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import streamlit as st
from PIL import Image
import pandas as pd
import numpy as np
from keras.models import load_model
model = load_model('./model.h5')
# Classification report data
data = {
"classes": ['Pepper__bell___Bacterial_spot',
'Pepper__bell___healthy',
'Potato___Early_blight',
'Potato___Late_blight',
'Potato___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_YellowLeaf__Curl_Virus',
'Tomato__Tomato_mosaic_virus',
'Tomato_healthy'],
"precision": [0.99, 0.99, 0.98, 0.83, 0.41, 0.99, 0.74, 1.00, 1.00, 0.91, 0.95, 0.60, 1.00, 0.87, 0.91],
"recall": [0.99, 0.86, 1.00, 0.99, 1.00, 0.74, 0.90, 0.83, 0.71, 0.97, 0.64, 0.99, 0.98, 0.98, 1.00],
"f1-score": [0.99, 0.92, 0.99, 0.90, 0.58, 0.85, 0.82, 0.90, 0.83, 0.94, 0.77, 0.74, 0.99, 0.92, 0.95],
"support": [199, 296, 213, 205, 28, 399, 206, 379, 187, 359, 326, 290, 629, 81, 331]
}
# Create a DataFrame
df = pd.DataFrame(data)
def get_predictions(immg):
# from skimage import io
# from keras.preprocessing import image
import keras.utils as image
#path='imbalanced/Scratch/Scratch_400.jpg'
# "C:\Users\prati\Desktop\AML 3104 final project\PlantVillage\Pepper__bell___Bacterial_spot\00f2e69a-1e56-412d-8a79-fdce794a17e4___JR_B.Spot 3132.JPG"
# pth = immg
# show_img=image.load_img(pth, grayscale=False, target_size=(200, 200))
disease_class = data["classes"]
x = image.img_to_array(immg)
x = np.expand_dims(x, axis = 0)
x = np.array(x, 'float32')
x /= 255
custom = model.predict(x)
print(custom[0])
# x = x.reshape([64, 64])
#plt.gray()
# plt.imshow(show_img)
# plt.show()
a=custom[0]
ind=np.argmax(a)
print('Prediction:',disease_class[ind])
return(disease_class[ind])
#User Interface---------------------------------------------------------
pred_flag = False
def main():
st.label_visibility='collapse'
st.title('Plant desise identification')
st.write('# Detecting plant desise using deep learnings dense net model')
# Display the DataFrame as a table in Streamlit
st.header('Evaluation Report:')
# Create two columns
col1, col2 = st.columns(2)
# Add content to the first column
with col1:
st.write('Classification Report:')
st.table(df)
# Add content to the second column
with col2:
st.write('Confussion matrix')
st.image("./confussion_matrix.png",width=650)
uploaded_file = st.file_uploader("", type=['jpg','png','jpeg'])
if uploaded_file is not None:
image = Image.open(uploaded_file)
st.markdown('<p style="text-align: center;"><label>Image : </label></p>',unsafe_allow_html=True)
st.image(image,width=500)
# image = Image.open()
if st.button("Predict"):
resized_image = image.resize((64, 64))
pred_cls = get_predictions(resized_image)
st.markdown('<p style="text-align: center;"><label>Prediction : </label></p>',unsafe_allow_html=True)
# st.image(image,width=900)
st.markdown(pred_cls)
# result =''
# st.success('The output is {}'.format(result))
if __name__ == '__main__': #
main() |