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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() |