update

.gitignore

@@ -171,3 +171,7 @@ nacc_test_with_np_cli.csv
 gen_nacc_meta.py
 nacc_variable.csv
 nacc_allowable_code.csv
+input_meta_info.toml
+input_meta_info_old.csv
+include_section.py
+app_backup_1.py

app_backup_1.py

@@ -1,135 +0,0 @@
-import streamlit as st
-import json
-import random
-import pandas as pd
-import pickle
-
-# 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
-    try:   
-        ckpt_path = './ckpt_swinunetr_stripped_MNI.pt'
-        model = adrd.model.ADRDModel.from_ckpt(ckpt_path, device='cpu')
-    except:
-        # ckpt_path = '../adrd_tool_copied_from_sahana/dev/ckpt/ckpt_swinunetr_stripped_MNI.pt'
-        # model = adrd.model.ADRDModel.from_ckpt(ckpt_path, device='cpu')
-        return None
-    return model
-
-@st.cache_resource
-def load_nacc_data():
-    from data.dataset_csv import CSVDataset
-    dat = CSVDataset(
-        dat_file = "./data/test.csv", 
-        cnf_file = "./data/input_meta_info.csv"
-    )
-    return dat
-
-model = load_model()
-dat_tst = load_nacc_data()
-
-def predict_proba(data_dict):
-    pred_dict = model.predict_proba([data_dict])[1][0]
-    return pred_dict
-
-# load NACC testing data
-from data.dataset_csv import CSVDataset
-dat_tst = CSVDataset(
-    dat_file = "./data/test.csv", 
-    cnf_file = "./data/input_meta_info.csv"
-)
-
-# initialize session state for the text input if it's not already set
-if 'input_text' not in st.session_state:
-    st.session_state.input_text = ""
-
-# section 1
-st.markdown("#### About")
-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 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.")
-
-# layout
-layout_l, layout_r = st.columns([1, 1])
-
-# 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 = 300
-        )
-
-        # create three columns
-        left_col, middle_col, right_col = st.columns([3, 4, 1])
-
-        with left_col:
-            sample_button = st.form_submit_button("Random case")
-
-        with right_col:
-            submit_button = st.form_submit_button("Predict")
-    
-with open('./data/nacc_variable_mappings.pkl', 'rb') as file:
-    nacc_mapping = pickle.load(file)
-    
-def convert_dictionary(original_dict, mappings):
-    transformed_dict = {}
-    
-    for key, value in original_dict.items():
-        if key in mappings:
-            new_key, transform_map = mappings[key]
-            
-            # If the value needs to be transformed
-            if value in transform_map:
-                transformed_value = transform_map[value]
-            else:
-                transformed_value = value  # Keep the original value if no transformation is needed
-            
-            transformed_dict[new_key] = transformed_value
-    
-    return transformed_dict
- 
-if sample_button:
-    idx = random.randint(0, len(dat_tst) - 1)
-    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:
-        st.experimental_rerun()
-    
-elif submit_button:
-    try:
-        # Parse the JSON input into a Python dictionary
-        data_dict = json.loads(json_input)
-        data_dict = convert_dictionary(data_dict, nacc_mapping)
-        # print(data_dict)
-        pred_dict = predict_proba(data_dict)
-        with layout_r:
-            st.write("Predicted probabilities:")
-            st.code(json.dumps(pred_dict, indent=2))
-    except json.JSONDecodeError as e:
-        # Handle JSON parsing errors
-        st.error(f"An error occurred: {e}")
-
-# section 3
-st.markdown("#### Feature Table")
-df_input_meta_info = pd.read_csv('./data/input_meta_info.csv')
-st.table(df_input_meta_info)