app.py

import streamlit as st
import numpy as np
from PIL import Image
import tensorflow as tf

# Load the model
@st.cache_resource
def load_model():
    return tf.keras.models.load_model('traffic_classifier.h5')

model = load_model()

# Class names
classes = {
    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:'Vehicle > 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 vehicle > 3.5 tons'
}

st.title('Traffic Sign Classifier')

uploaded_file = st.file_uploader("Upload a traffic sign image", type=["jpg", "jpeg", "png"])

if uploaded_file is not None:
    image = Image.open(uploaded_file)
    st.image(image, caption='Uploaded Image', use_column_width=True)
    
    # Preprocess the image
    image = image.resize((30, 30))
    image = image.convert('RGB')  # Convert to RGB
    image = np.array(image)
    image = image / 255.0  # Normalize pixel values
    image = np.expand_dims(image, axis=0)
    
    # Make prediction
    pred = model.predict(image)
    predicted_class = np.argmax(pred, axis=-1)[0]
    
    st.write(f"Predicted traffic sign: {classes[predicted_class]}")
    st.write(f"Confidence: {pred[0][predicted_class]*100:.2f}%")

st.write("Note: This application uses a pre-trained model. For best results, upload images that are similar to those used in the training dataset.")