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rhymeLineGeneration.ipynb
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| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "name": "rhymeLineGeneration.ipynb", | |
| "provenance": [], | |
| "collapsed_sections": [], | |
| "authorship_tag": "ABX9TyM3Rv4zmmh3Akn0susbEOzp", | |
| "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/9c1b465ff0b853cef201dc7b7d607931/rhymelinegeneration.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": "b93186dc-eaaa-4d7d-9e0f-30ee7e5192d9" | |
| }, | |
| "source": [ | |
| "from google.colab import files\n", | |
| "uploaded = files.upload()\n", | |
| "print(\"done\")" | |
| ], | |
| "execution_count": 2, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/html": [ | |
| "\n", | |
| " <input type=\"file\" id=\"files-c258c032-6ae2-4c80-a48d-d42d7df6230c\" name=\"files[]\" multiple disabled\n", | |
| " style=\"border:none\" />\n", | |
| " <output id=\"result-c258c032-6ae2-4c80-a48d-d42d7df6230c\">\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": "88d8ac59-d8a5-4776-9486-d1bc7d5505f5" | |
| }, | |
| "source": [ | |
| "!pip install openai\n", | |
| "import openai, json, random, pandas as pd, numpy as np, re" | |
| ], | |
| "execution_count": 3, | |
| "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 20.4MB/s eta 0:00:01\r\u001b[K |████▏ | 20kB 6.9MB/s eta 0:00:01\r\u001b[K |██████▎ | 30kB 7.7MB/s eta 0:00:01\r\u001b[K |████████▍ | 40kB 8.7MB/s eta 0:00:01\r\u001b[K |██████████▍ | 51kB 7.1MB/s eta 0:00:01\r\u001b[K |████████████▌ | 61kB 7.8MB/s eta 0:00:01\r\u001b[K |██████████████▋ | 71kB 8.7MB/s eta 0:00:01\r\u001b[K |████████████████▊ | 81kB 9.2MB/s eta 0:00:01\r\u001b[K |██████████████████▊ | 92kB 9.4MB/s eta 0:00:01\r\u001b[K |████████████████████▉ | 102kB 9.8MB/s eta 0:00:01\r\u001b[K |███████████████████████ | 112kB 9.8MB/s eta 0:00:01\r\u001b[K |█████████████████████████ | 122kB 9.8MB/s eta 0:00:01\r\u001b[K |███████████████████████████ | 133kB 9.8MB/s eta 0:00:01\r\u001b[K |█████████████████████████████▏ | 143kB 9.8MB/s eta 0:00:01\r\u001b[K |███████████████████████████████▎| 153kB 9.8MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 163kB 9.8MB/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: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (2.10)\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", | |
| "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=b32fc534c67eb22610e64a381c620521c117d4e0a482957cba260e4e99428335\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": 4, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "k67w5H0fpTkT", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "Default keyword arguments to pass the aPI" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "CHHFR46YJ58i", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "# steps\n", | |
| "#### SELECT PREIVOUS SENTENCE TO RHYME NOT DONE, just taking one randomly ####\n", | |
| "# Generate new word for next sentence\n", | |
| "# generrate next sentence\n", | |
| "# cycle" | |
| ], | |
| "execution_count": 5, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "NAsuHFqGlbpk", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "def getPossibleNextRhymingWords(lines, lineNumberToRhyme, numWords=10, verbose=False):\n", | |
| " \"\"\"\n", | |
| " This generatess numWords words that rhyme with the lineNumberToRhyme of lines\n", | |
| " \"\"\"\n", | |
| " kwargs = {\n", | |
| " \"engine\":\"davinci\",\n", | |
| " \"temperature\":0,\n", | |
| " \"max_tokens\":20,\n", | |
| " \"stop\":\"\\n\",\n", | |
| " \"logprobs\":1,\n", | |
| " \"presence_penalty\":5,\n", | |
| " }\n", | |
| "\n", | |
| " rhymeLine = lines.split(\"\\n\")[lineNumberToRhyme]\n", | |
| " rhymeWord = re.sub(r'[^\\w\\s]','',rhymeLine).strip().split()[-1]\n", | |
| " if verbose:\n", | |
| " print(\"RHYMING FOR: {}\".format(rhymeWord))\n", | |
| "\n", | |
| " context = \"\"\"Words are rhyming if their endings sound the same. For example, 'snout' rhymes with 'about' and 'hurtle' rhymes with 'turtle'. Consider this poem:\n", | |
| "{}\n", | |
| "\n", | |
