ChatGPT-Notebook5.ipynb

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    {
      "cell_type": "markdown",
      "source": "# Transforming\n\nIn this notebook, we will explore how to use Large Language Models for text transformation tasks such as language translation, spelling and grammar checking, tone adjustment, and format conversion.\n\n## Setup",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import openai\nimport os\n\nfrom dotenv import load_dotenv, find_dotenv\n_ = load_dotenv(find_dotenv()) # read local .env file\n\nopenai.api_key  = os.getenv('OPENAI_API_KEY')",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "def get_completion(prompt, model=\"gpt-3.5-turbo\", temperature=0): \n    messages = [{\"role\": \"user\", \"content\": prompt}]\n    response = openai.ChatCompletion.create(\n        model=model,\n        messages=messages,\n        temperature=temperature, \n    )\n    return response.choices[0].message[\"content\"]",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "## Translation\n\nChatGPT is trained with sources in many languages. This gives the model the ability to do translation. Here are some examples of how to use this capability.",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "prompt = f\"\"\"\nTranslate the following English text to Spanish: \\ \n```Hi, I would like to order a blender```\n\"\"\"\nresponse = get_completion(prompt)\nprint(response)",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "prompt = f\"\"\"\nTell me which language this is: \n```Combien coûte le lampadaire?```\n\"\"\"\nresponse = get_completion(prompt)\nprint(response)",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "prompt = f\"\"\"\nTranslate the following  text to French and Spanish\nand English pirate: \\\n```I want to order a basketball```\n\"\"\"\nresponse = get_completion(prompt)\nprint(response)",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "prompt = f\"\"\"\nTranslate the following text to Spanish in both the \\\nformal and informal forms: \n'Would you like to order a pillow?'\n\"\"\"\nresponse = get_completion(prompt)\nprint(response)",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "### Universal Translator\nImagine you are in charge of IT at a large multinational e-commerce company. Users are messaging you with IT issues in all their native languages. Your staff is from all over the world and speaks only their native languages. You need a universal translator!",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "user_messages = [\n  \"La performance du système est plus lente que d'habitude.\",  # System performance is slower than normal         \n  \"Mi monitor tiene píxeles que no se iluminan.\",              # My monitor has pixels that are not lighting\n  \"Il mio mouse non funziona\",                                 # My mouse is not working\n  \"Mój klawisz Ctrl jest zepsuty\",                             # My keyboard has a broken control key\n  \"我的屏幕在闪烁\"                                               # My screen is flashing\n] ",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "for issue in user_messages:\n    prompt = f\"Tell me what language this is: ```{issue}```\"\n    lang = get_completion(prompt)\n    print(f\"Original message ({lang}): {issue}\")\n\n    prompt = f\"\"\"\n    Translate the following  text to English \\\n    and Korean: ```{issue}```\n    \"\"\"\n    response = get_completion(prompt)\n    print(response, \"\\n\")",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "## Try it yourself!\nTry some translations on your own!",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "## Tone Transformation\nWriting can vary based on the intended audience. ChatGPT can produce different tones.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "prompt = f\"\"\"\nTranslate the following from slang to a business letter: \n'Dude, This is Joe, check out this spec on this standing lamp.'\n\"\"\"\nresponse = get_completion(prompt)\nprint(response)",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "## Format Conversion\nChatGPT can translate between formats. The prompt should describe the input and output formats.",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "data_json = { \"resturant employees\" :[ \n    {\"name\":\"Shyam\", \"email\":\"shyamjaiswal@gmail.com\"},\n    {\"name\":\"Bob\", \"email\":\"bob32@gmail.com\"},\n    {\"name\":\"Jai\", \"email\":\"jai87@gmail.com\"}\n]}\n\nprompt = f\"\"\"\nTranslate the following python dictionary from JSON to an HTML \\\ntable with column headers and title: {data_json}\n\"\"\"\nresponse = get_completion(prompt)\nprint(response)",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "from IPython.display import display, Markdown, Latex, HTML, JSON\ndisplay(HTML(response))",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "## Spellcheck/Grammar check.\n\nHere are some examples of common grammar and spelling problems and the LLM's response. \n\nTo signal to the LLM that you want it to proofread your text, you instruct the model to 'proofread' or 'proofread and correct'.",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "text = [ \n  \"The girl with the black and white puppies have a ball.\",  # The girl has a ball.\n  \"Yolanda has her notebook.\", # ok\n  \"Its going to be a long day. Does the car need it’s oil changed?\",  # Homonyms\n  \"Their goes my freedom. There going to bring they’re suitcases.\",  # Homonyms\n  \"Your going to need you’re notebook.\",  # Homonyms\n  \"That medicine effects my ability to sleep. Have you heard of the butterfly affect?\", # Homonyms\n  \"This phrase is to cherck chatGPT for speling abilitty\"  # spelling\n]\nfor t in text:\n    prompt = f\"\"\"Proofread and correct the following text\n    and rewrite the corrected version. If you don't find\n    and errors, just say \"No errors found\". Don't use \n    any punctuation around the text:\n    ```{t}```\"\"\"\n    response = get_completion(prompt)\n    print(response)",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "text = f\"\"\"\nGot this for my daughter for her birthday cuz she keeps taking \\\nmine from my room.  Yes, adults also like pandas too.  She takes \\\nit everywhere with her, and it's super soft and cute.  One of the \\\nears is a bit lower than the other, and I don't think that was \\\ndesigned to be asymmetrical. It's a bit small for what I paid for it \\\nthough. I think there might be other options that are bigger for \\\nthe same price.  It arrived a day earlier than expected, so I got \\\nto play with it myself before I gave it to my daughter.\n\"\"\"\nprompt = f\"proofread and correct this review: ```{text}```\"\nresponse = get_completion(prompt)\nprint(response)",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "from redlines import Redlines\n\ndiff = Redlines(text,response)\ndisplay(Markdown(diff.output_markdown))",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "prompt = f\"\"\"\nproofread and correct this review. Make it more compelling. \nEnsure it follows APA style guide and targets an advanced reader. \nOutput in markdown format.\nText: ```{text}```\n\"\"\"\nresponse = get_completion(prompt)\ndisplay(Markdown(response))",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "## Try it yourself!\nTry changing the instructions to form your own review.",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Thanks to the following sites:\n\nhttps://writingprompts.com/bad-grammar-examples/\n",
      "metadata": {}
    }
  ]
}