Create app.py

app.py

@@ -0,0 +1,44 @@
+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()