| "q: What is a word to add that rhymes with '{}'?\n", | |
| "\"\"\".format(lines, rhymeWord)\n", | |
| "\n", | |
| " prompt = context + \"a:\"\n", | |
| "\n", | |
| " possibilities = []\n", | |
| " attempt = 0\n", | |
| " while len(possibilities) < numWords:\n", | |
| " attempt += 1\n", | |
| " r = openai.Completion.create(prompt=prompt, **kwargs)\n", | |
| " newWord = r[\"choices\"][0][\"text\"].strip()\n", | |
| " newWord = re.sub(r'[^\\w\\s]', '', newWord)\n", | |
| " if verbose:\n", | |
| " print(\"attempt {}: {}/{}\".format(newWord, len(possibilities),attempt))\n", | |
| " if len(newWord.split()) > 2:\n", | |
| " continue\n", | |
| " if newWord in possibilities+[rhymeWord]:\n", | |
| " if kwargs[\"temperature\"] < 2:\n", | |
| " kwargs[\"temperature\"] += .1\n", | |
| " continue\n", | |
| " kwargs[\"temperature\"] = 0\n", | |
| " possibilities.append(newWord)\n", | |
| "\n", | |
| " prompt = context\n", | |
| " idx = 0\n", | |
| " for example in random.sample(possibilities, k=len(possibilities)):\n", | |
| " idx += 1\n", | |
| " prompt += \"{}: {}\\n\".format(idx, example)\n", | |
| " prompt += \"{}:\".format(len(possibilities)+1)\n", | |
| "\n", | |
| "\n", | |
| " return possibilities" | |
| ], | |
| "execution_count": 80, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "7U8pZBexmRv5", | |
| "colab_type": "code", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 527 | |
| }, | |
| "outputId": "3e6539d6-b389-4424-f48a-0d29b0c94bdd" | |
| }, | |
| "source": [ | |
| "partial = \"\"\"I met a traveler from a far off land,\n", | |
| "who said what is the meaning of this here sign,\"\"\"\n", | |
| "\n", | |
| "possibilities = getPossibleNextRhymingWords(partial, 1, numWords=5, verbose=True)" | |
| ], | |
| "execution_count": 81, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "RHYMING FOR: sign\n", | |
| "attempt sign: 0/1\n", | |
| "attempt sign: 0/2\n", | |
| "attempt rhyme: 0/3\n", | |
| "attempt rhyme: 1/4\n", | |
| "attempt rhyme: 1/5\n", | |
| "attempt rhyme: 1/6\n", | |
| "attempt rhyme: 1/7\n", | |
| "attempt sign: 1/8\n", | |
| "attempt time: 1/9\n", | |
| "attempt rhyme: 2/10\n", | |
| "attempt rhyme: 2/11\n", | |
| "attempt rhyme: 2/12\n", | |
| "attempt rhyme: 2/13\n", | |
| "attempt rhyme: 2/14\n", | |
| "attempt rhyme: 2/15\n", | |
| "attempt rhyme: 2/16\n", | |
| "attempt mine: 2/17\n", | |
| "attempt rhyme: 3/18\n", | |
| "attempt sign: 3/19\n", | |
| "attempt sign: 3/20\n", | |
| "attempt sign: 3/21\n", | |
| "attempt sign: 3/22\n", | |
| "attempt sign: 3/23\n", | |
| "attempt fine: 3/24\n", | |
| "attempt mine: 4/25\n", | |
| "attempt mine: 4/26\n", | |
| "attempt rhyme: 4/27\n", | |
| "attempt rhyme: 4/28\n", | |
| "attempt line: 4/29\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "J5HDBof0zhmj", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "# need additional checking, sometimes starts generating non-rhymes" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "ri_BcaloynIX", | |
| "colab_type": "code", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 35 | |
| }, | |
| "outputId": "c1181b79-14ff-400b-aaf7-b5699f11a041" | |
| }, | |
| "source": [ | |
| "\", \".join(possibilities)" | |
| ], | |
| "execution_count": 20, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "string" | |
| }, | |
| "text/plain": [ | |
| "'pine, mine, line, fine, nine'" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 20 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "e7WAT2PGxhLf", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "def getAvgUntilNewLineFromLogprobs(someDict):\n", | |
| " \"\"\"\n", | |
| " get mean logprob from the API JSON for a series, assumes the 'stop' token is '\\n'\n", | |
| " \"\"\"\n", | |
| "\n", | |
| " probs = dict(someDict)\n", | |
| " end = len(someDict[\"token_logprobs\"])\n", | |
| " if \"\\n\" in someDict[\"tokens\"]:\n", | |
| " end = someDict[\"tokens\"].index(\"\\n\")\n", | |
| " return np.mean(someDict[\"token_logprobs\"][:end])\n" | |
| ], | |
