| 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() | |