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brockmanmatt/importantrecipestorygeneration.ipynb

Last active July 22, 2020 07:22
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ImportantRecipeStoryGeneration.ipynb
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importantrecipestorygeneration.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "ImportantRecipeStoryGeneration.ipynb",
"provenance": [],
"collapsed_sections": [],
"authorship_tag": "ABX9TyP6XXFoNvyKbITDSz8R12iP",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/brockmanmatt/0c803aa69374537f1ae99ea9cc91fac4/importantrecipestorygeneration.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "J7wnsgT2kPut",
"colab_type": "code",
"colab": {
"resources": {
"http://localhost:8080/nbextensions/google.colab/files.js": {
"data": 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",
"ok": true,
"headers": [
[
"content-type",
"application/javascript"
]
],
"status": 200,
"status_text": ""
}
},
"base_uri": "https://localhost:8080/",
"height": 89
},
"outputId": "40236e9c-14bc-47d6-bb6a-af9baf6e2aea"
},
"source": [
"from google.colab import files\n",
"uploaded = files.upload()\n",
"print(\"done\")"
],
"execution_count": 1,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"\n",
" <input type=\"file\" id=\"files-df6ff66b-5318-464c-86b6-0f5564350667\" name=\"files[]\" multiple disabled\n",
" style=\"border:none\" />\n",
" <output id=\"result-df6ff66b-5318-464c-86b6-0f5564350667\">\n",
" Upload widget is only available when the cell has been executed in the\n",
" current browser session. Please rerun this cell to enable.\n",
" </output>\n",
" <script src=\"/nbextensions/google.colab/files.js\"></script> "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Saving key.json to key.json\n",
"done\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WHPHrUnhpKnI",
"colab_type": "text"
},
"source": [
"I'll install the API"
]
},
{
"cell_type": "code",
"metadata": {
"id": "zq0ltp2xn4yt",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 292
},
"outputId": "6d50d382-ab2c-415e-c8c1-48eba129d070"
},
"source": [
"!pip install openai\n",
"import openai, json, pandas as pd"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting openai\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/a8/65/c7461f4c87984534683f480ea5742777bc39bbf5721123194c2d0347dc1f/openai-0.2.4.tar.gz (157kB)\n",
"\r\u001b[K |██ | 10kB 17.2MB/s eta 0:00:01\r\u001b[K |████▏ | 20kB 1.5MB/s eta 0:00:01\r\u001b[K |██████▎ | 30kB 2.0MB/s eta 0:00:01\r\u001b[K |████████▍ | 40kB 1.6MB/s eta 0:00:01\r\u001b[K |██████████▍ | 51kB 1.9MB/s eta 0:00:01\r\u001b[K |████████████▌ | 61kB 2.1MB/s eta 0:00:01\r\u001b[K |██████████████▋ | 71kB 2.3MB/s eta 0:00:01\r\u001b[K |████████████████▊ | 81kB 2.5MB/s eta 0:00:01\r\u001b[K |██████████████████▊ | 92kB 2.7MB/s eta 0:00:01\r\u001b[K |████████████████████▉ | 102kB 2.6MB/s eta 0:00:01\r\u001b[K |███████████████████████ | 112kB 2.6MB/s eta 0:00:01\r\u001b[K |█████████████████████████ | 122kB 2.6MB/s eta 0:00:01\r\u001b[K |███████████████████████████ | 133kB 2.6MB/s eta 0:00:01\r\u001b[K |█████████████████████████████▏ | 143kB 2.6MB/s eta 0:00:01\r\u001b[K |███████████████████████████████▎| 153kB 2.6MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 163kB 2.6MB/s \n",
"\u001b[?25hRequirement already satisfied: requests>=2.20 in /usr/local/lib/python3.6/dist-packages (from openai) (2.23.0)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (1.24.3)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (3.0.4)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (2020.6.20)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (2.10)\n",
"Building wheels for collected packages: openai\n",
" Building wheel for openai (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for openai: filename=openai-0.2.4-cp36-none-any.whl size=170709 sha256=1a72cb7266c719d767b1f593aeac1c26daff22d60d61a710c01e177212d68284\n",
" Stored in directory: /root/.cache/pip/wheels/74/96/c8/c6e170929c276b836613e1b9985343b501fe455e53d85e7d48\n",
"Successfully built openai\n",
