Update services/sentiment.py

services/sentiment.py

@@ -1,35 +1,35 @@
-from transformers import pipeline
-
-# Pretrained emotion detection model
-# No training required
-emotion_classifier = pipeline(
-    task="text-classification",
-    model="j-hartmann/emotion-english-distilroberta-base",
-    return_all_scores=True
-)
-
-def analyze_emotion(ad_text: str):
-    """
-    Analyze emotional tone of an ad caption
-    Returns emotion scores
-    """
-
-    if not ad_text or len(ad_text.strip()) == 0:
-        return {"error": "Empty ad text"}
-
-    result = emotion_classifier(ad_text)[0]
-
-    emotions = []
-    for item in result:
-        emotions.append({
-            "emotion": item["label"],
-            "confidence": round(item["score"], 3)
-        })
-
-    # Sort by highest confidence
-    emotions = sorted(emotions, key=lambda x: x["confidence"], reverse=True)
-
-    return {
-        "ad_text": ad_text,
-        "emotions": emotions
-    }
+from transformers import pipeline
+
+# Pretrained emotion detection model
+# No training required
+emotion_classifier = pipeline(
+    task="text-classification",
+    model="j-hartmann/emotion-english-distilroberta-base",
+    return_all_scores=True
+)
+
+def detect_emotion(ad_text: str):
+    """
+    Analyze emotional tone of an ad caption
+    Returns emotion scores
+    """
+
+    if not ad_text or len(ad_text.strip()) == 0:
+        return {"error": "Empty ad text"}
+
+    result = emotion_classifier(ad_text)[0]
+
+    emotions = []
+    for item in result:
+        emotions.append({
+            "emotion": item["label"],
+            "confidence": round(item["score"], 3)
+        })
+
+    # Sort by highest confidence
+    emotions = sorted(emotions, key=lambda x: x["confidence"], reverse=True)
+
+    return {
+        "ad_text": ad_text,
+        "emotions": emotions
+    }