| "execution_count": 21, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "uaFQLyu8mnfM", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "def getBestNextLine(lines, possibilities, returnAll=False, verbose=False):\n", | |
| "\n", | |
| " \"\"\"\n", | |
| " Generates line to continue lines.\n", | |
| " each line ends with a possibleWord in possibilities\n", | |
| " default returns highest logprob line (like in best_of)\n", | |
| " with returnAll returns a pandas dataframe of the lines + logprobs\n", | |
| " \"\"\"\n", | |
| "\n", | |
| " rawContext = \"\"\"Line 1: I was walking outside when I saw a cat\n", | |
| "Line 2: When I came closer I saw clearly,\n", | |
| "Line 3: *illegible text* hat\n", | |
| "q: what does the illegible text say?\n", | |
| "a: on the feline's head was a tall texas hat\n", | |
| "\n", | |
| "Line 1: The bartender was pouring a drink,\n", | |
| "Line 2: While the weather outside turned sour,\n", | |
| "Line 3: *illegible text* think\n", | |
| "q: what does the illegible text say?\n", | |
| "a: and I was starting to think\n", | |
| "\n", | |
| "{}\n", | |
| "q: what does the illegible text say?\n", | |
| "a:\"\"\"\n", | |
| "\n", | |
| " sentenceKwargs = {\n", | |
| " \"engine\":\"davinci\",\n", | |
| " \"temperature\":.5,\n", | |
| " \"max_tokens\":25,\n", | |
| " \"stop\":\"\\n\",\n", | |
| " \"logprobs\":1,\n", | |
| " \"presence_penalty\":.3\n", | |
| " }\n", | |
| "\n", | |
| " good_sentences = []\n", | |
| " for word in possibilities:\n", | |
| " if verbose:\n", | |
| " print(\"TRYING {}\".format(word.upper()))\n", | |
| "\n", | |
| " examples = \"\"\n", | |
| " i = 0\n", | |
| " for line in lines.split(\"\\n\"):\n", | |
| " i+=1\n", | |
| " examples += \"Line {}: {}\\n\".format(i, line)\n", | |
| " examples += \"Line {}: *illegible text* {}\".format(i+1, word)\n", | |
| "\n", | |
| " context = rawContext.format(examples)\n", | |
| "\n", | |
| " for i in range(1,25):\n", | |
| " r = openai.Completion.create(prompt=context.format(word, word), **sentenceKwargs)\n", | |
| " newSentence = r[\"choices\"][0][\"text\"]\n", | |
| " lastWord = re.sub(r'[^\\w\\s]','',newSentence.strip().split()[-1])\n", | |
| " if lastWord.lower() == word.lower():\n", | |
| " if verbose:\n", | |
| " print(\"FOUN D IT! {} on try {}\".format(newSentence, i))\n", | |
| " good_sentences.append(r)\n", | |
| " break\n", | |
| " if i % 10 == 0:\n", | |
| " if verbose:\n", | |
| " print(\"{}: {}\".format(i, newSentence))\n", | |
| " \n", | |
| " texts = []\n", | |
| " logprobs = []\n", | |
| " for sentence in good_sentences:\n", | |
| " texts.append(sentence[\"choices\"][0][\"text\"])\n", | |
| " logprobs.append(sentence[\"choices\"][0][\"logprobs\"])\n", | |
| "\n", | |
| " df = pd.DataFrame(list(zip(texts, logprobs)), columns=[\"text\", \"logprobs\"])\n", | |
| " df[\"mean_logprob\"] = df.logprobs.apply(lambda x: getAvgUntilNewLineFromLogprobs(x))\n", | |
| "\n", | |
| " if returnAll:\n", | |
| " return df\n", | |
| " return df.loc[df.mean_logprob.idxmin(), \"text\"]" | |
| ], | |
| "execution_count": 74, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "TzO-8DAuw-ok", | |
| "colab_type": "code", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 187 | |
| }, | |
| "outputId": "6e3c568a-94b1-4e78-8df2-5f6b0f7049e2" | |
| }, | |
| "source": [ | |
| "test = getBestNextLine(partial, possibilities, returnAll=True, verbose=True)" | |
| ], | |
| "execution_count": 23, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "TRYING PINE\n", | |
| "FOUN D IT! I was thinking that the sign said pine on try 2\n", | |
| "TRYING MINE\n", | |
| "FOUN D IT! and I told him it was mine on try 1\n", | |
| "TRYING LINE\n", | |
| "FOUN D IT! I'm going to have to draw a line on try 3\n", | |
| "TRYING FINE\n", | |
| "FOUN D IT! and I said it means you have to be fine on try 4\n", | |
| "TRYING NINE\n", | |
| "FOUN D IT! he said the meaning of this here sign is nine on try 1\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "u9DizKlJFncQ", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "## k, try randomly rhyming lines and adding new ones" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "Q2_nKdS_xESy", | |
