Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,3 +1,4 @@
+# Import packages
 import os
 import joblib
 import numpy as np
@@ -9,7 +10,7 @@ import tempfile
 import streamlit.components.v1 as components
 import scipy.sparse as sp 
 
-# Setup
+# Setup API and features 
 API_URL = os.getenv('API_URL')
 NUMERIC_FEATURES = ['age','alcohol_consumption_per_week','physical_activity_minutes_per_week',
                     'diet_score','bmi','cholesterol_total','insulin_level','map','glucose_fasting']
@@ -21,7 +22,7 @@ st.set_page_config(page_title="Diabetes Predictions", layout="wide")
 
 # Function for SHAP plot
 def st_shap(plot, height=None):
-    """Renders a SHAP plot in Streamlit using standalone HTML + JS and white background."""
+    """Renders a SHAP plot in Streamlit using HTML style and white background."""
     import tempfile, os
     with tempfile.NamedTemporaryFile(delete=False, suffix=".html") as tmpfile:
         shap.save_html(tmpfile.name, plot)
@@ -42,11 +43,11 @@ def st_shap(plot, height=None):
 
     components.html(styled_html, height=height or 500, width=1000, scrolling=True)
 
-def _to_dense(X):
-    """Ensure X is a dense NumPy array (convert from sparse if needed)."""
-    if sp.issparse(X):
-        return X.toarray()
-    return np.asarray(X)
+#def _to_dense(X):
+   # """Ensure X is a dense NumPy array (convert from sparse if needed)."""
+   # if sp.issparse(X):
+    #    return X.toarray()
+   # return np.asarray(X)
 
 # Function to create synthetic data
 def synth_data(n_policyholders=100, seed=42):
@@ -154,7 +155,7 @@ else:
         shap_plot = shap.force_plot(
             explainer.expected_value,
             shap_values[0],
-            _to_dense(X_row)[0],
+            X_row[0],
             matplotlib=False,
             feature_names=feature_names
         )