import streamlit as st
from PIL import Image
import numpy as np
import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
import tensorflow_hub as hub

hide_streamlit_style = """
            <style>
            #MainMenu {visibility: hidden;}
            footer {visibility: hidden;}
            </style>
            """

st.markdown(hide_streamlit_style, unsafe_allow_html=True)

st.title('Plant Disease Prediction')
st.write("This model is capable of predicting plant disease as a demo")


def main():
    file_uploaded = st.file_uploader('Choose an image...', type='jpg')
    if file_uploaded is not None:
        image = Image.open(file_uploaded)
        st.write("Uploaded Image.")
        figure = plt.figure()
        plt.imshow(image)
        plt.axis('off')
        st.pyplot(figure)
        result, confidence = predict_class(image)
        st.write('Prediction : {}'.format(result))
        st.write('Confidence : {}%'.format(confidence))

imgSize = 256

def predict_class(image):
    with st.spinner('Loading Model...'):
        classifier_model = keras.models.load_model(
            r'plant_model_v5-beta.h5', compile=False)

    shape = ((imgSize, imgSize, 3))
    model = keras.Sequential(
        [hub.KerasLayer(classifier_model, input_shape=shape)])
    test_image = image.resize((imgSize, imgSize))
    test_image = keras.preprocessing.image.img_to_array(test_image)
    test_image /= 220.0
    test_image = np.expand_dims(test_image, axis=0)
    class_name = {0: 'Apple___Apple_scab', 1: 'Apple___Black_rot', 2: 'Apple___Cedar_apple_rust', 3: 'Apple___healthy', 4: 'Not a plant', 5: 'Blueberry___healthy', 6: 'Cherry___Powdery_mildew', 7: 'Cherry___healthy', 8: 'Corn___Cercospora_leaf_spot Gray_leaf_spot', 9: 'Corn___Common_rust', 10: 'Corn___Northern_Leaf_Blight', 11: 'Corn___healthy', 12: 'Grape___Black_rot',
                  13: 'Grape___Esca_(Black_Measles)', 14: 'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)', 15: 'Grape___healthy', 16: 'Orange___Haunglongbing_(Citrus_greening)', 17: 'Peach___Bacterial_spot', 18: 'Peach___healthy', 19: 'Pepper,_bell___Bacterial_spot', 20: 'Pepper,_bell___healthy', 21: 'Potato___Early_blight', 22: 'Potato___Late_blight', 23: 'Potato___healthy', 24: 'Raspberry___healthy', 25: 'Soybean___healthy', 26: 'Squash___Powdery_mildew', 27: 'Strawberry___Leaf_scorch', 28: 'Strawberry___healthy', 29: 'Tomato___Bacterial_spot', 30: 'Tomato___Early_blight', 31: 'Tomato___Late_blight', 32: 'Tomato___Leaf_Mold', 33: 'Tomato___Septoria_leaf_spot', 34: 'Tomato___Spider_mites Two-spotted_spider_mite', 35: 'Tomato___Target_Spot', 36: 'Tomato___Tomato_Yellow_Leaf_Curl_Virus', 37: 'Tomato___Tomato_mosaic_virus', 38: 'Tomato___healthy'}

    prediction = model.predict_generator(test_image)
    confidence = round(100 * (np.max(prediction[0])), 2)
    final_pred = class_name[np.argmax(prediction)]
    if confidence < 60:
        final_pred = 'Can\'t Predict the disease'
    
    if final_pred != class_name[4] and confidence == 100:
                final_pred = 'Not sure about the disease'

    return final_pred, confidence


footer = """
<style>
a:link , a:visited{
    color: white;
    background-color: transparent;
    text-decoration: None;
}
a:hover,  a:active {
    color: red;
    background-color: transparent;
    text-decoration: None;
}
.footer {
    position: fixed;
    left: 0;
    bottom: 0;
    width: 100%;
    background-color: transparent;
    color: black;
    text-align: center;
}
</style>
"""
st.markdown(footer, unsafe_allow_html=True)
if __name__ == "__main__":
    main()