Create app.py

app.py

@@ -0,0 +1,51 @@
+import streamlit as st
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+
+# Load the model
+@st.cache_resource
+def load_model():
+    return tf.keras.models.load_model('traffic_classifier.h5')
+
+model = load_model()
+
+# Class labels
+class_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 vehicles over 3.5 metric tons', 
+    'Right-of-way at the next intersection', 'Priority road', 'Yield', 'Stop', 
+    'No vehicles', 'Vehicles over 3.5 metric tons prohibited', 'No entry', 
+    'General caution', 'Dangerous curve to the left', 'Dangerous curve to the right', 
+    '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 vehicles over 3.5 metric tons'
+]
+
+st.title('Traffic Sign Classifier')
+
+uploaded_file = st.file_uploader("Upload a traffic sign image", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption='Uploaded Image', use_column_width=True)
+    
+    # Preprocess the image
+    image = image.resize((30, 30))
+    image = np.expand_dims(image, axis=0)
+    image = np.array(image)
+    
+    # Make prediction
+    pred = model.predict(image)
+    predicted_class = np.argmax(pred, axis=-1)[0]
+    
+    st.write(f"Predicted traffic sign: {class_names[predicted_class]}")
+    st.write(f"Confidence: {pred[0][predicted_class]*100:.2f}%")
+
+st.write("Note: This application uses a pre-trained model. For best results, upload images that are 30x30 pixels and similar to those used in the training dataset.")