app.py

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 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)