Create app.py

app.py

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+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 year in years:
+        if not pd.isna(df_bmd_data.loc[df_bmd_data['Drug'] == drug_selected, year].values[0]):
+            percent_increase = df_bmd_data.loc[df_bmd_data['Drug'] == drug_selected, 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))
+
+    return pd.DataFrame(predictions, columns=['Year', 'Predicted BMD', 'Predicted T-score'])
+
+# Step 5: Plot the BMD increase over time as a line graph and add T-score labels positioned below each point
+def plot_bmd_timeline_with_tscore(prediction_table, baseline_bmd):
+    years = ['0'] + list(prediction_table['Year'])  # Add "Year 0" as the starting point
+    bmd_values = [baseline_bmd] + list(prediction_table['Predicted BMD'])
+    tscore_values = [''] + list(prediction_table['Predicted T-score'])  # No T-score for Year 0
+
+    # Create a line plot for BMD values
+    trace_bmd = go.Scatter(x=years, y=bmd_values, mode='lines+markers', name='BMD', line=dict(color='blue'))
+    trace_tscore = go.Scatter(x=years, y=tscore_values, mode='lines+markers', name='T-score', line=dict(color='red'), yaxis='y2')
+
+    # Create layout with dual y-axes
+    layout = go.Layout(
+        title="Predicted BMD and T-score over Time",
+        xaxis=dict(title='Years'),
+        yaxis=dict(title='BMD (g/cm²)', showgrid=False),
+        yaxis2=dict(title='T-score', overlaying='y', side='right', showgrid=False),
+        legend=dict(x=0.1, y=1.1, orientation='h'),
+    )
+
+    fig = go.Figure(data=[trace_bmd, trace_tscore], layout=layout)
+    st.plotly_chart(fig)
+
+# 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 increase over time with T-score labels
+    plot_bmd_timeline_with_tscore(prediction_table, bmd_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=0.85, step=0.01)
+    tscore_patient = st.number_input("Initial T-score", min_value=-3.0, max_value=0.0, value=-2.0, step=0.01)
+
+    # Select drug
+    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']
+    drug_selected = st.selectbox("Select a drug", drug_options)
+
+    # 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"
+
+    # 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, drug_selected)
+
+if __name__ == "__main__":
+    main()