--- a
+++ b/pages/2_UNet.py
@@ -0,0 +1,44 @@
+import streamlit as st
+import functions as f
+import components.components as comp
+from pathlib import Path
+import pandas as pd
+
+# TITLE TAB
+st.set_page_config(page_title="UNet")
+
+# FUNCTIONS
+f.modelsCheck()
+description_UNet = Path('models/UNet/description_UNet.md').read_text()
+evaluation_unet_df = pd.read_csv('./models/UNet/csv_logger_UNet.csv')
+test_iou = evaluation_unet_df.iloc[-1]['test_iou']
+testing_time = evaluation_unet_df.iloc[-1]['testing_time']
+# HEADER
+def header_page():
+    st.markdown("<h1 style='text-align: center;'>UNet Architecture</h1>", unsafe_allow_html=True)
+    st.image('models/UNet/UNet.png')
+
+# BODY
+def body_page():
+    st.markdown(description_UNet, unsafe_allow_html=True)
+
+# SIDEBAR
+with st.sidebar:
+    description = "UNet is a convolutional neural network architecture designed for biomedical image segmentation tasks. It consists of an encoding path that extracts features through convolutions and downsampling, and a decoding path that restores the image resolution through upsampling and information merging. UNet's unique architecture effectively combines local image details with global contextual information, resulting in improved segmentation accuracy."
+    model_result = 'The UNet model in the Modern Lung Segmentation application achieved an Intersection over Union (IoU) of {:.3f} during testing, with a training time of {:.3f}s.'.format(test_iou, testing_time)
+
+    st.markdown(f'<p style="font-size:15px; text-align:justify">{description}', unsafe_allow_html=True)
+    st.markdown(f'<p style="font-size:15px; text-align:justify">{model_result}', unsafe_allow_html=True)
+
+
+# FOOTER
+def footer_page():
+    # st.markdown("<p><br></p>", unsafe_allow_html=True)
+    # st.markdown("<div style='margin-top:200px;'></div>", unsafe_allow_html=True)
+    comp.margin_top(200)
+    st.markdown("<p style='text-align: center; font-style:italic;'>Copyright ⓒ 2023 - By Achmad Bauravindah</p>", unsafe_allow_html=True)
+
+if __name__ == '__main__':
+    header_page()
+    body_page()
+    footer_page()
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