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import boto3, json
from transformers import pipeline
# Load a BERT‑based classifier fine‑tuned on diet‑related labels
classifier = pipeline("text-classification", model="vegamovies/diet-tagger")
def tag_movie(script_text: str) -> dict:
    results = classifier(script_text, top_k=5)
    tags = r['label']: r['score'] for r in results if r['score'] > 0.6
    return tags
# Example usage
script = open("movie_script.txt").read()
diet_tags = tag_movie(script)
print(json.dumps(diet_tags, indent=2))

The output might be:

  "VEGAN_COOKING": 0.92,
  "PLANT_BASED_ACTIVISM": 0.78,
  "MIXED_DIET": 0.45

Only tags above the confidence threshold are stored in the metadata database, ensuring users see reliable dietary information.