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abdulmatinomotoso
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7a7f340
1
Parent(s):
1e7fbda
Create app.py
Browse files
app.py
ADDED
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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 38 different classes of plant diseases")
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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'final1_model.h5', compile = False)
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shape = ((255,255,3))
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model = keras.Sequential([hub.KerasLayer(classifier_model, input_shape = shape)]) # ye bhi kaam kar raha he
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test_image = image.resize((255, 255))
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test_image = keras.preprocessing.image.img_to_array(test_image)
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test_image /= 255.0
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test_image = np.expand_dims(test_image, axis = 0)
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class_name = ["Apple___Apple_scab","Apple___Black_rot",
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"Apple___Cedar_apple_rust","Apple___healthy",
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"Blueberry___healthy",
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"Cherry_(including_sour)___Powdery_mildew",
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"Cherry___healthy",
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"Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot",
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"Corn_(maize)___Common_rust_",
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"Corn_(maize)___Northern_Leaf_Blight",
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"Corn_(maize)___healthy","Grape___Black_rot",
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"Grape___Esca_(Black_Measles)",
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"Grape___Leaf_blight_(Isariopsis_Leaf_Spot)",
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"Grape___healthy",
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"Orange___Haunglongbing_(Citrus_greening)",
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"Peach___Bacterial_spot",
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"Peach___healthy",
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"Pepper__bell___Bacterial_spot",
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"Pepper,_bell___healthy",
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"Potato___Early_blight",
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"Potato___Late_blight",
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"Potato___healthy",
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"Raspberry___healthy",
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"Soybean___healthy",
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"Squash___Powdery_mildew",
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"Strawberry___Leaf_scorch",
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"Strawberry___healthy",
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"Tomato___Bacterial_spot",
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"Tomato___Early_blight",
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"Tomato___Late_blight",
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"Tomato___Leaf_Mold",
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"Tomato___Septoria_leaf_spot",
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"Tomato___Spider_mites Two-spotted_spider_mite",
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"Tomato___Target_Spot",
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"Tomato___Tomato_Yellow_Leaf_Curl_Virus",
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"Tomato___Tomato_mosaic_virus",
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"Tomato___healthy"]
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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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<div class="footer">
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<p align="center"> Developed with ❤ by Mato</p>
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</div>
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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()
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