import streamlit as st 
from PIL import Image
import model as classify 
import numpy as np
from modules.nav import Navbar

Navbar()

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)