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()