Create app.py

app.py

@@ -0,0 +1,54 @@
+import tensorflow as tf
+import gradio as gr
+import numpy as np
+from PIL import Image
+
+TITLE = "Handwritten Digit Recognition Demo"
+
+DESCRIPTION = "This demo employs a basic CNN architecture inspired by [MIT 6.S191’s Lab2 Part1](https://github.com/aamini/introtodeeplearning/blob/master/lab2/Part1_MNIST.ipynb). "\
+              "It achieves about 98% accuracy on the MNIST test dataset but may perform poorly, particularly with digits 8 and 9, likely due to suboptimal image preprocessing."
+
+model = tf.keras.saving.load_model("tf_model_mnist")
+
+
+def preprocess(image):
+    """ Normalize Gradio image to MNIST format """
+    image = image.resize((28, 28), Image.Resampling.HAMMING)
+    img_array = np.asarray(image, dtype=np.float32)
+    for i in range(img_array.shape[0]):
+        for j in range(img_array.shape[1]):
+            alpha = img_array[i, j, 3]
+            if alpha == 0.:
+                img_array[i, j] = [0., 0., 0., 255.]
+            else:
+                img_array[i, j] = [255., 255., 255., 255.]
+
+    new_image = Image.fromarray(img_array.astype(np.uint8), "RGBA")
+    new_image = new_image.convert("L")
+    image_array  = tf.keras.utils.img_to_array(new_image)
+    image_array = (np.expand_dims(image_array, axis=0)/255.).astype(np.float32)
+    return image_array, new_image
+
+
+def predict(img):
+    img = img["composite"]
+    input_arr, new_image = preprocess(img)
+    print("input:", input_arr.shape)
+    predictions = model.predict(input_arr)
+    return {str(i): predictions[0][i] for i in range(10)}, new_image
+
+
+input_image = gr.Sketchpad(
+        layers=False,
+        type="pil",
+    )
+demo = gr.Interface(
+    title=TITLE,
+    description=DESCRIPTION,
+    predict,
+    inputs=input_image,
+    outputs=['label', 'image']
+)
+
+
+demo.launch()