Update app.py

app.py

@@ -14,7 +14,7 @@ REGRESSION_CONSTANTS = {
 # Load medication data
 @st.cache_data
 def load_medication_data():
-    file_path = "cleaned_bmd_medication_data.xlsx"
+    file_path = "/mnt/data/cleaned_bmd_medication_data.xlsx"
     return pd.read_excel(file_path)
 
 # Calculate predicted BMD after medication
@@ -28,27 +28,27 @@ def calculate_tscore(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']
+    all_results = []
     
-    prediction_results = []
     for _, row in site_data.iterrows():
         drug = row['Medication']
-        bmd_predictions = [bmd]
-        tscore_predictions = [calculate_tscore(bmd, mu, sigma)]
+        predictions = {'Year': [], 'Predicted BMD': [], 'Predicted T-score': []}
         
-        for year in years:
+        baseline_bmd = bmd
+        for year in row.index[1:-1]:  # Skip 'Medication' and 'Site' columns
             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)
+                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
         
-        prediction_results.append({
-            'Drug': drug,
-            'BMD Predictions': bmd_predictions,
-            'T-score Predictions': tscore_predictions,
-        })
-    return prediction_results
+        all_results.append({'Drug': drug, 'Predictions': predictions})
+    return all_results
 
 # Display results as table and plots
 def display_results(predictions, site):
@@ -56,33 +56,32 @@ def display_results(predictions, 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']
+        predictions = result['Predictions']
         
-        # Format values
-        bmd_predictions = [f"{bmd:.3f}" for bmd in bmd_predictions]
-        tscore_predictions = [f"{tscore:.1f}" for tscore in tscore_predictions]
-        
-        # Create table
-        data = {
-            'Year': years[:len(bmd_predictions)],
-            'Predicted BMD': bmd_predictions,
-            'Predicted T-score': tscore_predictions,
-        }
+        # Display table
         st.write(f"### {drug}")
-        st.dataframe(pd.DataFrame(data))
+        st.dataframe(pd.DataFrame(predictions))
         
-        # Create plots
-        fig_bmd = go.Scatter(x=data['Year'], y=[float(bmd) for bmd in bmd_predictions], mode='lines+markers', name='BMD')
-        fig_tscore = go.Scatter(x=data['Year'], y=[float(tscore) for tscore in tscore_predictions], mode='lines+markers', name='T-score')
-
-        # Combine both graphs in a single row
+        # 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=[fig_bmd], layout=go.Layout(title=f"{drug} - Predicted BMD", xaxis_title="Year", yaxis_title="BMD (g/cm²)")))
+            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=[fig_tscore], layout=go.Layout(title=f"{drug} - Predicted T-score", xaxis_title="Year", yaxis_title="T-score")))
+            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():