Update appbackup.py

appbackup.py

@@ -1,12 +1,14 @@
+#file_path = "cleaned_bmd_medication_data.xlsx"
+
 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},
+    'Femoral Neck': {'mu': 0.916852, 'sigma': 0.120754},
+    'Total Hip': {'mu': 0.955439, 'sigma': 0.125406},
+    'Lumbar spine (L1-L4)': {'mu': 1.131649, 'sigma': 0.139618},
 }
 
 # Load medication data
@@ -35,7 +37,6 @@ def generate_predictions(medication_data, site, bmd, mu, sigma):
         for year in row.index[1:-1]:  # Skip 'Medication' and 'Site' columns
             if not pd.isna(row[year]):
                 percentage_increase = row[year]
-                # BMD calculated using initial BMD (from user input)
                 predicted_bmd = bmd * (1 + percentage_increase)
                 predicted_tscore = calculate_tscore(predicted_bmd, mu, sigma)
                 
@@ -83,6 +84,22 @@ def display_results(predictions, site):
 def main():
     st.title("BMD and T-score Prediction Tool")
     
+    # DEXA Machine Selection
+    dexa_machine = st.selectbox("DEXA Machine", ["LUNAR"])
+    
+    # Gender Selection
+    gender = st.selectbox("Gender", ["Female"])
+    
+    # Location (Site) Selection with Mapping
+    site_mapping = {
+        'Lumbar spine (L1-L4)': 'LS',
+        'Femoral Neck': 'FN',
+        'Total Hip': 'TH'
+    }
+    site_options = list(site_mapping.keys())
+    selected_site = st.selectbox("Select Region (Site)", site_options)
+    site = site_mapping[selected_site]  # Map to the actual value in the dataset
+    
     # Input patient data
     bmd_patient = st.number_input(
         "Initial BMD",
@@ -90,17 +107,15 @@ def main():
         value=0.800, step=0.001,
         format="%.3f"
     )
-    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()
-    
+    constants = REGRESSION_CONSTANTS.get(selected_site, {})
+
     # Generate and display predictions
     if st.button("Predict"):
         predictions = generate_predictions(medication_data, site, bmd_patient, constants['mu'], constants['sigma'])
-        display_results(predictions, site)
+        display_results(predictions, selected_site)
 
 if __name__ == "__main__":
     main()