| "colab_type": "code", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 119 | |
| }, | |
| "outputId": "d4c39068-6bfe-4208-d1b9-9c42952316a4" | |
| }, | |
| "source": [ | |
| "testSentence = partial\n", | |
| "for i in range(3):\n", | |
| " possibilities = getPossibleNextRhymingWords(testSentence, random.randint(0,len(testSentence.split(\"\\n\"))-1), numWords=5)\n", | |
| " print(\"Posssibilities: {}\".format(\", \".join(possibilities)))\n", | |
| " newLine = getBestNextLine(testSentence, possibilities).strip()\n", | |
| " testSentence += \"\\n\" + newLine\n", | |
| " print(newLine)" | |
| ], | |
| "execution_count": 26, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Posssibilities: bine, vine, line, pine, shine\n", | |
| "that the answer to the question is pine\n", | |
| "Posssibilities: line, sign, plane, fine, mine\n", | |
| "I said that it's a pine needle plane\n", | |
| "Posssibilities: plane, line, vine, cone, tree\n", | |
| "but he was from another line\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "pPD6y9bqG6qk", | |
| "colab_type": "code", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 102 | |
| }, | |
| "outputId": "daa10e66-f224-441f-8ecc-545f2087eb8a" | |
| }, | |
| "source": [ | |
| "print(testSentence)" | |
| ], | |
| "execution_count": 28, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "I met a traveler from a far off land,\n", | |
| "who said what is the meaning of this here sign,\n", | |
| "that the answer to the question is pine\n", | |
| "I said that it's a pine needle plane\n", | |
| "but he was from another line\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "j7cn0WEf6E-X", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "# Final Poem! (well, it's horrible but that's not the point)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "Cmt6gAAhGeiT", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "def generateLineOnIssue(someContext):\n", | |
| " \"\"\"\n", | |
| " generates content about the context\n", | |
| " \"\"\"\n", | |
| "\n", | |
| " #arguments to send the API\n", | |
| " kwargs = {\n", | |
| " \"engine\":\"davinci\",\n", | |
| " \"temperature\":1,\n", | |
| " \"max_tokens\":100,\n", | |
| " \"stop\":\"\\n\\n\",\n", | |
| " }\n", | |
| " \n", | |
| " prompt = \"\"\"My friend wrote this story about a dog. It's only a few sentences:\n", | |
| "Oh dog, how I like your tail.\n", | |
| "You wag and run about.\n", | |
| "Every time you make it flail\n", | |
| "about in the air like a trout\n", | |
| "it's as if you're trying to flout\n", | |
| "although you run as fast as a smail\n", | |
| "\n", | |
| "My son wrote this poem about {}. It's a couple paragraphs, but I enjoyed it:\"\"\"\n", | |
| " r = openai.Completion.create(prompt=prompt.format(someContext), **kwargs)\n", | |
| " newWord = r[\"choices\"][0][\"text\"].strip()\n", | |
| " \n", | |
| " return newWord" | |
| ], | |
| "execution_count": 31, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "1I-uSz9bHk9_", | |
| "colab_type": "code", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 86 | |
| }, | |
| "outputId": "8f8efd32-c3c6-4bd9-d170-28f7ec1aceee" | |
| }, | |
| "source": [ | |
| "generateLineOnIssue(\"a cat sleeping\")" | |
| ], | |
| "execution_count": 32, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "string" | |
| }, | |
| "text/plain": [ | |
| "'His silky fur reflects a deep nocturnal state.\\nNo snoring cat that ever lived can compare,\\nHis snore is soft, his purr is slick\\n(wait: what breed of cat is he?)\\nA flamingo and a minimalist take a walk\\n(A flamingo? Yes, one does exist that looks like that)\\nA flamingo and a minimalist walk home overlooked\\n(A flamingo? Yes, one is abstract, like me?\\n('" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 32 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "slucojQLH2GD", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "def continueProseContext(someContext):\n", | |
| " \"\"\"\n", | |
| " generate new line from someContext\n", | |
| " \"\"\"\n", | |
| " #arguments to send the API\n", | |
| " kwargs = {\n", | |
| " \"engine\":\"davinci\",\n", | |
