Create app.py

app.py

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+import gradio as gr
+
+input_module1 = gr.inputs.Image(label = "Input Image", shape=(28, 28), image_mode = 'L')
+input_module2 = gr.inputs.Slider(1, 11, step=2, label = "K")
+output_module = gr.outputs.Textbox(label = "Predicted Image")
+
+def mnist_pipeline(input1, input2):
+  img_flatten = input1.reshape(input1.shape[0]*input1.shape[1])
+  new_feature = np.array([img_flatten])
+  with open('knn_aug.pkl', 'rb') as f:
+      mnist_pred = pickle.load(f)
+  ans = mnist_pred.predict(new_feature)
+  return ans[0]
+gr.Interface(fn=mnist_pipeline, 
+             inputs = [input_module1, input_module2], 
+             outputs = [output_module]).launch(debug=True)