Create sentiment_analysis.py

modules/sentiment_analysis.py

@@ -0,0 +1,30 @@
+# modules/sentiment_analysis.py
+
+from transformers import pipeline
+
+# Load sentiment pipeline (you can change model to "distilbert-base-uncased-finetuned-sst-2-english")
+sentiment_pipeline = pipeline("sentiment-analysis")
+
+def analyze_sentiment(texts: list) -> list:
+    """
+    Perform sentiment analysis on a list of texts.
+    Returns a list of dictionaries with label and score.
+    """
+    results = []
+    for t in texts:
+        if t.strip():
+            try:
+                result = sentiment_pipeline(t)[0]
+                results.append({
+                    "text": t,
+                    "label": result["label"],
+                    "score": float(result["score"])
+                })
+            except Exception as e:
+                results.append({
+                    "text": t,
+                    "label": "ERROR",
+                    "score": 0.0,
+                    "error": str(e)
+                })
+    return results