Merge pull request #1 from MasanneckLab/codex/locate-normalizable-variables

README.md

@@ -20,3 +20,5 @@ tags:
 
 This Streamlit application computes normative Z-scores and percentiles for various Withings smartwatch biomarkers by comparing user-provided values against a global normative dataset. It helps you interpret individual health metrics relative to global distributions.
 
+**Note**: Weight normalization is currently disabled in the UI. You can re-enable it by removing "weight" from the `DISABLED_BIOMARKERS` set in `app.py`.
+

app.py

@@ -39,6 +39,9 @@ BIOMARKER_LABELS = {
     # add any others here
 }
 
+# Biomarkers temporarily disabled in the UI. Remove from this set to re-enable.
+DISABLED_BIOMARKERS = {"weight"}
+
 
 def main():
     if "disclaimer_shown" not in st.session_state:
@@ -114,7 +117,9 @@ def main():
         bmi_param = bmi_cat
 
     # Biomarker selection with friendly labels
-    codes = sorted(norm_df["Biomarkers"].unique())
+    codes = sorted(
+        c for c in norm_df["Biomarkers"].unique() if c not in DISABLED_BIOMARKERS
+    )
     friendly = [BIOMARKER_LABELS.get(c, c.title()) for c in codes]
     default_idx = friendly.index("Number of Steps")
     selected_label = st.sidebar.selectbox(