Create pipeline.py

pipeline.py

@@ -0,0 +1,24 @@
+from transformers import pipeline
+from utils import clean_text, generate_wordcloud
+
+# Load Hugging Face models
+sentiment_model = pipeline("sentiment-analysis")
+summarizer = pipeline("summarization")
+
+def process_comment(text):
+    # Clean text
+    cleaned = clean_text(text)
+
+    # Sentiment
+    sentiment = sentiment_model(cleaned)[0]
+
+    # Summary
+    try:
+        summary = summarizer(cleaned, max_length=60, min_length=10, do_sample=False)[0]["summary_text"]
+    except:
+        summary = "Summary not available (text too short)."
+
+    # Word cloud
+    wc_img = generate_wordcloud(cleaned)
+
+    return sentiment, summary, wc_img