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 38 different classes of plant diseases")

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))

def predict_class(image) :
    with st.spinner('Loading Model...'):
        classifier_model = keras.models.load_model(r'final1_model.h5', compile = False)

    shape = ((255,255,3))
    model = keras.Sequential([hub.KerasLayer(classifier_model, input_shape = shape)])     # ye bhi kaam kar raha he
    test_image = image.resize((255, 255))
    test_image = keras.preprocessing.image.img_to_array(test_image)
    test_image /= 255.0
    test_image = np.expand_dims(test_image, axis = 0)
    class_name = ["Apple___Apple_scab","Apple___Black_rot",
              "Apple___Cedar_apple_rust","Apple___healthy",
              "Blueberry___healthy",
              "Cherry_(including_sour)___Powdery_mildew",
              "Cherry___healthy",
              "Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot", 
              "Corn_(maize)___Common_rust_",
              "Corn_(maize)___Northern_Leaf_Blight",
              "Corn_(maize)___healthy","Grape___Black_rot",
              "Grape___Esca_(Black_Measles)",
              "Grape___Leaf_blight_(Isariopsis_Leaf_Spot)",
              "Grape___healthy",
              "Orange___Haunglongbing_(Citrus_greening)",
              "Peach___Bacterial_spot",
              "Peach___healthy",
              "Pepper__bell___Bacterial_spot",
              "Pepper,_bell___healthy",
              "Potato___Early_blight",
              "Potato___Late_blight",
              "Potato___healthy",
              "Raspberry___healthy",
              "Soybean___healthy",
              "Squash___Powdery_mildew",
              "Strawberry___Leaf_scorch",
              "Strawberry___healthy",
              "Tomato___Bacterial_spot",
              "Tomato___Early_blight",
              "Tomato___Late_blight",
              "Tomato___Leaf_Mold",
              "Tomato___Septoria_leaf_spot",
              "Tomato___Spider_mites Two-spotted_spider_mite",
              "Tomato___Target_Spot",
              "Tomato___Tomato_Yellow_Leaf_Curl_Virus",
              "Tomato___Tomato_mosaic_virus",
              "Tomato___healthy"]
    prediction = model.predict_generator(test_image)
    confidence = round(100 * (np.max(prediction[0])), 2)
    final_pred = class_name[np.argmax(prediction)]
    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;
}

<div class="footer">
<p align="center"> Developed with ❤ by Mato</p>
</div>
</style>
"""
st.markdown(footer, unsafe_allow_html = True) 
if __name__ == "__main__": 
    main()