PR feedback

llama-guard-safe-chatbot/Llama-Guard-3-Ensuring-Safe-Chatbot.ipynb

@@ -17,7 +17,7 @@
     "\n",
     "[Meta's Llama Guard 3](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama-guard-3/) is an advanced content moderation tool designed to assess user messages for harmful content. By analyzing incoming messages, Llama Guard 3 can effectively identify and classify harmful content across 14 categories, including hate speech, threats, and misinformation. This proactive approach allows chatbots to filter out harmful messages, ensuring that the language model (LLM) does not engage with or respond to inappropriate content.\n",
     "\n",
-    "In this tutorial, we will demonstrate how utilizing Llama Guard 3 on Groq API effectively detects harmful content in user messages. By detecting harmful messages from users, you can ensure that your chatbot either does not respond or provides a generic or safe response that does not engage with the harmful content. We will provide examples of how the model identifies and classifies harmful messages across various categories, ensuring that inappropriate content is filtered out before the chatbot engages with users.\n",
+    "In this tutorial, we will build a chatbot to demonstrate how utilizing Llama Guard 3 with Groq API effectively detects harmful content in user messages. By detecting harmful messages from users, you can ensure that your chatbot either does not respond or provides a generic or safe response that does not engage with the harmful content. We will provide examples of how the model identifies and classifies harmful messages across various categories, ensuring that inappropriate content is filtered out before the chatbot engages with users.\n",
     "\n",
     "By implementing Llama Guard 3 through the [Groq API](https://console.groq.com/playground), you can significantly enhance user safety, uphold community standards, and create a respectful online environment. This proactive approach not only protects users but also contributes to a more positive and engaging experience. Let's get started!"
    ]
@@ -40,8 +40,9 @@
     "from groq import Groq\n",
     "import pandas as pd\n",
     "import random\n",
+    "import os\n",
     "\n",
-    "client = Groq()"
+    "client = Groq(os.getenv('GROQ_API_KEY'))"
    ]
   },
   {
@@ -182,7 +183,7 @@
    "id": "133d15c8-9a8a-4cae-a07e-998384c1d8eb",
    "metadata": {},
    "source": [
-    "We've provided Llama Guard 3 a user message, and it successfully identified the content as unsafe and in the S13 taxonomy (Elections). Now, let's try Llama Guard 3 with several prompts to see how effectively it classfies them. We'll use `llama-3.1-8b-instant` to generate example user messages, randomly choosing safe or unsafe, and run them through Llama Guard 3 to determine it."
+    "We've provided Llama Guard 3 a user message, and it successfully identified the content as unsafe and in the S13 taxonomy (Elections). Now, let's try Llama Guard 3 with several prompts to see how effectively it classifies them. We'll use `llama-3.1-8b-instant` to generate example user messages, randomly choosing safe or unsafe, and run them through Llama Guard 3 for classification"
    ]
   },
   {