import base64
import streamlit as st
from PIL import Image
import numpy as np
from keras.models import model_from_json
import subprocess


st.markdown('<h1 style="color:white;">Image Classification App</h1>', unsafe_allow_html=True)
st.markdown('<h2 style="color:white;">for classifying **zebras** and **horses**</h2>', unsafe_allow_html=True)

st.cache(allow_output_mutation=True)
def get_base64_of_bin_file(bin_file):
    with open(bin_file, 'rb') as f:
        data = f.read()
    return base64.b64encode(data).decode()

def set_png_as_page_bg(png_file):
    bin_str = get_base64_of_bin_file(png_file) 
    page_bg_img = '''
    <style>
    .stApp {
    background-image: url("data:image/png;base64,%s");
    background-size: cover;
    background-repeat: no-repeat;
    background-attachment: scroll; # doesn't work
    }
    </style>
    ''' % bin_str
    
    st.markdown(page_bg_img, unsafe_allow_html=True)
    return

set_png_as_page_bg('background.webp')
        

# def load_model():
#     # load json and create model
#     json_file = open('model.json', 'r')
#     loaded_model_json = json_file.read()
#     json_file.close()
#     CNN_class_index = model_from_json(loaded_model_json)
#     # load weights into new model
#     model = CNN_class_index.load_weights("model.h5")

#     #model= tf.keras.load_model('model.h5')
#     #CNN_class_index = json.load(open(f"{os.getcwd()}F:\Machine Learning Resources\ZebraHorse\model.json"))
#     return model, CNN_class_index

def load_model():
    # Load the model architecture
    with open('https://github.com/KaburaJ/Binary-Image-classification/blob/main/ZebraHorse/CNN%20Application/model.json', 'r') as f:
        model = model_from_json(f.read())

    # Load the model weights
    model.load_weights('https://github.com/KaburaJ/Binary-Image-classification/blob/main/ZebraHorse/CNN%20Application/model.h5')
    #CNN_class_index = json.load(open(f"{os.getcwd()}F:\Machine Learning Resources\ZebraHorse\model.json"))
    return model


def image_transformation(image):
    #image = Image._resize_dispatcher(image, new_shape=(256, 256))
    #image= np.resize((256,256))
    image = np.array(image)
    np.save('images.npy', image)
    image = np.load('images.npy', allow_pickle=True)

    return image


def image_prediction(image, model):
    image = image_transformation(image=image)
    outputs = model.predict(image)
    _, y_hat = outputs.max(1)
    predicted_idx = str(y_hat.item())
    return predicted_idx

def main():
    
    image_file  = st.file_uploader("Upload an image", type=['jpg', 'jpeg', 'png'])

    if image_file:
       
        left_column, right_column = st.columns(2)
        left_column.image(image_file, caption="Uploaded image", use_column_width=True)
        image = Image.open(image_file)
        image = image_transformation(image=image)
        

        pred_button = st.button("Predict")
        
        model = load_model()
        # label = ['Zebra', 'Horse']
        # label = np.array(label).reshape(1, -1)
        # ohe= OneHotEncoder()
        # labels = ohe.fit_transform(label).toarray()

        if pred_button:
            image_prediction(image, model)
            outputs = model.predict(image)
            _, y_hat = outputs.max(1)
            predicted_idx = str(y_hat.item())
            right_column.title("Prediction")
            right_column.write(predicted_idx)
        

if __name__ == '__main__':
    main()