert the input NumPy array to a Pillow image object and save it as a file
+    image = Image.fromarray(image)
+    image.save(image_file_path)
+
+    # Prepare test data for the model using the saved image file
+    test_data = get_data_batches(X=[image_file_path], test_data=True, batch_size=1, deployment = True)
+
+    # Perform prediction using the model
+    predictions = model.predict(test_data, verbose = 0)
+
+    # Get the index of the highest predicted value and obtain the corresponding breed label
+    predicted_label_index = np.argmax(predictions[0])
+    predicted_breed_label = unique_breeds[predicted_label_index]
+
+    # Remove the temporarily saved image file
+    os.remove(image_file_path)
+
+    return format_underscores(predicted_breed_label)