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)