File size: 737 Bytes
6ae0c6d 8b63b05 4f8607d 8b63b05 6b3f7e8 8b63b05 6b3f7e8 8b63b05 6b3f7e8 8b63b05 |
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 |
import gradio as gr
from transformers import pipeline
# Specify the model ID
model_id = "Canstralian/CySec_Known_Exploit_Analyzer"
# Load the model using Hugging Face's pipeline (adjust if needed)
analyzer = pipeline("text-classification", model=model_id)
# Define a function to process inputs using the model
def analyze_exploit(text):
result = analyzer(text)
return result
# Create a Gradio interface
interface = gr.Interface(
fn=analyze_exploit,
inputs=gr.Textbox(lines=2, placeholder="Enter exploit description..."),
outputs="json",
title="CySec Known Exploit Analyzer",
description="Analyze known cybersecurity exploits using a Hugging Face model."
)
# Launch the Gradio interface
interface.launch() |