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| import gradio as gr | |
| import re | |
| from services.anonymizer import anonymize | |
| from services.hate_speech import classify_hatespeech | |
| from services.cola import classify_correctness | |
| from services.bad_words import identify_bad_words | |
| examples = ['John is a son of a.', 'John a wonderful item', 'The dog is a bastard.', | |
| 'The dog a cat.', 'It was Peter Thiel from PayPal.'] | |
| def check_ethical(text): | |
| # simple heuristic based on offensive word list by cmu.edu | |
| text_has_bad_words = identify_bad_words(text) | |
| if len(text_has_bad_words) > 0: | |
| return {'status': 'Input contains offensive words.', 'data': text_has_bad_words} | |
| # based on SpaCy NER recognition | |
| anonymized = anonymize(text) | |
| # based on DistilRoberta hosted on transformers | |
| nice = float(classify_hatespeech(anonymized)) | |
| if nice < .8: | |
| return {'status': 'Input contains hate speech.', 'data': nice} | |
| # based on DistilBert hosted on transformers | |
| linguistic_incorrect = float(classify_correctness(anonymized)) | |
| if linguistic_incorrect < .8: | |
| return {'status': 'Input is linguistically inacceptable.', 'data': anonymized} | |
| return {'status': "ethical", 'data': anonymized} | |
| title = 'Ethical Integrity Demo' | |
| description = '''This demo evaluates texts for polarization, manipulative language, anonymizes, and filters for hate speech.\n | |
| It is an attempt for systems to ensure that only messages that meet the higher standards of ethical responsibility are used for further processing such as machine learning training.''' | |
| demo = gr.Interface(fn=check_ethical, inputs='text', outputs='text', | |
| examples=examples, title=title, description=description) | |
| demo.launch() | |