Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+
+# Load the Keras model
+model = tf.keras.models.load_model("denis_mnist_cnn_model.h5")
+
+# Define a function to preprocess input and make predictions
+def predict(image):
+    # Preprocess the image (resize, normalize, etc.)
+    image = tf.image.resize(image, (224, 224))  # Example: Resize to 224x224
+    image = np.expand_dims(image, axis=0)  # Add batch dimension
+    image = image / 255.0  # Normalize pixel values
+
+    # Perform prediction
+    prediction = model.predict(image)
+    return {"prediction": prediction.tolist()}
+    
+
+# Create a Gradio interface
+interface = gr.Interface(
+    fn=predict, 
+    inputs="image",  # Text input for comma-separated values
+    outputs="json"  # JSON output for prediction results
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    interface.launch()