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import streamlit as st |
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from PIL import Image |
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import numpy as np |
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import tensorflow as tf |
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from tensorflow import keras |
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import matplotlib.pyplot as plt |
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import tensorflow_hub as hub |
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hide_streamlit_style = """ |
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<style> |
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#MainMenu {visibility: hidden;} |
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footer {visibility: hidden;} |
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</style> |
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""" |
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st.markdown(hide_streamlit_style, unsafe_allow_html = True) |
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st.title('Plant Disease Prediction') |
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st.write("This model is capable of predicting plant disease as a demo") |
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def main() : |
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file_uploaded = st.file_uploader('Choose an image...', type = 'jpg') |
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if file_uploaded is not None : |
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image = Image.open(file_uploaded) |
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st.write("Uploaded Image.") |
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figure = plt.figure() |
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plt.imshow(image) |
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plt.axis('off') |
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st.pyplot(figure) |
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result, confidence = predict_class(image) |
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st.write('Prediction : {}'.format(result)) |
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st.write('Confidence : {}%'.format(confidence)) |
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def predict_class(image) : |
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with st.spinner('Loading Model...'): |
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classifier_model = keras.models.load_model(r'plant_badr_model.h5', compile = False) |
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shape = ((200,200,3)) |
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model = keras.Sequential([hub.KerasLayer(classifier_model, input_shape = shape)]) |
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test_image = image.resize((200, 200)) |
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test_image = keras.preprocessing.image.img_to_array(test_image) |
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test_image /= 256.0 |
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test_image = np.expand_dims(test_image, axis = 0) |
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class_name = list(range(0, 37)) |
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prediction = model.predict_generator(test_image) |
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confidence = round(100 * (np.max(prediction[0])), 2) |
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final_pred = class_name[np.argmax(prediction)] |
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return final_pred, confidence |
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footer = """ |
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<style> |
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a:link , a:visited{ |
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color: white; |
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background-color: transparent; |
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text-decoration: None; |
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} |
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a:hover, a:active { |
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color: red; |
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background-color: transparent; |
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text-decoration: None; |
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} |
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.footer { |
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position: fixed; |
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left: 0; |
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bottom: 0; |
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width: 100%; |
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background-color: transparent; |
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color: black; |
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text-align: center; |
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} |
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</style> |
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""" |
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st.markdown(footer, unsafe_allow_html = True) |
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if __name__ == "__main__": |
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main() |