"Installing collected packages: openai\n",
"Successfully installed openai-0.2.4\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Q2yE0jcnpMEV",
"colab_type": "text"
},
"source": [
"Loading in key.json that I uploaded; I do this so I don't need to worry about accidently leaking creds if I share the colab (which I'm 99% sure is just a json file that won't expose them)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "bwNXXwHen5x9",
"colab_type": "code",
"colab": {}
},
"source": [
"openai.api_key = json.load(open(\"key.json\", \"r\"))[\"key\"]"
],
"execution_count": 3,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "k67w5H0fpTkT",
"colab_type": "text"
},
"source": [
"Default keyword arguments to pass the aPI"
]
},
{
"cell_type": "code",
"metadata": {
"id": "e1EwpqqJkTYh",
"colab_type": "code",
"colab": {}
},
"source": [
"#arguments to send the API\n",
"kwargs = {\n",
"\"engine\":\"davinci\",\n",
"\"temperature\":.25,\n",
"\"max_tokens\":150,\n",
"\"stop\":\"\\n\\n\",\n",
"\"presence_penalty\":2\n",
"}"
],
"execution_count": 18,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "zZubgPoOpWDH",
"colab_type": "text"
},
"source": [
"Quick wrapper to automatically save prompts and responses sent for later analysis if needed"
]
},
{
"cell_type": "code",
"metadata": {
"id": "sXTDJx0An9Bl",
"colab_type": "code",
"colab": {}
},
"source": [
"import datetime\n",
"def query(prompt, myKwargs = kwargs):\n",
" \"\"\"\n",
" wrapper for the API to save the prompt and the result\n",
" \"\"\"\n",
"\n",
" r = openai.Completion.create(prompt=prompt, **myKwargs)[\"choices\"][0][\"text\"].strip()\n",
" with open(\"{}.json\".format(datetime.datetime.now().strftime(\"%Y%m%d%s\")), \"w\") as fh:\n",
" json.dump({\"prompt\":prompt, \"response\":r}, fh, indent=4)\n",
" return r"
],
"execution_count": 19,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "EdFXafcJpZ3Q",
"colab_type": "text"
},
"source": [
"Test to make sure my query works"
]
},
{
"cell_type": "code",
"metadata": {
"id": "4SlyKgjyopPn",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"outputId": "d47f5661-31bd-4cc3-d240-c2ca4d8aef7c"
},
"source": [
"newKwargs = kwargs.copy()\n",
"newKwargs[\"stop\"] = \"\\n\"\n",
"query(\"q: what is 1+1?\\na:\", newKwargs)"
],
"execution_count": 20,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic": {
"type": "string"
},
"text/plain": [
"'2'"
]
},
"metadata": {
"tags": []
},
"execution_count": 20
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "sfNm_crtUQ1K",
"colab_type": "code",
"colab": {}
},
"source": [
"prompt = \"\"\"INGREDIENTS: 1 cup couscous\n",
"1 1/4 cups boiling water\n",
"1 cup loosely packed cilantro, finely chopped\n",
"1 cup loosely packed Italian parsley, finely chopped\n",
"1/2 English cucumber, cut lengthwise and very thinly sliced\n",
"1/2 red onion, cut in half and shaved extremely thin\n",
"Origin (travel): Last week my husband and I packed up the car and drove nearly 500 miles to Atlanta to celebrate my sister’s wedding. I love a good road trip. It gives you time to think and to talk, and to watch the landscape of mountains and fields roll by. While free of the discomforts of plane travel, car travel does carry its own pitfalls, like the lure of fast food and gas station Slurpees. My strategy is to be prepared with something delicious and easy, like this couscous salad — a refreshing lunch at any time, but especially on a long summer road trip.\n",
"(end)\n",
"\n",
"INGREDIENTS: For the minestrone:\n",
"1 pound dried butter beans\n",
"1/4 cup extra-virgin olive oil, divided\n",
"2 medium sweet onions, cut into small dice\n",
"2 quarts low-sodium vegetable or chicken broth\n",
"1 large carrot, cut into small dice\n",
"Origin (cookbook): Hugh calls this Southern minestrone from his cookbook The Chef and the Slow Cooker “a brothy celebration of all things vegetable,” and we have to agree. The star of this celebration? The humble butter bean — a legume that, when cooked properly, becomes nearly as creamy and smooth as its namesake.\n",
"(end)\n",
"\n",
"{}\n",
"Origin ({}):\"\"\""
],
"execution_count": 65,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "O6fNICrHUZhW",
