pipeline.py

from transformers import pipeline
from utils import clean_text, generate_wordcloud

# Load Hugging Face models
sentiment_model = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")

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