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import streamlit as st
import pickle
from Prediction import Prediction
from transformers import TFAutoModel
# resnet_model = pickle.load(open('models/ResNet01.pkl','rb'))
# cnn_model = pickle.load(open('models/CNNModel2.pkl','rb'))
# inc_model = pickle.load(open('models/Inception01.pkl','rb'))
resnet_model = TFAutoModel.from_pretrained("yashpat85/ResNet01")
def show_error_popup(message):
st.error(message, icon="🚨")
st.set_page_config(layout="wide")
st.title('Kidney Disease Classification using CNN')
st.markdown('By 22DCS079 & 22DCS085')
st.header('Add Ct Scan Image')
uploaded_file = st.file_uploader("Choose a ct scan image", type=["jpg", "png", "jpeg"])
st.header("Available Models")
option = st.selectbox(
"Available Models",
("ResNet", "CNN","InceptionNet"),
)
pm = Prediction()
col1, col2= st.columns(2)
if uploaded_file is not None:
with col1:
image_data = uploaded_file.read()
st.image(image_data, caption="Uploaded Image")
with col2:
if option=="CNN":
p = pm.predict_image(cnn_model, image_data)
elif option=="ResNet":
p = pm.predict_image(resnet_model, image_data)
elif option=="InceptionNet":
p = pm.predict_image(inc_model, image_data)
else:
p = "Other Models are still under training due to overfitting"
print(p)
if p=='Normal':
st.markdown("""
<style>
.big-font {
display: flex;
align-items:center;
justify-content: center;
font-size:50px !important;
color:green;
height: 50vh;
}
</style>
""", unsafe_allow_html=True)
st.markdown(f'<div class="big-font">{p}</div>', unsafe_allow_html=True)
else:
st.markdown("""
<style>
.big-font {
display: flex;
align-items:center;
justify-content: center;
font-size:50px !important;
color:red;
height: 50vh;
}
</style>
""", unsafe_allow_html=True)
st.markdown(f'<div class="big-font">{p}</div>', unsafe_allow_html=True)
else:
show_error_popup("Please Upload Image...")
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