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| 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() |