--- a
+++ b/pages/3_AttUNet.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="AttUNet")
+
+# FUNCTIONS
+f.modelsCheck()
+description_AttUNet = Path('models/AttUNet/description_AttUNet.md').read_text()
+evaluation_AttUNet_df = pd.read_csv('./models/AttUNet/csv_logger_AttUNet.csv')
+test_iou = evaluation_AttUNet_df.iloc[-1]['test_iou']
+testing_time = evaluation_AttUNet_df.iloc[-1]['testing_time']
+# HEADER
+def header_page():
+    st.markdown("<h1 style='text-align: center;'>AttUNet Architecture</h1>", unsafe_allow_html=True)
+    st.image('models/AttUNet/AttUNet.png')
+
+# BODY
+def body_page():
+    st.markdown(description_AttUNet, unsafe_allow_html=True)
+
+# SIDEBAR
+with st.sidebar:
+    description = "The Attention UNet is an extension of the UNet architecture that incorporates attention mechanisms to improve image segmentation performance. It utilizes attention modules in both the encoding and decoding paths to focus on relevant image regions and enhance localization accuracy. With the integration of attention mechanisms, the Attention UNet addresses the limitations of the traditional UNet in capturing long-range dependencies and handling class imbalance, resulting in more accurate segmentations."
+    model_result = 'The AttUNet 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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