ChatGPT-Notebook6.ipynb

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    {
      "cell_type": "markdown",
      "source": "# Expanding\nIn this lesson, you will generate customer service emails that are tailored to each customer's review.\n\n## Setup",
      "metadata": {}
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    {
      "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): # Andrew mentioned that the prompt/ completion paradigm is preferable for this class\n    messages = [{\"role\": \"user\", \"content\": prompt}]\n    response = openai.ChatCompletion.create(\n        model=model,\n        messages=messages,\n        temperature=temperature, # this is the degree of randomness of the model's output\n    )\n    return response.choices[0].message[\"content\"]",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "## Customize the automated reply to a customer email",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# given the sentiment from the lesson on \"inferring\",\n# and the original customer message, customize the email\nsentiment = \"negative\"\n\n# review for a blender\nreview = f\"\"\"\nSo, they still had the 17 piece system on seasonal \\\nsale for around $49 in the month of November, about \\\nhalf off, but for some reason (call it price gouging) \\\naround the second week of December the prices all went \\\nup to about anywhere from between $70-$89 for the same \\\nsystem. And the 11 piece system went up around $10 or \\\nso in price also from the earlier sale price of $29. \\\nSo it looks okay, but if you look at the base, the part \\\nwhere the blade locks into place doesn’t look as good \\\nas in previous editions from a few years ago, but I \\\nplan to be very gentle with it (example, I crush \\\nvery hard items like beans, ice, rice, etc. in the \\ \nblender first then pulverize them in the serving size \\\nI want in the blender then switch to the whipping \\\nblade for a finer flour, and use the cross cutting blade \\\nfirst when making smoothies, then use the flat blade \\\nif I need them finer/less pulpy). Special tip when making \\\nsmoothies, finely cut and freeze the fruits and \\\nvegetables (if using spinach-lightly stew soften the \\ \nspinach then freeze until ready for use-and if making \\\nsorbet, use a small to medium sized food processor) \\ \nthat you plan to use that way you can avoid adding so \\\nmuch ice if at all-when making your smoothie. \\\nAfter about a year, the motor was making a funny noise. \\\nI called customer service but the warranty expired \\\nalready, so I had to buy another one. FYI: The overall \\\nquality has gone done in these types of products, so \\\nthey are kind of counting on brand recognition and \\\nconsumer loyalty to maintain sales. Got it in about \\\ntwo days.\n\"\"\"",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "prompt = f\"\"\"\nYou are a customer service AI assistant.\nYour task is to send an email reply to a valued customer.\nGiven the customer email delimited by ```, \\\nGenerate a reply to thank the customer for their review.\nIf the sentiment is positive or neutral, thank them for \\\ntheir review.\nIf the sentiment is negative, apologize and suggest that \\\nthey can reach out to customer service. \nMake sure to use specific details from the review.\nWrite in a concise and professional tone.\nSign the email as `AI customer agent`.\nCustomer review: ```{review}```\nReview sentiment: {sentiment}\n\"\"\"\nresponse = get_completion(prompt)\nprint(response)",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "## Remind the model to use details from the customer's email",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "prompt = f\"\"\"\nYou are a customer service AI assistant.\nYour task is to send an email reply to a valued customer.\nGiven the customer email delimited by ```, \\\nGenerate a reply to thank the customer for their review.\nIf the sentiment is positive or neutral, thank them for \\\ntheir review.\nIf the sentiment is negative, apologize and suggest that \\\nthey can reach out to customer service. \nMake sure to use specific details from the review.\nWrite in a concise and professional tone.\nSign the email as `AI customer agent`.\nCustomer review: ```{review}```\nReview sentiment: {sentiment}\n\"\"\"\nresponse = get_completion(prompt, temperature=0.7)\nprint(response)",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "## Try experimenting on your own!",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "",
      "metadata": {},
      "execution_count": null,
      "outputs": []
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