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from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from fastapi.staticfiles import StaticFiles
from pydantic import BaseModel, Field
from typing import Optional
from cold.classifier import ToxicTextClassifier
import torch
app = FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
model = ToxicTextClassifier()
model.load_state_dict(torch.load("output/lited_best.pth",map_location="cpu"))
class PredictionInput(BaseModel):
text: str = Field(..., title="Text to classify", description="The text to classify for malicious content")
context: Optional[str] = Field(None, title="Context for classification", description="Optional context to provide additional information for classification")
@app.post("/predict")
def predict(input: PredictionInput):
try:
if not input.text:
raise HTTPException(status_code=400, detail="Text input is required")
elif len(input.text) > 512:
raise HTTPException(status_code=400, detail="Text input exceeds maximum length of 512 characters")
if input.context and len(input.context) > 512:
raise HTTPException(status_code=400, detail="Context input exceeds maximum length of 512 characters")
if not input.context:
result = model.predict(input.text, device="cpu")
print(result)
return {"text": input.text, "prediction": result[0]["prediction"], "probabilities": result[0]["probabilities"]}
else:
result = model.predict([[input.text,input.context]], device="cpu")
return {"text": input.text, "context": input.context, "prediction": result[0]["prediction"], "probabilities": result[0]["probabilities"]}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
app.mount("/", StaticFiles(directory="out", html=True), name="static")
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