Update app.py

app.py

@@ -8,12 +8,23 @@ 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."
 
+
+PIL_INTERPOLATION_METHODS = {
+    "nearest": Image.Resampling.NEAREST,
+    "bilinear": Image.Resampling.BILINEAR,
+    "bicubic": Image.Resampling.BICUBIC,
+    "hamming": Image.Resampling.HAMMING,
+    "box": Image.Resampling.BOX,
+    "lanczos": Image.Resampling.LANCZOS,
+}
+
+
 model = tf.keras.saving.load_model("tf_model_mnist")
 
 
-def preprocess(image):
+def preprocess(image, resample_method):
     """ Normalize Gradio image to MNIST format """
-    image = image.resize((28, 28), Image.Resampling.BOX)
+    image = image.resize((28, 28), PIL_INTERPOLATION_METHODS[resample_method])
     img_array = np.asarray(image, dtype=np.float32)
     for i in range(img_array.shape[0]):
         for j in range(img_array.shape[1]):
@@ -30,14 +41,19 @@ def preprocess(image):
     return image_array, new_image
 
 
-def predict(img):
+def predict(img, resample_method):
     img = img["composite"]
-    input_arr, new_image = preprocess(img)
+    input_arr, new_image = preprocess(img, resample_method)
     print("input:", input_arr.shape)
     predictions = model.predict(input_arr)
     return {str(i): predictions[0][i] for i in range(10)}, new_image
 
 
+resample_method = gr.Dropdown(
+    choices=list(PIL_INTERPOLATION_METHODS.keys()),
+    value='bilinear',
+)
+
 input_image = gr.Sketchpad(
         layers=False,
         type="pil",
@@ -47,7 +63,7 @@ demo = gr.Interface(
     predict,
     title=TITLE,
     description=DESCRIPTION,
-    inputs=input_image,
+    inputs=[input_image, resample_method],
     outputs=['label', 'image']
 )