Update app.py

app.py

@@ -1,8 +1,6 @@
 import streamlit as st
 import pandas as pd
 import joblib
-import shap
-import matplotlib.pyplot as plt
 
 # Load the model
 @st.cache_resource
@@ -15,7 +13,7 @@ model = load_model()
 st.title("Retention Probability Predictor")
 st.markdown("""
 Predict the probability of retention based on patient and treatment details. 
-Provide the required inputs below to see the prediction and feature contributions.
+Provide the required inputs below to see the prediction.
 """)
 
 # Sidebar for inputs
@@ -85,25 +83,3 @@ if st.sidebar.button("Predict Retention Probability"):
     st.subheader("Calculated Features")
     st.write(f"**Failed Buprenorphine Rate:** {Failed_Bup_Rate:.2%}")
     st.write(f"**Proportion of Days Covered:** {proportionofDaysCovered:.2%}")
-
-    # Explain model prediction using SHAP
-    st.subheader("Feature Contribution to Prediction")
-    explainer = shap.Explainer(model, input_data)  # Create a SHAP explainer
-    shap_values = explainer(input_data)
-
-    # Plot feature importance
-    shap.force_plot(
-        explainer.expected_value[1],
-        shap_values.values[0],
-        input_data.iloc[0],
-        matplotlib=True,
-        show=False
-    )
-    st.pyplot(plt.gcf())  # Display SHAP force plot
-
-    st.write("The above visualization shows how each feature contributes to the prediction.")
-
-    # Feature importance bar chart
-    st.subheader("Feature Importance (Bar Chart)")
-    shap.summary_plot(shap_values, input_data, plot_type="bar", show=False)
-    st.pyplot(plt.gcf())