Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+
+# Load the car brand classifier model
+model_path = "car_brand_classifier_finetuned.keras"
+model = tf.keras.models.load_model(model_path)
+
+labels = ['Hyundai', 'Lexus', 'Mazda', 'Mercedes', 'Opel', 'Skoda', 'Toyota', 'Volkswagen']
+
+# Define function for car brand classification with data augmentation
+def preprocess_image(image):
+    image = Image.fromarray(image.astype('uint8'), 'RGB')
+    image = image.resize((224, 224))
+    image = np.array(image)
+    image = image / 255.0  # Normalize pixel values
+    return image
+
+
+# Prediction function
+def predict_car_brand(image):
+    image = preprocess_image(image)
+    prediction = model.predict(np.expand_dims(image, axis=0))
+    predicted_class = labels[np.argmax(prediction)]
+    confidence = np.round(np.max(prediction) * 100, 2)
+    result = f"Label: {predicted_class}, Confidence: {confidence}%"
+    return result
+
+# Create Gradio interface
+input_image = gr.Image()
+output_text = gr.Textbox(label="Car Brand")
+interface = gr.Interface(fn=predict_car_brand,
+                         inputs=input_image,
+                         outputs=output_text,
+                         description="A car brand classifier using transfer learning and fine-tuning with EfficientNetB0.",
+                         theme="default")
+
+if __name__ == "__main__":
+    interface.launch()