Create appbackup.py

appbackup.py

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+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},
+}
+
+# Load medication data
+@st.cache_data
+def load_medication_data():
+    file_path = "cleaned_bmd_medication_data.xlsx"
+    return pd.read_excel(file_path)
+
+# Calculate predicted BMD after medication
+def calculate_bmd(bmd, percentage_increase):
+    return bmd * (1 + percentage_increase)
+
+# Convert BMD to T-score
+def calculate_tscore(bmd, mu, sigma):
+    return (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]
+    all_results = []
+    
+    for _, row in site_data.iterrows():
+        drug = row['Medication']
+        predictions = {'Year': ['0'], 'Predicted BMD': [round(bmd, 3)], 'Predicted T-score': [round(calculate_tscore(bmd, mu, sigma), 1)]}
+        
+        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)
+                
+                predictions['Year'].append(year.replace(" Year", ""))  # Simplify year label
+                predictions['Predicted BMD'].append(round(predicted_bmd, 3))
+                predictions['Predicted T-score'].append(round(predicted_tscore, 1))
+        
+        all_results.append({'Drug': drug, 'Predictions': predictions})
+    return all_results
+
+# Display results as table and plots
+def display_results(predictions, site):
+    st.subheader(f"Predictions for {site}")
+    
+    for result in predictions:
+        drug = result['Drug']
+        predictions = result['Predictions']
+        
+        # Display table
+        st.write(f"### {drug}")
+        st.dataframe(pd.DataFrame(predictions))
+        
+        # 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=[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=[tscore_plot], layout=go.Layout(
+                title=f"{drug} - Predicted T-score", xaxis_title="Year", yaxis_title="T-score"
+            )))
+
+# Streamlit UI
+def main():
+    st.title("BMD and T-score Prediction Tool")
+    
+    # Input patient data
+    bmd_patient = st.number_input(
+        "Initial BMD",
+        min_value=0.000, max_value=2.000,
+        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()
+    
+    # Generate and display predictions
+    if st.button("Predict"):
+        predictions = generate_predictions(medication_data, site, bmd_patient, constants['mu'], constants['sigma'])
+        display_results(predictions, site)
+
+if __name__ == "__main__":
+    main()