main.py

import streamlit as st
import tensorflow as tf
import numpy as np
def model_prediction(test_image):
    model = tf.keras.models.load_model("trained_plant_disease_model.keras")
    image = tf.keras.preprocessing.image.load_img(test_image,target_size=(128,128))
    input_arr = tf.keras.preprocessing.image.img_to_array(image)
    input_arr = np.array([input_arr]) #convert single image to batch
    predictions = model.predict(input_arr)
    return np.argmax(predictions) #return index of max element

#Sidebar
st.sidebar.title("Plant Disease Detection System for Sustainable Agriculture")
app_mode = st.sidebar.selectbox("Select Page",["HOME","DISEASE RECOGNITION"])
#app_mode = st.sidebar.selectbox("Select Page",["Home"," ","Disease Recognition"])

# import Image from pillow to open images
from PIL import Image
img = Image.open("Diseases.png")

# display image using streamlit
# width is used to set the width of an image
st.image(img)

#Main Page
if(app_mode=="HOME"):
    st.markdown("<h1 style='text-align: center;'>Plant Disease Detection System for Sustainable Agriculture", unsafe_allow_html=True)
    
#Prediction Page
elif(app_mode=="DISEASE RECOGNITION"):
    st.header("Plant Disease Detection System for Sustainable Agriculture")
    test_image = st.file_uploader("Choose an Image:")
    if(st.button("Show Image")):
        st.image(test_image,width=4,use_column_width=True)
    #Predict button
    if(st.button("Predict")):
        st.snow()
        st.write("Our Prediction")
        result_index = model_prediction(test_image)
        #Reading Labels
        class_name = ['Apple___Apple_scab', 'Apple___Black_rot', 'Apple___Cedar_apple_rust', 'Apple___healthy',
                    'Blueberry___healthy', 'Cherry_(including_sour)___Powdery_mildew', 
                    'Cherry_(including_sour)___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']
        st.success("Model is Predicting it's a {}".format(class_name[result_index]))