Upload app.py

app.py

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+import cv2
+import numpy as np
+import gradio as gr
+from keras.models import load_model
+
+names = [
+    'Speed limit (20km/h)',
+    'Speed limit (30km/h)',
+    'Speed limit (50km/h)',
+    'Speed limit (60km/h)',
+    'Speed limit (70km/h)',
+    'Speed limit (80km/h)',
+    'End of speed limit (80km/h)',
+    'Speed limit (100km/h)',
+    'Speed limit (120km/h)',
+    'No passing',
+    'No passing for vechiles over 3.5 metric tons',
+    'Road Block',
+    'Priority road',
+    'Yield',
+    'Stop',
+    'No vehicles',
+    'Vechiles over 3.5 metric tons prohibited',
+    'No entry',
+    'General caution',
+    'Double curve',
+    'Bumpy Road',
+    'Slippery road',
+    'Road narrows on the right',
+    'Road Work',
+    'Traffic Signals',
+    'Pedestrians',
+    'Children crossing',
+    'Bicycles crossing',
+    'Beware of ice/snow',
+    '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',
+    'Keep left',
+    'Roundabout mandatory',
+    'End of no passing',
+    'End of no passing by vechiles over 3.5 metric tons'
+]
+
+# Load the saved model
+model = load_model('model.h5')
+
+
+# Preprocess the input image
+def preprocess_image(img):
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    img = cv2.equalizeHist(img)
+    img = img / 255
+    img = cv2.resize(img, (32, 32))
+    img = img.reshape(1, 32, 32, 1)
+    return img
+
+
+# Define the prediction function
+def predict_image(image):
+    preprocessed_image = preprocess_image(image)
+    predictions = model.predict(preprocessed_image)
+    class_index = np.argmax(predictions)
+    class_label = names[class_index]
+    accuracy = predictions[0][class_index]
+    return f"Prediction: {class_label}, Accuracy: {accuracy:.2%}"
+
+
+# Create the Gradio interface
+iface = gr.Interface(fn=predict_image, inputs="image", outputs="text")
+
+# Run the interface
+iface.launch()