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import streamlit as st
import tensorflow as tf
import numpy as np
def model_prediction(test_image):
model = tf.keras.models.load_model("trained_plant_disease_model.keras")
image = tf.keras.preprocessing.image.load_img(test_image,target_size=(128,128))
input_arr = tf.keras.preprocessing.image.img_to_array(image)
input_arr = np.array([input_arr]) #convert single image to batch
predictions = model.predict(input_arr)
return np.argmax(predictions) #return index of max element
#Sidebar
st.sidebar.title("AgriSens")
app_mode = st.sidebar.selectbox("Select Page",["HOME","DISEASE RECOGNITION"])
#app_mode = st.sidebar.selectbox("Select Page",["Home","About","Disease Recognition"])
# import Image from pillow to open images
from PIL import Image
img = Image.open("Diseases.png")
# display image using streamlit
# width is used to set the width of an image
st.image(img)
#Main Page
if(app_mode=="HOME"):
st.markdown("<h1 style='text-align: center;'>SMART DISEASE DETECTION", unsafe_allow_html=True)
#Prediction Page
elif(app_mode=="DISEASE RECOGNITION"):
st.header("DISEASE RECOGNITION")
test_image = st.file_uploader("Choose an Image:")
if(st.button("Show Image")):
st.image(test_image,width=4,use_column_width=True)
#Predict button
if(st.button("Predict")):
st.snow()
st.write("Our Prediction")
result_index = model_prediction(test_image)
#Reading Labels
class_name = ['Apple___Apple_scab', 'Apple___Black_rot', 'Apple___Cedar_apple_rust', 'Apple___healthy',
'Blueberry___healthy', 'Cherry_(including_sour)___Powdery_mildew',
'Cherry_(including_sour)___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']
st.success("Model is Predicting it's a {}".format(class_name[result_index])) |