Update model.py

model.py

@@ -105,12 +105,17 @@ def assess_churn_risk(sentiment_label, emotion_label):
 
 # === Pain Point Extractor ===
 def extract_pain_points(text):
+    """
+    Returns a list of keyword-based user pain points.
+    Later you can extend with transformer-based aspect mining (KeyBERT, LLMs).
+    """
     common_issues = [
         "slow", "crash", "lag", "expensive", "confusing", "noisy", "poor", "rude",
         "unhelpful", "bug", "broken", "unresponsive", "not working", "error", "delay", "disconnect"
     ]
-    matched = [kw for kw in common_issues if re.search(rf"\b{kw}\b", text.lower())]
-    return list(set(matched))[:5]
+    text_lower = text.lower()
+    matches = [kw for kw in common_issues if re.search(rf"\b{kw}\b", text_lower)]
+    return list(set(matches))[:5]
 
 # === Industry Detector ===
 def detect_industry(text):