app.py

#file_path = "cleaned_bmd_medication_data.xlsx"

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

# Constants from linear regression
REGRESSION_CONSTANTS = {
    'FN': {'mu': 0.916852, 'sigma': 0.120754},
    'TH': {'mu': 0.955439, 'sigma': 0.125406},
    'LS': {'mu': 1.131649, 'sigma': 0.139618},
}

# Load medication data
@st.cache_data
def load_medication_data():
    file_path = "/mnt/data/cleaned_bmd_medication_data.xlsx"
    return pd.read_excel(file_path)

# Calculate predicted BMD after medication
def calculate_bmd(bmd, percentage_increase):
    return bmd * (1 + percentage_increase)

# Convert BMD to T-score
def calculate_tscore(bmd, mu, sigma):
    return (bmd - mu) / sigma

# Generate prediction table for all drugs
def generate_predictions(medication_data, site, bmd, mu, sigma):
    site_data = medication_data[medication_data['Site'] == site]
    all_results = []
    
    for _, row in site_data.iterrows():
        drug = row['Medication']
        predictions = {'Year': [], 'Predicted BMD': [], 'Predicted T-score': []}
        
        baseline_bmd = bmd
        for year in row.index[1:-1]:  # Skip 'Medication' and 'Site' columns
            if not pd.isna(row[year]):
                percentage_increase = row[year]
                predicted_bmd = calculate_bmd(baseline_bmd, percentage_increase)
                predicted_tscore = calculate_tscore(predicted_bmd, mu, sigma)
                
                predictions['Year'].append(year)
                predictions['Predicted BMD'].append(round(predicted_bmd, 3))
                predictions['Predicted T-score'].append(round(predicted_tscore, 1))
                
                baseline_bmd = predicted_bmd
        
        all_results.append({'Drug': drug, 'Predictions': predictions})
    return all_results

# Display results as table and plots
def display_results(predictions, site):
    st.subheader(f"Predictions for {site}")
    
    for result in predictions:
        drug = result['Drug']
        predictions = result['Predictions']
        
        # Display table
        st.write(f"### {drug}")
        st.dataframe(pd.DataFrame(predictions))
        
        # Plot BMD and T-score
        bmd_plot = go.Scatter(
            x=predictions['Year'], y=predictions['Predicted BMD'], mode='lines+markers',
            name='Predicted BMD', line=dict(color='blue')
        )
        tscore_plot = go.Scatter(
            x=predictions['Year'], y=predictions['Predicted T-score'], mode='lines+markers',
            name='Predicted T-score', line=dict(color='green')
        )
        
        # Combine plots in a single row
        col1, col2 = st.columns(2)
        with col1:
            st.plotly_chart(go.Figure(data=[bmd_plot], layout=go.Layout(
                title=f"{drug} - Predicted BMD", xaxis_title="Year", yaxis_title="BMD (g/cm²)"
            )))
        with col2:
            st.plotly_chart(go.Figure(data=[tscore_plot], layout=go.Layout(
                title=f"{drug} - Predicted T-score", xaxis_title="Year", yaxis_title="T-score"
            )))

# 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=0.8, step=0.01)
    site_options = ['FN', 'TH', 'LS']
    site = st.selectbox("Select Region (Site)", site_options)
    
    # Load constants and medication data
    constants = REGRESSION_CONSTANTS[site]
    medication_data = load_medication_data()
    
    # Generate and display predictions
    if st.button("Predict"):
        predictions = generate_predictions(medication_data, site, bmd_patient, constants['mu'], constants['sigma'])
        display_results(predictions, site)

if __name__ == "__main__":
    main()