Update app.py

app.py

@@ -3,37 +3,19 @@ import gradio as gr
 import tensorflow as tf
 import numpy as np
 
-# Title and description for the interface
-title = "Welcome to your first sketch recognition app!"
-head = "<center>The robot was trained to classify numbers (0 to 9). To test it, write your number in the space provided.</center>"
-
 
 # Load the trained model
 model = tf.keras.models.load_model("number_recognition_model_colab.keras")
 
 
-def recognize_digit(image):
-    if image is not None:
-        image = image.reshape((1, 28, 28, 1)).astype('float32')/255
-
-        prediction = model.predict(image)
-
-        return {str(i): float(prediction[0][i]) for i in range(10)}
-    else:
-        return ''
-
 
-# Build and launch the Gradio interface
-demo = gr.Interface(
-    fn = recognize_digit,
-    inputs = gr.Image(
-        shape=(28, 28), 
-        image_mode='L', 
-        invert_colors=True, 
-        source='canvas',
-        brush_radius=1, 
-        tool="color-sketch",
-    ),
-    outputs = gr.Label(num_top_classes=3),
-    live = True
-).launch(share=True)
+def recognize_digit(image):
+    prediction = model.predict(np.reshape(image, (1, 28, 28))).tolist()[0]
+    return {str(i): prediction[i] for i in range(10)}
+
+sketchpad = gr.Sketchpad(shape=(28, 28))
+gr.Interface(fn=recognize_digit, 
+             inputs=sketchpad,
+             outputs="label",
+             title="Handwritten Digits Classifier",
+             description="This app uses lenet5 for handwritten digits classification").launch()