Create app.py

app.py

@@ -0,0 +1,60 @@
+import numpy as np
+import pandas as pd
+import gradio as gr
+from matplotlib import pyplot as plt
+
+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(input_data):
+    # 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"]
+
+    
+    # Reshape the input data to match the neural network architecture
+    X = input_data.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=gr.inputs.Image(shape=(28, 28)),
+    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()