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from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
import torch
from detoxify import Detoxify
class Guardrail:
def __init__(self):
tokenizer = AutoTokenizer.from_pretrained("ProtectAI/deberta-v3-base-prompt-injection")
model = AutoModelForSequenceClassification.from_pretrained("ProtectAI/deberta-v3-base-prompt-injection")
self.classifier = pipeline(
"text-classification",
model=model,
tokenizer=tokenizer,
truncation=True,
max_length=512,
device=torch.device("cuda" if torch.cuda.is_available() else "cpu")
)
def guard(self, prompt):
return self.classifier(prompt)
def determine_level(self, label, score):
if label == "SAFE":
return 0, "safe"
else:
if score > 0.9:
return 4, "high"
elif score > 0.75:
return 3, "medium"
elif score > 0.5:
return 2, "low"
else:
return 1, "very low"
class TextPrompt(BaseModel):
prompt: str
class ClassificationResult(BaseModel):
label: str
score: float
level: int
severity_label: str
app = FastAPI()
guardrail = Guardrail()
toxicity_classifier = Detoxify('original')
class ToxicityResult(BaseModel):
toxicity: float
severe_toxicity: float
obscene: float
threat: float
insult: float
identity_attack: float
@app.post("/api/models/toxicity/classify", response_model=ToxicityResult)
def classify_toxicity(text_prompt: TextPrompt):
try:
result = toxicity_classifier.predict(text_prompt.prompt)
return {
"toxicity": result['toxicity'],
"severe_toxicity": result['severe_toxicity'],
"obscene": result['obscene'],
"threat": result['threat'],
"insult": result['insult'],
"identity_attack": result['identity_attack']
}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.post("/api/models/PromptInjection/classify", response_model=ClassificationResult)
def classify_text(text_prompt: TextPrompt):
try:
result = guardrail.guard(text_prompt.prompt)
label = result[0]['label']
score = result[0]['score']
level, severity_label = guardrail.determine_level(label, score)
return {"label": label, "score": score, "level": level, "severity_label": severity_label}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000)
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