import streamlit as st
import pandas as pd
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.efficientnet import preprocess_input
  
def predict(uploaded_file, model, classes):
    img = Image.open(uploaded_file)
    img = img.resize((180, 180))
    img_array = np.array(img)
    img_array = np.expand_dims(img_array, axis=0)
    img_array = preprocess_input(img_array)

    prediction = model.predict(img_array)
    predicted_class_index = np.argmax(prediction)
    predicted_class_label = classes[predicted_class_index]

    st.image(img, use_column_width=True)
    st.write(f"Predicted Vehicle: {predicted_class_label}")

def run():
    st.header('Vehicle Type Recognition 	:busstop:')
    st.write('The objective of this project is to build a machine learning model to classify vehicles into the following categories using Convolutional Neural Networks.')
    st.markdown("""
                - Auto Rickshaw  :auto_rickshaw:
                - Bicycle  :bicyclist:
                - Bus  :bus:
                - Car  :car:
                - Motorcycle  :racing_motorcycle:
                - Truck  :truck:
                - Van 	:minibus:
                """)

    with st.form(key='Form Upload Vehicle Type Recognition'):
        uploaded_files = st.file_uploader("Choose a .JPEG/.JPG/.PNG file", accept_multiple_files=True)

        if uploaded_files:
            for uploaded_file in uploaded_files:

                st.write("filename:", uploaded_file.name)
                model = load_model('vehicleclassifier.h5')
                classes = ['Auto-rickshaw :auto_rickshaw:', 'Bicycle :bicyclist:', 'Bus :bus:', 'Car :car:', 'Motorcycle :racing_motorcycle:', 'Truck :truck:', 'Van 	:minibus:']
                predict(uploaded_file, model, classes)

        st.form_submit_button(label='Submit')

if __name__ == '__main__':
    run()