Update app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+from tensorflow.keras.applications import ResNet152, preprocess_input, decode_predictions
+from tensorflow.keras.preprocessing.image import img_to_array
+from PIL import Image
+import numpy as np
+
+# Load the pre-trained ResNet152 model
+MODEL_PATH = "resnet152-image-classifier"  # Directory where the model is saved
+model = tf.keras.models.load_model(MODEL_PATH)
+
+def predict_image(image):
+    """
+    This function processes the uploaded image and returns the top 3 predictions.
+    """
+    # Preprocess the image
+    image = image.resize((224, 224))  # ResNet152 expects 224x224 input
+    image_array = img_to_array(image)
+    image_array = preprocess_input(image_array)  # Normalize the image
+    image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension
+
+    # Get predictions
+    predictions = model.predict(image_array)
+    decoded_predictions = decode_predictions(predictions, top=3)[0]
+
+    # Format predictions as a dictionary
+    results = {label: f"{confidence * 100:.2f}%" for _, label, confidence in decoded_predictions}
+    return results
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="pil"),  # Accepts an image input
+    outputs=gr.Label(num_top_classes=3),  # Shows top 3 predictions with confidence
+    title="ResNet152 Image Classifier",
+    description="Upload an image, and the model will predict what's in the image.",
+    examples=["dog.jpg", "cat.jpg"],  # Example images for users to test
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    interface.launch()