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import streamlit as st
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from PIL import Image
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import numpy as np
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import tensorflow as tf
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model = tf.keras.models.load_model('model.h5')
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classes = { 0:'Speed limit (20km/h)',
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1:'Speed limit (30km/h)',
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2:'Speed limit (50km/h)',
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3:'Speed limit (60km/h)',
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4:'Speed limit (70km/h)',
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5:'Speed limit (80km/h)',
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6:'End of speed limit (80km/h)',
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7:'Speed limit (100km/h)',
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8:'Speed limit (120km/h)',
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9:'No passing',
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10:'No passing veh over 3.5 tons',
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11:'Right-of-way at intersection',
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12:'Priority road',
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13:'Yield',
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14:'Stop',
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15:'No vehicles',
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16:'Veh > 3.5 tons prohibited',
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17:'No entry',
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18:'General caution',
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19:'Dangerous curve left',
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20:'Dangerous curve right',
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21:'Double curve',
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22:'Bumpy road',
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23:'Slippery road',
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24:'Road narrows on the right',
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25:'Road work',
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26:'Traffic signals',
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27:'Pedestrians',
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28:'Children crossing',
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29:'Bicycles crossing',
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30:'Beware of ice/snow',
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31:'Wild animals crossing',
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32:'End speed + passing limits',
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33:'Turn right ahead',
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34:'Turn left ahead',
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35:'Ahead only',
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36:'Go straight or right',
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37:'Go straight or left',
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38:'Keep right',
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39:'Keep left',
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40:'Roundabout mandatory',
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41:'End of no passing',
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42:'End no passing veh > 3.5 tons' }
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st.title('German Traffic Sign Recognition - Metehan Ayhan')
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st.write("Upload an image of a traffic sign to predict its class.")
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uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
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if uploaded_file is not None:
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image = Image.open(uploaded_file)
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st.image(image, caption='Uploaded Traffic Sign.', use_column_width=True)
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st.write("")
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st.write("Classifying...")
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image = image.resize((32, 32))
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image = np.array(image)
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image = np.expand_dims(image, axis=0)
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predictions = model.predict(image)
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predicted_class = np.argmax(predictions[0])
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st.write(f"Prediction: {classes[predicted_class]}")
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