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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():
# Use the combined medication data we cleaned earlier
file_path = "/mnt/data/BMD medication prediction.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]
years = ['1st Year', '2nd Year', '3rd Year', '4th Year', '5th Year', '6th Year', '8th Year', '10th Year']
prediction_results = []
for _, row in site_data.iterrows():
drug = row['Medication']
bmd_predictions = [bmd]
tscore_predictions = [calculate_tscore(bmd, mu, sigma)]
for year in years:
if not pd.isna(row[year]):
bmd_new = calculate_bmd(bmd_predictions[-1], row[year])
tscore_new = calculate_tscore(bmd_new, mu, sigma)
bmd_predictions.append(bmd_new)
tscore_predictions.append(tscore_new)
prediction_results.append({
'Drug': drug,
'BMD Predictions': bmd_predictions,
'T-score Predictions': tscore_predictions,
})
return prediction_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']
bmd_predictions = result['BMD Predictions']
tscore_predictions = result['T-score Predictions']
years = ['0', '1st', '2nd', '3rd', '4th', '5th', '6th', '8th', '10th']
# Create table
data = {
'Year': years[:len(bmd_predictions)],
'Predicted BMD': bmd_predictions,
'Predicted T-score': tscore_predictions,
}
st.write(f"### {drug}")
st.dataframe(pd.DataFrame(data))
# Create plots
fig_bmd = go.Figure(data=[go.Scatter(x=data['Year'], y=data['Predicted BMD'], mode='lines+markers', name='BMD')])
fig_bmd.update_layout(title=f"{drug} - Predicted BMD over Time", xaxis_title="Year", yaxis_title="BMD (g/cm²)")
st.plotly_chart(fig_bmd)
fig_tscore = go.Figure(data=[go.Scatter(x=data['Year'], y=data['Predicted T-score'], mode='lines+markers', name='T-score')])
fig_tscore.update_layout(title=f"{drug} - Predicted T-score over Time", xaxis_title="Year", yaxis_title="T-score")
st.plotly_chart(fig_tscore)
# 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()
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