Update app.py

app.py

@@ -38,13 +38,13 @@ 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, c_avg_new, c_sd_new):
+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 = []
+        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]
@@ -68,9 +68,9 @@ def display_prediction_tables_and_plots(prediction_tables, baseline_bmd, baselin
         st.dataframe(table)
 
         # Create and display separate plots for each drug
-        years = ['0'] + list(table['Year'])
-        bmd_values = [baseline_bmd] + list(table['Predicted BMD'])
-        tscore_values = [baseline_tscore] + list(table['Predicted T-score'])
+        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'))
@@ -110,7 +110,7 @@ def main_with_separate_tables(file_path, bmd_patient, tscore_patient, C_avg_luna
     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, c_avg_new, c_sd_new)
+    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}")
@@ -122,14 +122,14 @@ def main_with_separate_tables(file_path, bmd_patient, tscore_patient, C_avg_luna
 def main():
     st.title("BMD and T-score Prediction Tool")
 
-    # 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)
-
     # 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',
@@ -142,7 +142,7 @@ def main():
     # 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   # Example: SD for Total Hip (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)