added application file

app.py

@@ -0,0 +1,44 @@
+import gradio as gr
+import tensorflow as tf
+import matplotlib.pyplot as plt
+
+def predict(input_img):
+
+    # Load the saved Keras model
+    model = tf.keras.models.load_model("VGG19.h5")
+    # Preprocess the image
+    # img_0 = tf.keras.utils.load_img(input_image)
+    img_0 = tf.keras.utils.img_to_array(input_img)
+    img_0 = tf.image.resize(img_0, (256, 256))
+    img_1 = tf.expand_dims(img_0, axis = 0)
+
+    class_names = ["bordered", "borderless", "row_bordered"]
+
+    # Make predictions using the model
+    predictions = model.predict(img_1)
+    predicted_label = tf.argmax(predictions, 1).numpy().item()
+
+    for item in predictions :
+      item = tf.round((item*100))
+    
+    fig = plt.figure(1, figsize=(8, 10))
+    plt.axis('off')
+    plt.rcParams.update({'font.size': 24})
+    plt.title(f'prediction : {class_names[predicted_label]}\n\n'
+              f'{item[0]} % {class_names[0]}\n'
+              f'{item[1]} % {class_names[1]}\n'
+              f'{item[2]} % {class_names[2]}\n')
+    plt.imshow(img_0/255)
+    return input_img, plt
+
+    
+output = gr.Plot()
+gradio_app = gr.Interface(
+    predict,
+    inputs=gr.Image(label="table type", sources=['upload', 'webcam'], type="pil"),
+    outputs=[gr.Image(label="Processed Image"), output],
+    title="Table-type Classification"
+)
+
+if __name__ == "__main__":
+    gradio_app.launch(debug=True)