app.py

import streamlit as st
import pandas as pd
import plotly.graph_objs as go

# Step 1: Load the cleaned sheet based on the selected site
def load_clean_bmd_data(file_path, site):
    sheet_name = ''
    if site == 'Total Hip':
        sheet_name = 'Clean TH avg rise BMD'
    elif site == 'Femoral Neck':
        sheet_name = 'clean FN avg rise BMD'
    elif site == 'Lumbar Spine (L1-L4)':
        sheet_name = 'clean LS avg rise BMD'

    df_clean_bmd_data = pd.read_excel(file_path, sheet_name=sheet_name)

    # Select relevant columns: Drug names and BMD percentage increases
    df_cleaned = df_clean_bmd_data[['Unnamed: 0', '1st', '2nd', '3rd', '4th', '5th', '6th', '8th', '10th']]

    # Rename the first column to 'Drug'
    df_cleaned.columns = ['Drug', '1st', '2nd', '3rd', '4th', '5th', '6th', '8th', '10th']

    # Remove any rows with missing drug names
    df_cleaned = df_cleaned.dropna(subset=['Drug'])

    return df_cleaned

# Step 2: Adjust constants based on patient's BMD and T-score
def adjust_constants(bmd_patient, tscore_patient, c_avg, c_sd):
    # Adjust C_avg and C_sd based on patient's BMD and T-score
    error = tscore_patient - (bmd_patient - c_avg) / c_sd
    c_avg_new = c_avg + error * c_sd
    c_sd_new = (bmd_patient - c_avg) / tscore_patient
    return c_avg_new, c_sd_new

# Step 3: Calculate BMD increase after medication (corrected version)
def calculate_bmd_increase(baseline_bmd, percentage_increase):
    return baseline_bmd * (1 + percentage_increase)

# Step 4: Create a table showing BMD prediction and T-score conversion for each year for each drug
def create_bmd_and_tscore_prediction_tables(df_bmd_data, selected_drugs, bmd_patient, tscore_patient, c_avg_new, c_sd_new):
    years = ['1st', '2nd', '3rd', '4th', '5th', '6th', '8th', '10th']
    drug_tables = {}

    # Loop through each selected drug and generate the prediction table
    for drug in selected_drugs:
        predictions = [('0', bmd_patient, tscore_patient)]  # Add initial value (Year 0)
        for year in years:
            if not pd.isna(df_bmd_data.loc[df_bmd_data['Drug'] == drug, year].values[0]):
                percent_increase = df_bmd_data.loc[df_bmd_data['Drug'] == drug, year].values[0]
                bmd_new = calculate_bmd_increase(bmd_patient, percent_increase)  # Use baseline BMD
                tscore_new = calculate_tscore_from_bmd(bmd_new, c_avg_new, c_sd_new)  # Calculate predicted T-score
                predictions.append((year, bmd_new, tscore_new))
        
        # Create DataFrame for each drug
        drug_table = pd.DataFrame(predictions, columns=['Year', 'Predicted BMD', 'Predicted T-score'])
        drug_tables[drug] = drug_table

    return drug_tables

# Step 5: Plot BMD and T-score as separate graphs side by side for each drug
def display_prediction_tables_and_plots(prediction_tables, baseline_bmd, baseline_tscore):
    # Loop through each drug's prediction table
    for drug, table in prediction_tables.items():
        st.write(f"### {drug} Results")
        
        # Display the prediction table for the current drug
        st.dataframe(table)

        # Create and display separate plots for each drug
        years = list(table['Year'])
        bmd_values = list(table['Predicted BMD'])
        tscore_values = list(table['Predicted T-score'])

        # Create BMD plot
        trace_bmd = go.Scatter(x=years, y=bmd_values, mode='lines+markers', name=f'{drug} (BMD)', line=dict(color='blue'))
        fig_bmd = go.Figure(data=[trace_bmd], layout=go.Layout(title=f'{drug} - Predicted BMD over Time', xaxis=dict(title='Years'), yaxis=dict(title='BMD (g/cm²)')))
        
        # Create T-score plot
        trace_tscore = go.Scatter(x=years, y=tscore_values, mode='lines+markers', name=f'{drug} (T-score)', line=dict(color='green'))
        fig_tscore = go.Figure(data=[trace_tscore], layout=go.Layout(title=f'{drug} - Predicted T-score over Time', xaxis=dict(title='Years'), yaxis=dict(title='T-score')))

