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