Update app.py

app.py

@@ -58,67 +58,4 @@ iface = gr.Interface(
 )
 
 if __name__ == "__main__":
-    iface.launch()
-
-'''import numpy as np
-import gradio as gr
-from PIL import Image
-
-def ReLU(Z):
-    return np.maximum(Z, 0)
-
-def softmax(Z):
-    A = np.exp(Z) / sum(np.exp(Z))
-    return A
-
-def init_params():
-    W1 = np.random.rand(10, 784) - 0.5
-    b1 = np.random.rand(10, 1) - 0.5
-    W2 = np.random.rand(10, 10) - 0.5
-    b2 = np.random.rand(10, 1) - 0.5
-    return W1, b1, W2, b2
-
-def forward_prop(W1, b1, W2, b2, X):
-    Z1 = W1.dot(X) + b1
-    A1 = ReLU(Z1)
-    Z2 = W2.dot(A1) + b2
-    A2 = softmax(Z2)
-    return Z1, A1, Z2, A2
-
-def get_predictions(A2):
-    return np.argmax(A2, 0)
-
-def make_predictions(X, W1, b1, W2, b2):
-    _, _, _, A2 = forward_prop(W1, b1, W2, b2, X)
-    predictions = get_predictions(A2)
-    return predictions
-
-def predict_digit(img):
-    # Load the trained parameters
-    params = np.load("trained_params.npz", allow_pickle=True)
-    W1, b1, W2, b2 = params["W1"], params["b1"], params["W2"], params["b2"]
-
-    # Convert the sketchpad drawing to grayscale and resize it to (28, 28)
-    img_pil = Image.fromarray(np.uint8(img * 255)).convert("L").resize((28, 28))
-
-    # Convert the image to a NumPy array and normalize it
-    X = np.array(img_pil).reshape((784, 1)) / 255.
-
-    # Get the prediction
-    prediction = make_predictions(X, W1, b1, W2, b2)
-
-    return int(prediction)
-
-iface = gr.Interface(
-    fn=predict_digit,
-    inputs="sketchpad",
-    outputs=gr.outputs.Label(num_top_classes=3),
-    live=True,
-    capture_session=True,
-    title="Handwritten Digit Recognizer",
-    description="Draw a digit using your mouse, and the model will try to recognize it.",
-)
-
-if __name__ == "__main__":
-    iface.launch()
-'''
+    iface.launch()