ok

app.py

@@ -2,12 +2,29 @@ import streamlit as st
 import json
 import random
 
+# set page configuration to wide mode
+st.set_page_config(layout="wide")
+
+st.markdown("""
+<style>
+.bounding-box {
+    border: 2px solid #4CAF50;  # Green border
+    border-radius: 5px;         # Rounded corners
+    padding: 10px;              # Padding inside the box
+    margin: 10px;               # Space outside the box
+}
+</style>
+""", unsafe_allow_html=True)
+
 @st.cache_resource
 def load_model():
     import adrd
-    ckpt_path = './ckpt_densenet_emb_encoder_2_AUPR.pt'
-    # ckpt_path = '../adrd_tool_copied_from_sahana/dev/ckpt/ckpt_densenet_emb_encoder_2_AUPR.pt'
-    model = adrd.model.ADRDModel.from_ckpt(ckpt_path, device='cpu')
+    try:   
+        ckpt_path = './ckpt_densenet_emb_encoder_2_AUPR.pt'
+        model = adrd.model.ADRDModel.from_ckpt(ckpt_path, device='cpu')
+    except:
+        ckpt_path = '../adrd_tool_copied_from_sahana/dev/ckpt/ckpt_densenet_emb_encoder_2_AUPR.pt'
+        model = adrd.model.ADRDModel.from_ckpt(ckpt_path, device='cpu')
     return model
 
 @st.cache_resource
@@ -37,28 +54,39 @@ dat_tst = CSVDataset(
 if 'input_text' not in st.session_state:
     st.session_state.input_text = ""
 
-# Create a form for user input
-with st.form("json_input_form"):
-    st.write("Please enter the input features in the textbox below, formatted as a JSON dictionary. Click the \"Random NACC Example\" button to populate the textbox with a randomly selected case from the NACC testing dataset. Use the \"Predict\" button to submit your input to the model, which will then provide probability predictions for mental status and all 10 etiologies.")
-    json_input = st.text_area(
-        "Please enter JSON-formatted input features:", 
-        value = st.session_state.input_text,
-        height = 250
-    )
+# section 1
+st.markdown("#### About ADRD")
+st.markdown("Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an AI model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations, and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals.")
+
+# section 2
+st.markdown("#### Demo")
+st.markdown("Please enter the input features in the textbox below, formatted as a JSON dictionary. Click the \"**Random NACC Case**\" button to populate the textbox with a randomly selected case from the NACC testing dataset. Use the \"**Predict**\" button to submit your input to the model, which will then provide probability predictions for mental status and all 10 etiologies.")
 
-    # create three columns
-    left_col, middle_col, right_col = st.columns([3, 4, 1])
+# layout
+layout_l, layout_r = st.columns([1, 1])
 
-    with left_col:
-        sample_button = st.form_submit_button("Random NACC Case")
+# create a form for user input
+with layout_l:
+    with st.form("json_input_form"):
+        json_input = st.text_area(
+            "Please enter JSON-formatted input features:", 
+            value = st.session_state.input_text,
+            height = 250
+        )
 
-    with right_col:
-        submit_button = st.form_submit_button("Predict")
+        # create three columns
+        left_col, middle_col, right_col = st.columns([3, 4, 1])
 
+        with left_col:
+            sample_button = st.form_submit_button("Random NACC Case")
+
+        with right_col:
+            submit_button = st.form_submit_button("Predict")
+            
 if sample_button:
     idx = random.randint(0, len(dat_tst) - 1)
-    example = dat_tst[idx][0]    
-    st.session_state.input_text = json.dumps(example)
+    random_case = dat_tst[idx][0]    
+    st.session_state.input_text = json.dumps(random_case, indent=2)
 
     # reset input text after form processing to show updated text in the input box
     if 'input_text' in st.session_state:
@@ -69,8 +97,12 @@ elif submit_button:
         # Parse the JSON input into a Python dictionary
         data_dict = json.loads(json_input)
         pred_dict = predict_proba(data_dict)
-        st.write("Predicted probabilities:")
-        st.json(pred_dict)
+        with layout_r:
+            st.write("Predicted probabilities:")
+            st.json(pred_dict)
     except json.JSONDecodeError as e:
         # Handle JSON parsing errors
         st.error(f"An error occurred: {e}")
+
+# section 3
+