Spaces:
Sleeping
Sleeping
async
Browse files
main.py
CHANGED
@@ -3,8 +3,8 @@ from pydantic import BaseModel
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
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import torch
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from detoxify import Detoxify
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class Guardrail:
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def __init__(self):
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@@ -20,8 +20,8 @@ class Guardrail:
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device=torch.device("cuda" if torch.cuda.is_available() else "cpu")
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)
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def guard(self, prompt):
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return self.classifier
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def determine_level(self, label, score):
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if label == "SAFE":
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@@ -36,18 +36,15 @@ class Guardrail:
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else:
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return 1, "very low"
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-
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class TextPrompt(BaseModel):
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prompt: str
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class ClassificationResult(BaseModel):
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label: str
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score: float
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level: int
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severity_label: str
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app = FastAPI()
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guardrail = Guardrail()
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toxicity_classifier = Detoxify('original')
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@@ -61,9 +58,9 @@ class ToxicityResult(BaseModel):
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identity_attack: float
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@app.post("/api/models/toxicity/classify", response_model=ToxicityResult)
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def classify_toxicity(text_prompt: TextPrompt):
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try:
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result = toxicity_classifier.predict
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return {
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"toxicity": result['toxicity'],
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"severe_toxicity": result['severe_toxicity'],
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@@ -75,11 +72,10 @@ def classify_toxicity(text_prompt: TextPrompt):
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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@app.post("/api/models/PromptInjection/classify", response_model=ClassificationResult)
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def classify_text(text_prompt: TextPrompt):
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try:
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result = guardrail.guard(text_prompt.prompt)
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label = result[0]['label']
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score = result[0]['score']
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level, severity_label = guardrail.determine_level(label, score)
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@@ -87,8 +83,6 @@ def classify_text(text_prompt: TextPrompt):
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=8000)
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
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import torch
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from detoxify import Detoxify
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import asyncio
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from fastapi.concurrency import run_in_threadpool
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class Guardrail:
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def __init__(self):
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device=torch.device("cuda" if torch.cuda.is_available() else "cpu")
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)
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async def guard(self, prompt):
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return await run_in_threadpool(self.classifier, prompt)
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def determine_level(self, label, score):
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if label == "SAFE":
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else:
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return 1, "very low"
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class TextPrompt(BaseModel):
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prompt: str
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class ClassificationResult(BaseModel):
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label: str
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score: float
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level: int
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severity_label: str
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app = FastAPI()
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guardrail = Guardrail()
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toxicity_classifier = Detoxify('original')
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identity_attack: float
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@app.post("/api/models/toxicity/classify", response_model=ToxicityResult)
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async def classify_toxicity(text_prompt: TextPrompt):
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try:
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result = await run_in_threadpool(toxicity_classifier.predict, text_prompt.prompt)
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return {
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"toxicity": result['toxicity'],
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"severe_toxicity": result['severe_toxicity'],
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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@app.post("/api/models/PromptInjection/classify", response_model=ClassificationResult)
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async def classify_text(text_prompt: TextPrompt):
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try:
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result = await guardrail.guard(text_prompt.prompt)
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label = result[0]['label']
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score = result[0]['score']
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level, severity_label = guardrail.determine_level(label, score)
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=8000)
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