Update app.py

app.py

@@ -0,0 +1,47 @@
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from tensorflow.keras.applications.resnet50 import preprocess_input
+from tensorflow.keras.utils import load_img, img_to_array
+
+# Load your trained model
+model = tf.keras.models.load_model("denis_mnist_cnn_model_resnet50_v1.h5")  # Ensure you upload this file to Hugging Face Spaces
+
+# Define a function to preprocess the image
+def preprocess_image(image):
+    """
+    Preprocesses the uploaded image for prediction.
+    """
+    image = image.resize((128, 128))  # Resize to match the model input size
+    image = img_to_array(image)  # Convert PIL image to NumPy array
+    image = preprocess_input(image)  # Normalize for ResNet50
+    image = np.expand_dims(image, axis=0)  # Add batch dimension
+    return image
+
+# Define the prediction function
+def predict(image):
+    """
+    Accepts an image, preprocesses it, and returns the predicted label.
+    """
+    processed_image = preprocess_image(image)
+    predictions = model.predict(processed_image)
+    predicted_class = np.argmax(predictions, axis=1)[0]  # Get the class index
+    confidence = np.max(predictions)  # Get confidence score
+    return f"Predicted Class: {predicted_class}, Confidence: {confidence:.2f}"
+
+# Create a Gradio interface
+interface = gr.Interface(
+    fn=predict,  # The prediction function
+    inputs=gr.inputs.Image(type="pil", label="Upload an Image"),  # Input: Image
+    outputs="text",  # Output: Text
+    title="MNIST ResNet50 Classifier",
+    description="Upload an image to classify it using the trained ResNet50 model.",
+    examples=[
+        ["example_images/example1.png"],  # Add paths to example images in your Hugging Face repository
+        ["example_images/example2.png"]
+    ],
+)
+
+# Launch the app
+if __name__ == "__main__":
+    interface.launch()