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| 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("AgriSens") | |
| app_mode = st.sidebar.selectbox("Select Page",["HOME","DISEASE RECOGNITION"]) | |
| #app_mode = st.sidebar.selectbox("Select Page",["Home","About","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;'>SMART DISEASE DETECTION", unsafe_allow_html=True) | |
| #Prediction Page | |
| elif(app_mode=="DISEASE RECOGNITION"): | |
| st.header("DISEASE RECOGNITION") | |
| 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])) |