Text Classification
Transformers
Safetensors
English
distilbert
cybersecurity
xss
security
web
payload-detection
web-security
Instructions to use kd7979148/XSS_Payload_Detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kd7979148/XSS_Payload_Detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kd7979148/XSS_Payload_Detector")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kd7979148/XSS_Payload_Detector") model = AutoModelForSequenceClassification.from_pretrained("kd7979148/XSS_Payload_Detector") - Notebooks
- Google Colab
- Kaggle
File size: 1,035 Bytes
afd9f5c 636d073 afd9f5c 636d073 afd9f5c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | # -*- coding: utf-8 -*-
from flask import (
Flask,
request,
render_template
)
app = Flask(__name__)
@app.route("/", methods=["GET", "POST"])
def home():
#################################################
# save logs
#################################################
with open("access.log", "a", encoding="utf-8") as f:
log = (
f'{request.remote_addr} - '
f'"{request.method} {request.full_path} HTTP/1.1"\n'
)
f.write(log)
f.flush()
#################################################
# q param
#################################################
q = request.args.get("q", "")
#################################################
# html render
#################################################
return render_template(
"index.html",
q=q
)
if __name__ == "__main__":
app.run(
host="0.0.0.0",
port=8080,
debug=False
)
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