Update main.py

main.py

@@ -109,8 +109,13 @@ async def analyze(data: ReviewInput, x_api_key: str = Header(None)):
 
             sentiment_pipeline = pipeline("sentiment-analysis", model=data.model)
             sentiment = sentiment_pipeline(data.text)[0]
-            emotion = detect_emotion(data.text)
+            emotion_raw = detect_emotion(data.text)
+            emotion = emotion_raw["label"] if isinstance(emotion_raw, dict) and "label" in emotion_raw else str(emotion_raw)
             churn_risk = assess_churn_risk(sentiment["label"], emotion)
+            pain_points = []
+            if data.aspects:
+                from model import extract_pain_points  # 🔍 Import inline if not already
+                pain_points = extract_pain_points(data.text)
 
             industry = detect_industry(data.text) if not data.industry or "auto" in data.industry.lower() else data.industry
             product_category = detect_product_category(data.text) if not data.product_category or "auto" in data.product_category.lower() else data.product_category
@@ -122,7 +127,9 @@ async def analyze(data: ReviewInput, x_api_key: str = Header(None)):
                 "product_category": product_category,
                 "device": "Web",
                 "industry": industry,
-                "churn_risk": churn_risk
+                "churn_risk": churn_risk,
+                "pain_points": pain_points
+
             }
 
         if data.follow_up:
@@ -169,6 +176,9 @@ async def bulk_analyze(data: BulkReviewInput, token: str = Query(None)):
             summary = smart_summarize(review_text, n_clusters=2 if data.intelligence else 1)
             sentiment = sentiment_pipeline(review_text)[0]
             emotion = detect_emotion(review_text)
+            churn = assess_churn_risk(sentiment["label"], emotion)
+            pain = extract_pain_points(review_text) if data.aspects else []
+
 
             ind = auto_fill(data.industry[i] if data.industry else None, detect_industry(review_text))
             prod = auto_fill(data.product_category[i] if data.product_category else None, detect_product_category(review_text))
@@ -182,7 +192,9 @@ async def bulk_analyze(data: BulkReviewInput, token: str = Query(None)):
                 "emotion": emotion,
                 "industry": ind,
                 "product_category": prod,
-                "device": dev
+                "device": dev,
+                "churn_risk": churn,
+                "pain_points": pain
             }
 
             if data.follow_up and i < len(data.follow_up):