Adding pages

Home.py

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+import streamlit as st 
+from PIL import Image
+import model as classify 
+import numpy as np
+
+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)

pages/1_Show_Images.py

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+import streamlit as st
+
+x = st.slider('Select a value')
+st.write(x, 'squared is', x * x)

pages/2_Predict_Images.py

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+import streamlit as st
+
+x = st.slider('Select a value')
+st.write(x, 'squared is', x * x)