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import gradio as gr | |
import requests | |
import os | |
import json | |
api_key = os.getenv('API_KEY') | |
def call_llama_guard_api(content, assistant_response): | |
invoke_url = "https://api.nvcf.nvidia.com/v2/nvcf/pexec/functions/b34280ac-24e4-4081-bfaa-501e9ee16b6f" | |
headers = { | |
"Authorization": f"Bearer {api_key}", | |
"Accept": "application/json", | |
} | |
payload = { | |
"messages": [ | |
{"content": content, "role": "user"}, | |
{"content": assistant_response, "role": "assistant"} | |
] | |
} | |
session = requests.Session() | |
response = session.post(invoke_url, headers=headers, json=payload) | |
while response.status_code == 202: | |
request_id = response.headers.get("NVCF-REQID") | |
fetch_url = f"https://api.nvcf.nvidia.com/v2/nvcf/pexec/status/{request_id}" | |
response = session.get(fetch_url, headers=headers) | |
response.raise_for_status() | |
response_body = response.json() | |
print(response_body) | |
return response_body | |
content_input = gr.Textbox(lines=2, placeholder="Enter your content here...", label="User Content") | |
assistant_response_input = gr.Textbox(lines=2, placeholder="Enter assistant's response here...", label="Assistant Response") | |
iface = gr.Interface(fn=call_llama_guard_api, | |
inputs=[content_input, assistant_response_input], | |
outputs="text", | |
title="Llama Guard Safety Classifier", | |
description="Classify the safety of LLM prompts and responses using Llama Guard" | |
) | |
iface.launch() | |