File size: 2,122 Bytes
c2fac7e 5862545 81b9622 5862545 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | import streamlit as st
import tensorflow as tf
import numpy as np
from PIL import Image
# Load your saved model
# Make sure your 'traffic_sign_model.keras' file is in the same folder
model = tf.keras.models.load_model('src/traffic_sign_model.keras')
# Your class labels dictionary
sign_labels = {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 veh over 3.5 tons', 11:'Right-of-way at intersection',
12:'Priority road', 13:'Yield', 14:'Stop', 15:'No vehicles', 16:'Veh > 3.5 tons prohibited',
17:'No entry', 18:'General caution', 19:'Dangerous curve left', 20:'Dangerous curve 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 speed + 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 no passing veh > 3.5 tons'}
st.title("Traffic Sign Classifier")
uploaded_file = st.file_uploader("Choose a traffic sign image...", type=["jpg", "png"])
if uploaded_file is not None:
image = Image.open(uploaded_file)
st.image(image, caption='Uploaded Image', use_column_width=True)
# Preprocess image to match model input
image = image.resize((224, 224))
img_array = np.array(image) / 255.0
img_array = np.expand_dims(img_array, axis=0)
# Predict
predictions = model.predict(img_array)
predicted_class = np.argmax(predictions, axis=1)[0]
st.write(f"Prediction: {sign_labels[predicted_class]}") |