Update app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from tensorflow.keras.preprocessing import image
+from PIL import Image
+
+# Load the trained model
+MODEL_PATH = "setosys_dogs_model.h5"
+model = tf.keras.models.load_model(MODEL_PATH)
+
+# Define image preprocessing function
+def preprocess_image(img):
+    img = img.resize((224, 224))  # Resize image to model input size
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+    img_array = img_array / 255.0  # Normalize pixel values
+    return img_array
+
+# Define the prediction function
+def predict_dog_breed(img):
+    img_array = preprocess_image(img)
+    predictions = model.predict(img_array)
+    class_idx = np.argmax(predictions)  # Get class index with highest probability
+    confidence = float(np.max(predictions))  # Get confidence score
+
+    # Define your class labels (replace with your actual class names)
+    class_labels = ["Labrador Retriever", "German Shepherd", "Golden Retriever", "Bulldog", "Poodle"]  
+    predicted_breed = class_labels[class_idx] if class_idx < len(class_labels) else "Unknown"
+
+    return {predicted_breed: confidence}
+
+# Create a Gradio interface
+interface = gr.Interface(
+    fn=predict_dog_breed,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(),
+    title="Dog Breed Classifier",
+    description="Upload an image of a dog to predict its breed.",
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    interface.launch()