Update app.py

app.py

@@ -4,8 +4,8 @@ import numpy as np
 import pandas as pd
 import streamlit as st
 import joblib
-import shap
-import matplotlib.pyplot as plt
+
+
 import os
 from huggingface_hub import hf_hub_download, HfApi
 import hmac
@@ -33,12 +33,13 @@ from sklearn.model_selection import train_test_split
 
 #Figures setting block
 import io
-from lifelines import CoxPHFitter
+
 
 
 
 # REPLACE make_fig with this (or add this and stop using plt.plot directly)
 def make_fig(figsize=(5.5, 3.6), dpi=120):
+    import matplotlib.pyplot as plt
     fig, ax = plt.subplots(figsize=figsize, dpi=dpi)
     return fig, ax
 
@@ -582,8 +583,8 @@ def train_and_save(
     use_feature_selection: bool,
     l1_C: float,
     use_dimred: bool,
-    svd_components: int,
-):
+    svd_components: int,):
+    from lifelines import CoxPHFitter
     X = df[feature_cols].copy()
     y_raw = df[LABEL_COL].copy()
 
@@ -832,6 +833,7 @@ def train_and_save(
 # SHAP
 # ============================================================
 def build_shap_explainer(pipe, X_bg, max_bg=200):
+    import shap
     if len(X_bg) > max_bg:
         X_bg = X_bg.sample(max_bg, random_state=42)
 
@@ -1259,7 +1261,7 @@ def options_for(col: str, df: pd.DataFrame | None):
     return [""] + out
 
 import re
-import country_converter as coco
+
 
 # Canonical region labels you can use for analysis
 # (UN-style: Africa, Americas, Asia, Europe, Oceania; you can later refine into subregions)
@@ -1320,26 +1322,32 @@ def normalize_country_name(x: str) -> str | None:
     # country_converter can handle many variants; pass through as-is
     return s.strip()
 
-def country_to_region(country: str | None) -> str:
+from typing import Optional
+
+def country_to_region(country: Optional[str]) -> str:
     """
     Map a country name to a broad region for analytics.
     Returns one of: Africa, Americas, Asia, Europe, Oceania, Unknown.
+    Lazy-imports country_converter to reduce startup memory.
     """
-    if not country:
+    if not country or pd.isna(country):
         return REGION_UNKNOWN
 
-    # coco returns 'not found' if it can't map; we convert to Unknown
+    country = str(country).strip()
+
+    import country_converter as coco  # lazy import
+
     r = coco.convert(names=country, to="continent")
+
     if not r or str(r).lower() in ("not found", "nan", "none"):
         return REGION_UNKNOWN
 
-    # Normalize labels to your preferred set
-    # coco usually returns: Africa, America, Asia, Europe, Oceania
-    # Convert "America" -> "Americas"
     if r == "America":
         return "Americas"
+
     return str(r)
 
+
 def add_ethnicity_region(df: pd.DataFrame, eth_col: str = "Ethnicity", out_col: str = "Ethnicity_Region") -> pd.DataFrame:
     """Adds an analytics-only region column derived from the Ethnicity/nationality column."""
     if eth_col not in df.columns: