app.py

from fastapi import FastAPI
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Initialize FastAPI app
app = FastAPI()

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("canstralian/CyberAttackDetection")
model = AutoModelForCausalLM.from_pretrained("canstralian/CyberAttackDetection")

# Define the input data model
class LogData(BaseModel):
    log: str

@app.post("/predict")
async def predict(data: LogData):
    # Tokenize the input log data
    inputs = tokenizer(data.log, return_tensors="pt")
    
    # Generate predictions
    with torch.no_grad():
        outputs = model.generate(**inputs)
    
    # Decode the generated tokens to text
    prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)
    
    return {"prediction": prediction}