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Running on Zero
Running on Zero
| from fastapi import FastAPI | |
| from pydantic import BaseModel | |
| from transformers import pipeline | |
| from glin_profanity import Filter | |
| # Initialize your models | |
| toxicity_pipe = pipeline("text-classification", model="textdetox/twitter-xlmr-toxicity-classifier") | |
| detox_pipe = pipeline("text2text-generation", model="s-nlp/mt0-xl-detox-orpo") | |
| profanity_filter = Filter({"languages": ["english", "spanish", "french"], "replace_with": "***"}) | |
| app = FastAPI(title="Multilingual Moderation API") | |
| class TextRequest(BaseModel): | |
| text: str | |
| class ModerationResponse(BaseModel): | |
| original_text: str | |
| detected_categories: dict | |
| is_flagged: bool | |
| rewritten_text: str | None | |
| async def moderate_text(request: TextRequest): | |
| text = request.text | |
| profanity_result = profanity_filter.check_profanity(text) | |
| toxicity_scores = toxicity_pipe(text)[0] | |
| is_flagged = profanity_result["contains_profanity"] or toxicity_scores['score'] > 0.5 | |
| rewritten = None | |
| if is_flagged: | |
| rewritten = detox_pipe(text, max_length=100)[0]['generated_text'] | |
| return ModerationResponse( | |
| original_text=text, | |
| detected_categories={"profanity": profanity_result["contains_profanity"], "toxicity_model": toxicity_scores}, | |
| is_flagged=is_flagged, | |
| rewritten_text=rewritten | |
| ) | |
| async def health_check(): | |
| return {"status": "ok"} |