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import streamlit as st
from PIL import Image
import model as classify
import numpy as np
st.title(f'🛡️ Objectives')
sign_names = {
0: 'Speed limit (20km/h)',
1: 'Speed limit (30km/h)',
2: 'Speed limit (50km/h)',
3: 'Speed limit (60km/h)',
4: 'Speed limit (70km/h)',
5: 'Speed limit (80km/h)',
6: 'End of speed limit (80km/h)',
7: 'Speed limit (100km/h)',
8: 'Speed limit (120km/h)',
9: 'No passing',
10: 'No passing for vehicles over 3.5 metric tons',
11: 'Right-of-way at the next intersection',
12: 'Priority road',
13: 'Yield',
14: 'Stop',
15: 'No vehicles',
16: 'Vehicles over 3.5 metric tons prohibited',
17: 'No entry',
18: 'General caution',
19: 'Dangerous curve to the left',
20: 'Dangerous curve to the right',
21: 'Double curve',
22: 'Bumpy road',
23: 'Slippery road',
24: 'Road narrows on the right',
25: 'Road work',
26: 'Traffic signals',
27: 'Pedestrians',
28: 'Children crossing',
29: 'Bicycles crossing',
30: 'Beware of ice/snow',
31: 'Wild animals crossing',
32: 'End of all speed and passing limits',
33: 'Turn right ahead',
34: 'Turn left ahead',
35: 'Ahead only',
36: 'Go straight or right',
37: 'Go straight or left',
38: 'Keep right',
39: 'Keep left',
40: 'Roundabout mandatory',
41: 'End of no passing',
42: 'End of no passing by vehicles over 3.5 metric tons'}
st.title("Traffic Sign Classifier")
uploaded_file = st.file_uploader("Choose an image...", type="jpg")
if uploaded_file is not None:
image = Image.open(uploaded_file)
st.image(image, caption='Uploaded Image', use_column_width=True)
st.write("")
if st.button('predict'):
st.write("Result...")
#label = classify.predict(uploaded_file)
#label = label.item()
#res = sign_names.get(label)
#st.markdown(res)
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