"colab_type": "code",
"colab": {}
},
"source": [
"recipe=\"\"\"INGREDIENTS: 8 tbsp. unsalted butter (1 stick), room temperature\n",
"1 cup brown sugar, packed\n",
"2 tbsp. heavy cream\n",
"1 tsp. vanilla extract\n",
"½ tsp. kosher salt\n",
"1 cup flour\n",
" ½ cup chocolate chips\"\"\""
],
"execution_count": 66,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "YQzF7aCGUuzL",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "89234b41-9d33-4cc1-b3ae-6a741e04fba4"
},
"source": [
"myKwargs = kwargs.copy()\n",
"myKwargs[\"stop\"] = \"(end)\"\n",
"origin=\"boating\"\n",
"print(query(prompt=prompt.format(recipe, origin), myKwargs=myKwargs))"
],
"execution_count": 68,
"outputs": [
{
"output_type": "stream",
"text": [
"This is a recipe that I have been making for years. It's great to make on the boat because it doesn't require an oven and can be made in a saucepan over a stove or Sterno flame.\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "tbJS793hUx4o",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "bb88bf1d-2d13-4970-8a53-444fa99aa69f"
},
"source": [
"myKwargs = kwargs.copy()\n",
"myKwargs[\"stop\"] = \"(end)\"\n",
"origin=\"paragliding\"\n",
"print(query(prompt=prompt.format(recipe, origin), myKwargs=myKwargs))"
],
"execution_count": 70,
"outputs": [
{
"output_type": "stream",
"text": [
"The first time I ever flew a paraglider, it was on the beach in La Ventana, Mexico. It was my second day of flying and I had been having trouble with thermals all morning. My instructor suggested we take a break for lunch. We ate tacos at a little stand on the beach and then walked down to the water’s edge. He pulled out his glider and asked if I wanted to go up again.\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "8C_aaYt_W8Zl",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "2b0d8caa-5393-4e57-a806-d785d81a929d"
},
"source": [
"myKwargs = kwargs.copy()\n",
"myKwargs[\"stop\"] = \"(end)\"\n",
"origin=\"paragliding\"\n",
"print(query(prompt=prompt.format(recipe, origin), myKwargs=myKwargs))"
],
"execution_count": 71,
"outputs": [
{
"output_type": "stream",
"text": [
"I have a friend who has been paragliding for over 20 years. He’s done it all, from jumping off the Eiffel Tower to flying in tandem with his dog. The first time he took me up, we were soaring above a canyon and he told me that he was going to show me something special. Then he pulled out a bag of chocolate chips and started throwing them into the air. As they fell, we swooped down and caught them in our mouths.\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Rk2VZjLgY6b_",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "985907e1-c484-43ab-8acd-0cf4e2cc7683"
},
"source": [
"myKwargs = kwargs.copy()\n",
"myKwargs[\"stop\"] = \"(end)\"\n",
"origin=\"wedding\"\n",
"print(query(prompt=prompt.format(recipe, origin), myKwargs=myKwargs))"
],
"execution_count": 72,
"outputs": [
{
"output_type": "stream",
"text": [
"My sister’s wedding cake was a three-tiered, four-layer chocolate mousse cake with buttercream frosting. I made the cake and served it at my sister's rehearsal dinner on Friday night. It was a hit!\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Q14Bn6BWY7t0",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "92fd83a7-9d0e-4d4f-b782-6edfe84381cf"
},
"source": [
"myKwargs = kwargs.copy()\n",
"myKwargs[\"stop\"] = \"(end)\"\n",
"myKwargs[\"temperature\"] = .8\n",
"\n",
"origin=\"wedding\"\n",
"print(query(prompt=prompt.format(recipe, origin), myKwargs=myKwargs))"
],
"execution_count": 73,
"outputs": [
{
"output_type": "stream",
"text": [
"We made this cake for my sister’s bridal shower. She had a huge selection of delicious cakes and pastries to choose from, but since I knew she was also planning on having a giant dessert buffet, I decided that I would make a homemade version of the classic chocolate chip cookie instead.\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "mzYJk_z_Y_IB",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}
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