3.0_Name_Generator_GPT_Gemini.ipynb

{
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      "source": [
        "# Name Generator (GPT - Gemini) v1\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FFPBKLapSCkM"
      },
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wFNV1e3ASJha"
      },
      "source": [
        "### Install the Python SDK for the Gemini API\n",
        "\n",
        "The Python SDK for the Gemini API, is contained in the [`google-generativeai`](https://pypi.org/project/google-generativeai/) package. Install the dependency using pip:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
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        "outputId": "c434596b-5840-4d50-c1d2-f02dd2b7e45b"
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      "outputs": [],
      "source": [
        "!pip install -q -U google-generativeai"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KCFF5VSTbcAR"
      },
      "source": [
        "### Import packages"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vRC2HngneEeQ"
      },
      "source": [
        "Import the necessary packages."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting python-dotenv\n",
            "  Downloading python_dotenv-1.0.1-py3-none-any.whl.metadata (23 kB)\n",
            "Downloading python_dotenv-1.0.1-py3-none-any.whl (19 kB)\n",
            "Installing collected packages: python-dotenv\n",
            "Successfully installed python-dotenv-1.0.1\n"
          ]
        }
      ],
      "source": [
        "!pip install python-dotenv"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "TS9l5igubpHO"
      },
      "outputs": [],
      "source": [
        "import textwrap\n",
        "\n",
        "import google.generativeai as genai\n",
        "\n",
        "from IPython.display import Markdown\n",
        "\n",
        "def to_markdown(text):\n",
        "  text = text.replace('•', '  *')\n",
        "  return Markdown(textwrap.indent(text, '> ', predicate=lambda _: True))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gHYFrFPjSGNq"
      },
      "source": [
        "### Setup your API key\n",
        "\n",
        "Before you can use the Gemini API, you must first obtain an API key. If you don't already have one, create a key with one click in Google AI Studio.\n",
        "\n",
        "<a class=\"button button-primary\" href=\"https://makersuite.google.com/app/apikey\" target=\"_blank\" rel=\"noopener noreferrer\">Get an API key</a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "ab9ASynfcIZn"
      },
      "outputs": [],
      "source": [
        "# Or use `os.getenv('GOOGLE_API_KEY')` to fetch an environment variable.\n",
        "import os\n",
        "from dotenv import load_dotenv\n",
        "\n",
        "# Load environment variables from .env file\n",
        "load_dotenv()\n",
        "\n",
        "# Access secrets from environment variables\n",
        "GOOGLE_API_KEY=os.getenv('GOOGLE_API_KEY')\n",
        "\n",
        "genai.configure(api_key=GOOGLE_API_KEY)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8ssbTMNVSMd-"
      },
      "source": [
        "## List models\n",
        "\n",
        "Now you're ready to call the Gemini API. Use `list_models` to see the available Gemini models:\n",
        "\n",
        "* `gemini-pro`: optimized for text-only prompts.\n",
        "* `gemini-pro-vision`: optimized for text-and-images prompts."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "id": "QvvWFy08e5c5",
        "outputId": "31dbc9e3-bcd8-402a-89ef-087a39c5066b"
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "models/gemini-1.0-pro\n",
            "models/gemini-1.0-pro-001\n",
            "models/gemini-1.0-pro-latest\n",
            "models/gemini-1.0-pro-vision-latest\n",
            "models/gemini-pro\n",
            "models/gemini-pro-vision\n"
          ]
        }
      ],
      "source": [
        "for m in genai.list_models():\n",
        "  if 'generateContent' in m.supported_generation_methods:\n",
        "    print(m.name)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FTl5NjtrhA0J"
      },
      "source": [
        "Note: For detailed information about the available models, including their capabilities and rate limits, see [Gemini models](https://ai.google.dev/models/gemini). There are options for requesting [rate limit increases](https://ai.google.dev/docs/increase_quota). The rate limit for Gemini-Pro models is 60 requests per minute (RPM).\n",
        "\n",
        "The `genai` package also supports the PaLM  family of models, but only the Gemini models support the generic, multimodal capabilities of the `generateContent` method."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LZfoK3I3hu6V"
      },
      "source": [
        "## Generate text from text inputs\n",
        "\n",
        "For text-only prompts, use the `gemini-pro` model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "2bcfnGEviwTI"
      },
      "outputs": [],
      "source": [
        "model = genai.GenerativeModel('gemini-pro')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WR_2A_sxk8sK"
      },
      "source": [
        "The `generate_content` method can handle a wide variety of use cases, including multi-turn chat and multimodal input, depending on what the underlying model supports. The available models only support text and images as input, and text as output.\n",
        "\n",
        "In the simplest case, you can pass a prompt string to the <a href=\"https://ai.google.dev/api/python/google/generativeai/GenerativeModel#generate_content\"><code>GenerativeModel.generate_content</code></a> method:"
      ]
    },
    {
      "cell_type": "code",
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        {
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          "text": [
            "CPU times: user 3.44 ms, sys: 552 µs, total: 3.99 ms\n",
            "Wall time: 2.47 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "\n",
        "startChar = \"J\"\n",
        "originCountry = \"Canada\"\n",
        "religion = \"Christian\"\n",
        "gender = \"Male\"\n",
        "\n",
        "\n",
        "prompt = f\"Suggest a 5 names that starts with {startChar} for a {religion} {gender} form {originCountry}. response with following text in formate: [<name1>, <name2>, <name3>, <name4>, <name5>]\"\n",
        "\n",
        "response = model.generate_content(prompt)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FbrR-n_qlpFd"
      },
      "source": [
        "In simple cases, the `response.text` accessor is all you need. To display formatted Markdown text, use the `to_markdown` function:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
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        "id": "G-zBkueElVEO",
        "outputId": "562aeaff-d01d-4782-aa71-2a379f213803"
      },
      "outputs": [
        {
          "data": {
            "text/markdown": [
              "> [Jatin, Jay, Jairam, Jagannath, Jeevan]"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "to_markdown(response.text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
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