Create app_test.py

app_test.py

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+import tensorflow as tf
+import gradio as gr
+import numpy as np
+
+# Load the CNN model from the .h5 file
+model = tf.keras.models.load_model('TP_MNIST_CNN_model.h5')
+
+def predict_digit(image):
+    # Preprocess the input image
+    image = np.expand_dims(image, axis=0)  # Add batch dimension
+    image = image / 255.0  # Normalize pixel values
+
+    # Make predictions
+    predictions = model.predict(image)
+
+    # Get the predicted digit
+    predicted_digit = np.argmax(predictions)
+
+    return predicted_digit
+
+# Define Gradio interface
+gr.Interface(
+    title="Reconnaissance d'écriture manuscrite des données MNIST by PSW",
+    fn=predict_digit, 
+    inputs=gr.Sketchpad(label="Desinner le chiffre ici",  height=300, width=800),
+    outputs="number",
+    live=True
+).launch(debug=True)