Create app.py

app.py

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+import gradio as gr
+import numpy as np
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.image import load_img, img_to_array
+
+# Load the trained model
+model = load_model('model-v1.h5')
+
+# Define class names (replace with your actual class names)
+class_names = [    "Speed limit (20 km/h)",
+    "Speed limit (30 km/h)",
+    "Speed limit (50 km/h)",
+    "Speed limit (60 km/h)",
+    "Speed limit (70 km/h)",
+    "Speed limit (80 km/h)",
+    "End of speed limit (80 km/h)",
+    "Speed limit (100 km/h)",
+    "Speed limit (120 km/h)",
+    "No overtaking",
+    "No overtaking vehicles over 3.5 metric tons",
+    "Priority at next intersection",
+    "Yield",
+    "Stop",
+    "No entry",
+    "No vehicles",
+    "No vehicles except motorcycles",
+    "Only motorcycles allowed",
+    "End of no vehicles",
+    "Restriction ends",
+    "Dangerous curve to the left",
+    "Dangerous curve to the right",
+    "Double curve",
+    "Bumpy road",
+    "Slippery road",
+    "Road narrows on the right",
+    "Roadwork",
+    "Traffic signals",
+    "Pedestrian crossing",
+    "School zone",
+    "Cycle path",
+    "Parking",
+    "No parking",
+    "No stopping",
+    "No pedestrians",
+    "Wild animals crossing",
+    "End of all speed and passing limits",
+    "Turn right ahead",
+    "Turn left ahead",
+    "Ahead only",
+    "Go straight or right",
+    "Go straight or left",
+    "Keep right"]  # Update this with your classes
+
+# Preprocessing function
+def preprocess_image(image):
+    image = image.resize((30, 30))  # Resize to model's input size
+    image_array = img_to_array(image) / 255.0  # Normalize to [0, 1]
+    image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension
+    return image_array
+
+# Prediction function
+def classify_image(image):
+    # Preprocess the image
+    preprocessed_image = preprocess_image(image)
+    
+    # Make prediction
+    predictions = model.predict(preprocessed_image)
+    
+    # Get the class with the highest probability
+    predicted_class = np.argmax(predictions, axis=1)[0]
+    
+    # Get the class name
+    class_name = class_names[predicted_class]
+    
+    return f"Predicted Class: {class_name}"
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=classify_image,  # Function to handle predictions
+    inputs=gr.inputs.Image(type="pil"),  # Accept image input
+    outputs="text",  # Display output as text
+    title="Image Classification",
+    description="Upload an image to classify it into one of the predefined classes."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    interface.launch()