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    "## This demo app shows how to query Llama 3 using the Gradio UI.\n",
    "\n",
    "Since we are using OctoAI in this example, you'll need to obtain an OctoAI token:\n",
    "\n",
    "- You will need to first sign into [OctoAI](https://octoai.cloud/) with your Github or Google account\n",
    "- Then create a free API token [here](https://octo.ai/docs/getting-started/how-to-create-an-octoai-access-token) that you can use for a while (a month or $10 in OctoAI credits, whichever one runs out first)\n",
    "\n",
    "**Note** After the free trial ends, you will need to enter billing info to continue to use Llama 3 hosted on OctoAI.\n",
    "\n",
    "To run this example:\n",
    "- Run the notebook\n",
    "- Set up your OCTOAI API token and enter it when prompted\n",
    "- Enter your question and click Submit\n",
    "\n",
    "In the notebook or a browser with URL http://127.0.0.1:7860 you should see a UI with your answer.\n",
    "\n",
    "Let's start by installing the necessary packages:\n",
    "- openai for us to use its APIs to talk to the OctoAI endpoint\n",
    "- gradio is used for the UI elements\n",
    "\n",
    "And setting up the OctoAI token."
   ]
  },
  {
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   "execution_count": null,
   "id": "6ae4f858-6ef7-49d9-b45b-1ef79d0217a0",
   "metadata": {},
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   "source": [
    "!pip install openai gradio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3306c11d-ed82-41c5-a381-15fb5c07d307",
   "metadata": {},
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   "source": [
    "from getpass import getpass\n",
    "import os\n",
    "\n",
    "OCTOAI_API_TOKEN = getpass()\n",
    "os.environ[\"OCTOAI_API_TOKEN\"] = OCTOAI_API_TOKEN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "928041cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gradio as gr\n",
    "import openai\n",
    "\n",
    "# Init OctoAI client\n",
    "client = openai.OpenAI(\n",
    "    base_url=\"https://text.octoai.run/v1\",\n",
    "    api_key=os.environ[\"OCTOAI_API_TOKEN\"]\n",
    ")\n",
    "\n",
    "def predict(message, history):\n",
    "    history_openai_format = []\n",
    "    for human, assistant in history:\n",
    "        history_openai_format.append({\"role\": \"user\", \"content\": human})\n",
    "        history_openai_format.append({\"role\": \"assistant\", \"content\": assistant})\n",
    "    history_openai_format.append({\"role\": \"user\", \"content\": message})\n",
    "\n",
    "    response = client.chat.completions.create(\n",
    "        model = 'meta-llama-3-70b-instruct',\n",
    "        messages = history_openai_format,\n",
    "        temperature = 0.0,\n",
    "        stream = True\n",
    "     )\n",
    "\n",
    "    partial_message = \"\"\n",
    "    for chunk in response:\n",
    "        if chunk.choices[0].delta.content is not None:\n",
    "              partial_message = partial_message + chunk.choices[0].delta.content\n",
    "              yield partial_message\n",
    "\n",
    "gr.ChatInterface(predict).launch()"
   ]
  }
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