Update app.py

app.py

@@ -30,100 +30,76 @@ def adjust_constants(bmd_patient, tscore_patient, c_avg, c_sd):
 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):
+# 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):
     years = ['1st', '2nd', '3rd', '4th', '5th', '6th', '8th', '10th']
-    predictions = []
+    drug_tables = {}
 
-    for drug in drug_selected:
+    # Loop through each selected drug and generate the prediction table
+    for drug in selected_drugs:
+        predictions = []
         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)
+                predictions.append((year, bmd_new, tscore_new))
+        
+        # Create DataFrame for each drug
+        drug_table = pd.DataFrame(predictions, columns=['Year', 'Predicted BMD', 'Predicted T-score'])
+        drug_tables[drug] = drug_table
+
+    return drug_tables
+
+# Step 5: Plot BMD and T-score as separate graphs side by side for each drug
+def display_prediction_tables_and_plots(prediction_tables, baseline_bmd, baseline_tscore):
+    # Loop through each drug's prediction table
+    for drug, table in prediction_tables.items():
+        st.write(f"### {drug} Results")
+        
+        # Display the prediction table for the current drug
+        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'])
+
+        # Create BMD plot
+        trace_bmd = go.Scatter(x=years, y=bmd_values, mode='lines+markers', name=f'{drug} (BMD)')
+        fig_bmd = go.Figure(data=[trace_bmd], layout=go.Layout(title=f'{drug} - Predicted BMD over Time', xaxis=dict(title='Years'), yaxis=dict(title='BMD (g/cm²)')))
+        
+        # Create T-score plot
+        trace_tscore = go.Scatter(x=years, y=tscore_values, mode='lines+markers', name=f'{drug} (T-score)', yaxis='y2')
+        fig_tscore = go.Figure(data=[trace_tscore], layout=go.Layout(title=f'{drug} - Predicted T-score over Time', xaxis=dict(title='Years'), yaxis2=dict(title='T-score', overlaying='y', side='right')))
+        
+        # Display the plots
+        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):
+def main_with_separate_tables(file_path, bmd_patient, tscore_patient, C_avg_lunar, C_sd_lunar, selected_drugs):
     # 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)
+    # 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)
 
-    # Display baseline BMD and T-score before the year-by-year comparison
+    # Display baseline BMD and T-score
     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
+    # Step 5: Display prediction tables and plots for each drug
+    display_prediction_tables_and_plots(prediction_tables, bmd_patient, tscore_patient)
 
 # Streamlit UI
 def main():
@@ -139,10 +115,8 @@ def main():
                     '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
+    # Add option to select multiple drugs
+    selected_drugs = st.multiselect("Select drugs to compare", 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)
@@ -157,7 +131,7 @@ def main():
     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)
+            main_with_separate_tables(file_path, bmd_patient, tscore_patient, C_avg_lunar, C_sd_lunar, selected_drugs)
 
 if __name__ == "__main__":
-    main()
+    main()