| " \"temperature\":1,\n", | |
| " \"max_tokens\":100,\n", | |
| " \"stop\":\"\\n\",\n", | |
| " }\n", | |
| " r = openai.Completion.create(prompt=someContext + \"\\n\", **kwargs)\n", | |
| " newLine = r[\"choices\"][0][\"text\"].strip()\n", | |
| " return newLine" | |
| ], | |
| "execution_count": 39, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "7YR9JKQxK-Gm", | |
| "colab_type": "code", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 102 | |
| }, | |
| "outputId": "7fa2d1ff-0e42-40f4-ea52-500268a0bdbf" | |
| }, | |
| "source": [ | |
| "print(testSentence)" | |
| ], | |
| "execution_count": 42, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "I met a traveler from a far off land,\n", | |
| "who said what is the meaning of this here sign,\n", | |
| "that the answer to the question is pine\n", | |
| "I said that it's a pine needle plane\n", | |
| "but he was from another line\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "LbwOhIKqIgFT", | |
| "colab_type": "code", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 35 | |
| }, | |
| "outputId": "baeaa4d6-286d-4374-874b-2057cfdef81c" | |
| }, | |
| "source": [ | |
| "continueProseContext(testSentence)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "string" | |
| }, | |
| "text/plain": [ | |
| "\"he said it's a pipe of tobacco\"" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 40 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "m-qzWDpbK35t", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "wg2Qj3NnLF6k", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "# K, now gonna keep in groups of 5 and do monorhyme - each stanza is same rhyme end" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "1AIFlSC86HuI", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "" | |
| ], | |
| "execution_count": 34, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "q9ZcQiXZIZup", | |
| "colab_type": "code", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 221 | |
| }, | |
| "outputId": "319cb039-e7d6-4f9e-ed5d-9f3ef4e58ecf" | |
| }, | |
| "source": [ | |
| "myIssue = \"I watched the cat chasing the mouse down the stairs\"\n", | |
| "verbose=True\n", | |
| "poem = []\n", | |
| "\n", | |
| "#make 4 graphs\n", | |
| "for graph in range(2):\n", | |
| " preceeding_poem = \"\\n\".join(poem)\n", | |
| " newGraph = []\n", | |
| "\n", | |
| " # add first line of graph\n", | |
| " if len(poem) == 0:\n", | |
| " newLine = myIssue\n", | |
| " else:\n", | |
| " newLine = \"\"\n", | |
| " while len(newLine) < 10:\n", | |
| " newLine = continueProseContext(preceeding_poem)\n", | |
| " newGraph.append(newLine)\n", | |
| " poem.append(newLine)\n", | |
| " print(newLine)\n", | |
| "\n", | |
| " #add 2 more lines per graph\n", | |
| " for line in range(4):\n", | |
| " preceeding_poem = \"\\n\".join(poem)\n", | |
| " preceeding_graph = \"\\n\".join(newGraph)\n", | |
| "\n", | |
| " possibilities = getPossibleNextRhymingWords(preceeding_graph, random.randint(0,len(preceeding_graph.split(\"\\n\"))-1), numWords=3)\n", | |
| " newLine = getBestNextLine(preceeding_poem, possibilities).strip()\n", | |
| "\n", | |
| " newGraph.append(newLine)\n", | |
| " poem.append(newLine)\n", | |
| "\n", | |
| " print(newLine)\n", | |
| " print(\"\")\n", | |
| "\n", | |
| "\n", | |
| "\n" | |
| ], | |
| "execution_count": 86, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "I watched the cat chasing the mouse down the stairs\n", | |
| "I watched the cat and the mouse run out of the case\n", | |
| "and I'm not sure how many mice\n", | |
| "and I saw the cat running in place\n", | |
| "the cat ran around in a race\n", | |
| "\n", | |
| "but it could never catch the mouse.\n", | |
| "and I saw the cat take a piece of cheese\n", | |
| "the mouse sees\n", | |
| "the cat's nose\n", | |
| "and I was thinking about my nose\n", | |
| "\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "l6wFnPPAfUiU", | |
| "colab_type": "code", | |
| "colab": {} | |
| }, | |
| "source": [ | |
| "" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| } | |
| ] | |
| } |
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