app.py

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

# Step 1: Load the cleaned sheet "Clean TH avg rise BMD" and display it
def load_clean_bmd_data(file_path):
    sheet_clean_th_avg_rise = 'Clean TH avg rise BMD'
    df_clean_th_avg_rise = pd.read_excel(file_path, sheet_name=sheet_clean_th_avg_rise)

    # Select relevant columns: Drug names and BMD percentage increases
    df_cleaned = df_clean_th_avg_rise[['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
def create_bmd_and_tscore_prediction_table(df_bmd_data, drug_selected, bmd_patient, c_avg_new, c_sd_new):
    years = ['1st', '2nd', '3rd', '4th', '5th', '6th', '8th', '10th']
    predictions = []

    for drug in drug_selected:
        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((drug, year, bmd_new, tscore_new))

    return pd.DataFrame(predictions, columns=['Drug', 'Year', 'Predicted BMD', 'Predicted T-score'])

# Step 5: Plot BMD and T-score as separate graphs side by side
def plot_bmd_and_tscore_separate(prediction_table, baseline_bmd, baseline_tscore):
    years = ['0'] + list(prediction_table['Year'].unique())
    
    # Plot for BMD
    traces_bmd = []
    for drug in prediction_table['Drug'].unique():
        df_drug = prediction_table[prediction_table['Drug'] == drug]
        bmd_values = [baseline_bmd] + list(df_drug['Predicted BMD'])
        trace_bmd = go.Scatter(x=years, y=bmd_values, mode='lines+markers', name=drug + ' (BMD)')
        traces_bmd.append(trace_bmd)

    # Plot for T-score
    traces_tscore = []
    for drug in prediction_table['Drug'].unique():
        df_drug = prediction_table[prediction_table['Drug'] == drug]
        tscore_values = [baseline_tscore] + list(df_drug['Predicted T-score'])
        trace_tscore = go.Scatter(x=years, y=tscore_values, mode='lines+markers', name=drug + ' (T-score)', yaxis='y2')
        traces_tscore.append(trace_tscore)

    # Create BMD layout
    layout_bmd = go.Layout(
        title="Predicted BMD over Time",
        xaxis=dict(title='Years'),
        yaxis=dict(title='BMD (g/cm²)', showgrid=False),
        legend=dict(x=0.1, y=1.1, orientation='h')
    )

    # Create T-score layout
    layout_tscore = go.Layout(
        title="Predicted T-score over Time",
        xaxis=dict(title='Years'),
        yaxis2=dict(title='T-score', overlaying='y', side='right', showgrid=False),
        legend=dict(x=0.1, y=1.1, orientation='h')
    )

    # Create BMD and T-score figures
    fig_bmd = go.Figure(data=traces_bmd, layout=layout_bmd)
    fig_tscore = go.Figure(data=traces_tscore, layout=layout_tscore)

    # Use Streamlit columns to place two plots side by side
    col1, col2 = st.columns(2)
    with col1:
        st.plotly_chart(fig_bmd)
    with col2:
        st.plotly_chart(fig_tscore)

# 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_plot_tscore_labels(file_path, bmd_patient, tscore_patient, C_avg_lunar, C_sd_lunar, drug_selected):
    # Step 1: Load and clean BMD data from the Excel sheet
    df_bmd_data = load_clean_bmd_data(file_path)

    # 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 the prediction table with BMD and T-score
    prediction_table = create_bmd_and_tscore_prediction_table(df_bmd_data, drug_selected, bmd_patient, c_avg_new, c_sd_new)

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

    st.write("BMD and T-score Prediction Table")
    st.dataframe(prediction_table)

    # Step 5: Plot the BMD and T-score graphs side by side
    plot_bmd_and_tscore_separate(prediction_table, bmd_patient, tscore_patient)

    # Step 6: Check if goal is achieved and show the predicted BMD and T-score for each year
    for i, row in prediction_table.iterrows():
        st.write(f"Year {row['Year']}: BMD = {row['Predicted BMD']:.3f}, T-score = {row['Predicted T-score']:.2f}")

        # Check if the goal of BMD >= -2.4 is achieved
        if row['Predicted T-score'] >= -2.4:
            st.success(f"Goal achieved at year {row['Year']}")
            break

# 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)
    tscore_patient = st.number_input("Initial T-score", min_value=-5.0, max_value=2.0, value=-2.5, step=0.01)

    # 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 all medications
    selected_drugs = st.multiselect("Select drugs to compare", drug_options, default=None)
    if "All Medications" in selected_drugs:
        selected_drugs = drug_options  # If "All Medications" is selected, include all drugs

    # Set C_avg and C_sd for Lunar device (example values)
    C_avg_lunar = 0.95  # Example: Average BMD for Total Hip from Excel (Lunar)
    C_sd_lunar = 0.12   # Example: SD for Total Hip (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_plot_tscore_labels(file_path, bmd_patient, tscore_patient, C_avg_lunar, C_sd_lunar, selected_drugs)

if __name__ == "__main__":
    main()