app.py

import gradio as gr
import os

from transformers import pipeline


auth_token = os.environ.get("HF_ACCESS_TOKEN") or True

pipe = pipeline(model="fmops/ai-traffic-classifier", use_auth_token=auth_token)
id2label = {"LABEL_0": "not ai traffic", "LABEL_1": "suspected ai traffic"}


def predict(path, method, content):
 prompt = f"""
 path: {path}
 method: {method}
 content: {content}
 """
 return {id2label[x["label"]]: x["score"] for x in pipe(prompt, truncation=True)}


with gr.Blocks() as demo:
 gr.Markdown(
 """
 # AI Traffic Classifier

 This is a demo of the AI traffic classifier.
 """
 )
 iface = gr.Interface(
 fn=predict,
 inputs=["text", "text", "text"],
 examples=[
 ["/login", "POST", ""],
 [
 "/backend-api/conversation",
 "POST",
 """ {"action":"next","messages":[{"id":"aaa229d6-f97d-427c-b7bb-0e6276079c95","author":{"role":"user"},"content":{"content_type":"text","parts":["Write a long poem"]},"metadata":{}}],"conversation_id":"395edb51-5cb3-432f-a142-d87c160d403b","parent_message_id":"535f59f0-ed0f-4d9f-8e4b-4b5c0834833b","model":"text-davinci-002-render-sha","timezone_offset_min":420,"suggestions":[],"history_and_training_disabled":true,"arkose_token":null}""",
 ],
 [
 "/api/chat",
 "POST",
 """{"text":"How are you aware of GPT-3? There must have been some data leakage..."}""",
 ],
 ],
 outputs="label",
 )

demo.launch()