Create app.py

app.py

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+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import gradio as gr
+
+# Load the Keras model
+model = tf.keras.models.load_model("car_brand_classifier_final.h5")
+
+# Define the preprocessing function
+def preprocess_image(image):
+    image = image.resize((299, 299))  # Resize to match model input size
+    image = np.array(image) / 255.0  # Normalize pixel values
+    image = np.expand_dims(image, axis=0)  # Add batch dimension
+    return image
+
+# Define the prediction function
+def predict(image):
+    # Preprocess the image
+    processed_image = preprocess_image(image)
+
+    # Make a prediction
+    predictions = model.predict(processed_image)
+    predicted_class = np.argmax(predictions, axis=1)[0]
+
+    # Return the result
+    return f"Predicted class: {predicted_class}"
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs="image",
+    outputs="text",
+    title="Car Vision",
+    description="Upload an image of a car to classify its brand."
+)
+
+# Launch the app
+iface.launch()