app.py

import streamlit as st 
import pandas as pd
import numpy as np
import tensorflow as tf
from pathlib import Path


st.set_page_config(page_title="Traffic sign prediction", page_icon="⛔", layout='centered', initial_sidebar_state="collapsed")

label_csv = pd.read_csv('./labels.csv', sep=',')
labels = {row[1]['ClassId']:row[1]['Name'] for row in label_csv.iterrows()}
print(labels)

def main():
    # title
    html_temp = """
    <div>
    <h1 style="color:DarkRed;text-align:left;"> Traffic sign prediction  ⛔ </h1>
    </div>
    """
    st.markdown(html_temp, unsafe_allow_html=True)

    col1,col2  = st.columns([2,2])
    
    with col1: 
        with st.expander(" ℹ️ Information", expanded=True):
            st.write("""
            Automatic traffic sign detection is an important role in self-driving car innovation.
            """)

    with col2:
        df = pd.DataFrame()
        upload_file = st.file_uploader("Choose a file of traffic sign.")

        if upload_file is not None:
            bytes_data = upload_file.getvalue()
            fd = open("./img_to_predict.jpg", "wb")
            fd.write(bytes_data)
            fd.close()
            st.image(bytes_data)
        
        if st.button('Predict'):
            loaded_model = tf.keras.models.load_model("./model.h5", compile=True)
            loaded_model.summary()
            if Path("./img_to_predict.jpg").exists():
                img = tf.keras.preprocessing.image.load_img("./img_to_predict.jpg", target_size=(128, 128), interpolation='lanczos')
                img = tf.keras.preprocessing.image.img_to_array(img)
                pred = loaded_model.predict(np.array([img]))
                pred_label = np.argsort(pred)
                for i in pred_label[0]:
                    st.write(f"{labels[i]} : {pred[0][i]*100:0.2f} %")

    st.warning("Note: This A.I application is for educational/demo purposes only and cannot be relied upon.")
    hide_menu_style = """
    <style>
    #MainMenu {visibility: hidden;}
    </style>
    """

hide_menu_style = """
        <style>
        #MainMenu {visibility: hidden;}
        </style>
        """
st.markdown(hide_menu_style, unsafe_allow_html=True)

if __name__ == '__main__':
	main()