diff --git a/code/pages/2_HMM-GLM.py b/code/pages/2_HMM-GLM.py
new file mode 100644
index 0000000..0331d85
--- /dev/null
+++ b/code/pages/2_HMM-GLM.py
@@ -0,0 +1,108 @@
+"""Visualize HMM-GLM results from Faeze
+
+Han, ChatGPT
+"""
+import os
+from PIL import Image
+
+import streamlit as st
+import s3fs
+import streamlit_nested_layout
+
+# Set up the S3 bucket and prefix
+bucket_name = "s3://aind-behavior-data/faeze/HMM-GLM"
+
+# Initialize s3fs
+s3 = s3fs.S3FileSystem(anon=False)
+
+# Function to open image from S3
+@st.cache_data(ttl=24*3600)
+def open_image_from_s3(file_key, crop=None, **kwargs):
+    with s3.open(f'{file_key}', 'rb') as f:
+        img = Image.open(f)
+        img = img.crop(crop)
+        
+    st.image(img, **kwargs)
+
+# Get the list of data folders
+data_folders = [os.path.basename(f) for f in 
+                s3.glob(f'{bucket_name}/*')
+                ]
+
+# Data folder selection dropdown
+with st.sidebar:
+    widget_data_folder = st.container()
+    widget_mouse = st.container()
+    widget_n_states = st.container()
+    widget_model_comparison = st.container()
+    
+    if st.button('Reload data from S3'):
+        st.cache_data.clear()
+        st.rerun()
+    
+data_folder_selected = widget_data_folder.selectbox('Select Data Folder', data_folders)
+
+if data_folder_selected:
+    # Get the list of mice folders
+    mice = [os.path.basename(f) for f in
+                     s3.glob(f'{bucket_name}/{data_folder_selected}/*')
+                     if not os.path.basename(f).startswith('.')
+                    ]
+    # Mouse selection dropdown
+    mouse_selected = widget_mouse.selectbox('Select Mouse', mice)
+
+    # Show mouse-wise figures
+    if mouse_selected:
+        mouse_folder = f'{bucket_name}/{data_folder_selected}/{mouse_selected}'
+        
+        with widget_model_comparison:
+            fig_model_comparisons = ['AIC.png', 'BIC.png', 'LL.png']
+
+            for i, fig_model_comparison in enumerate(fig_model_comparisons):
+                img = open_image_from_s3(
+                    f'{mouse_folder}/{fig_model_comparison}',
+                    caption=fig_model_comparison,
+                    )
+
+        # Number of states selection
+        num_states = widget_n_states.selectbox('Select Number of States', 
+                                                ['two_states', 'three_states', 'four_states'],
+                                                index=2, # Default shows four states
+                                                )
+
+        if num_states:
+            num_states_folder = f'{mouse_folder}/{num_states}'
+            fig_states = ['GLM_Weights.png', 'GLM_TM.png', 'frac_occupancy.png', 'frac_occupancy_of_sessions.png']
+
+            cols = st.columns([1, 0.2, 1])
+            with cols[0]:
+                open_image_from_s3(f'{num_states_folder}/GLM_Weights.png', 
+                                   caption='GLM weights')
+            with cols[2]:
+                open_image_from_s3(f'{num_states_folder}/frac_occupancy_of_sessions.png', 
+                                   caption='Fraction occupancy over sessions')
+            with cols[1]:
+                open_image_from_s3(f'{num_states_folder}/frac_occupancy.png', 
+                                   caption='Fraction occupancy (all)')
+                open_image_from_s3(f'{num_states_folder}/GLM_TM.png', 
+                                   caption='Fraction occupancy (all)')    
+                                            
+            # Grouped by selection
+            with st.container(height=2000):
+                cols = st.columns([1.5, 1, 4])
+                grouped_by = cols[0].selectbox('Grouped By', ['grouped_by_sessions', 'grouped_by_sessions_conventional_view', 'grouped_by_states'])
+                num_cols = cols[1].number_input(
+                    label='number of columns',
+                    min_value=1,
+                    max_value=10,
+                    value=2,
+                )
+                
+                if grouped_by:
+                    fig_sessions = s3.glob(f'{num_states_folder}/{grouped_by}/*.png')
+                    cols = st.columns(num_cols)
+                    
+                    for i, fig_session in enumerate(fig_sessions):
+                        with cols[i % num_cols]:
+                            img = open_image_from_s3(fig_session, caption=fig_session)
+