        # Display the plots
        col1, col2 = st.columns(2)
        with col1:
            st.plotly_chart(fig_bmd)
        with col2:
            st.plotly_chart(fig_tscore)

        # Step 6: Check if goal is achieved and show the predicted BMD and T-score for each year
        goal_achieved = False
        for i, row in table.iterrows():
            if row['Predicted T-score'] >= -2.49:
                st.success(f"Goal achieved for {drug} at year {row['Year']} with T-score = {row['Predicted T-score']:.2f}")
                goal_achieved = True
                break
        if not goal_achieved:
            st.warning(f"Goal not achieved for {drug}")

# Step 6: Calculate T-score from adjusted BMD
def calculate_tscore_from_bmd(bmd_patient, c_avg, c_sd):
    return (bmd_patient - c_avg) / c_sd

# Main function to load data, run the application, and plot results with T-score labels
def main_with_separate_tables(file_path, bmd_patient, tscore_patient, C_avg_lunar, C_sd_lunar, selected_drugs, site):
    # Step 1: Load and clean BMD data from the selected site (Total Hip, Femoral Neck, Lumbar Spine)
    df_bmd_data = load_clean_bmd_data(file_path, site)

    # Step 2: Adjust constants based on the patient's data
    c_avg_new, c_sd_new = adjust_constants(bmd_patient, tscore_patient, C_avg_lunar, C_sd_lunar)

    # Step 4: Create separate prediction tables for each selected drug
    prediction_tables = create_bmd_and_tscore_prediction_tables(df_bmd_data, selected_drugs, bmd_patient, tscore_patient, c_avg_new, c_sd_new)

    # Display baseline BMD and T-score
    st.write(f"Baseline: BMD = {bmd_patient:.3f}, T-score = {tscore_patient:.2f}")

    # Step 5: Display prediction tables and plots for each drug
    display_prediction_tables_and_plots(prediction_tables, bmd_patient, tscore_patient)

# Streamlit UI
def main():
    st.title("BMD and T-score Prediction Tool")

    # Input patient data
    bmd_patient = st.number_input("Initial BMD", min_value=0.0, max_value=2.0, value=0.635, step=0.001, format="%.3f")
    tscore_patient = st.number_input("Initial T-score", min_value=-5.0, max_value=2.0, value=-2.5, step=0.01, format="%.2f")

    # Site options (Total Hip, Femoral Neck, Lumbar Spine)
    site_options = ['Total Hip', 'Femoral Neck', 'Lumbar Spine (L1-L4)']
    site = st.selectbox("Select site", site_options)

    # Drug options
    drug_options = ['Teriparatide', 'Teriparatide + Denosumab', 'Denosumab', 'Denosumab + Teriparatide',
                    'Romosozumab', 'Romosozumab + Denosumab', 'Romosozumab + Alendronate',
                    'Romosozumab + Ibandronate', 'Romosozumab + Zoledronate', 'Alendronate',
                    'Risedronate', 'Ibandronate oral', 'Ibandronate IV (3mg)', 'Zoledronate']

    # Add option to select multiple drugs
    selected_drugs = st.multiselect("Select drugs to compare", drug_options)

    # Set constants for each site
    if site == 'Total Hip':
        C_avg_lunar = 0.95  # Example: Average BMD for Total Hip from Excel (Lunar)
        C_sd_lunar = 0.12
    elif site == 'Femoral Neck':
        C_avg_lunar = 0.905  # Example: Average BMD for Femoral Neck from Excel (Lunar)
        C_sd_lunar = 0.116   # Example: SD for Femoral Neck (Lunar)
    elif site == 'Lumbar Spine (L1-L4)':
        C_avg_lunar = 1.097  # Example: Average BMD for Lumbar Spine (L1-L4) from Excel (Lunar)
        C_sd_lunar = 0.128   # Example: SD for Lumbar Spine (L1-L4) (Lunar)

    # Example file path
    file_path = "BMD constant calculator.xlsx"

    # ตรวจสอบว่ามีการเลือกยาแล้วหรือไม่
    if len(selected_drugs) == 0:
        st.warning("Please select at least one drug to compare.")
    else:
        # Run prediction and plot results
        if st.button("Predict"):
            main_with_separate_tables(file_path, bmd_patient, tscore_patient, C_avg_lunar, C_sd_lunar, selected_drugs, site)

if __name__ == "__main__":
    main()