{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook I'm using the OpenPipe client to capture a set of calls to the OpenAI API.\n",
    "\n",
    "For this example I'll blithely throw engineering best practices to the wind and use the notebook itself to manage dependencies. 😁"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: openpipe==3.0.3 in /usr/local/lib/python3.10/dist-packages (3.0.3)\n",
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      "Requirement already satisfied: charset-normalizer<3,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai<0.28.0,>=0.27.8->openpipe==3.0.3) (2.1.1)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai<0.28.0,>=0.27.8->openpipe==3.0.3) (1.26.13)\n",
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      "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<0.28.0,>=0.27.8->openpipe==3.0.3) (1.9.2)\n",
      "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<0.28.0,>=0.27.8->openpipe==3.0.3) (1.4.0)\n",
      "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<0.28.0,>=0.27.8->openpipe==3.0.3) (1.3.1)\n",
      "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<5.0,>=3.0->httpcore<0.18.0,>=0.15.0->httpx<0.25.0,>=0.24.1->openpipe==3.0.3) (1.1.2)\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3.10 -m pip install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install openpipe==3.0.3 python-dotenv==1.0.0 joblib==1.3.2 datasets==2.14.4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When working with remote datasets (or any data, really), it's a good idea to visually inspect some samples to make sure it looks like you expect. I'll print a recipe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recipe dataset shape:\n",
      "------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['recipe'],\n",
       "    num_rows: 5000\n",
       "})"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First recipe:\n",
      "------------------ Shrimp Creole\n",
      "\n",
      "Ingredients:\n",
      "- 20 shrimp (8 oz.)\n",
      "- 2 c. (16 oz. can) tomato sauce\n",
      "- 1 small onion, chopped\n",
      "- 1 celery stalk, chopped\n",
      "- 1/4 green bell pepper, diced\n",
      "- 1/4 c. sliced mushrooms\n",
      "- 3 Tbsp. parsley\n",
      "- 1/2 tsp. pepper\n",
      "- 1 to 1-1/2 c. brown rice, prepared according to pkg. directions (not included in exchanges)\n",
      "\n",
      "Directions:\n",
      "- Peel, devein and wash shrimp; set aside.\n",
      "- (If shrimp are frozen, let thaw first in the refrigerator.)\n",
      "- Simmer tomato sauce, onion, celery, green pepper, mushrooms, parsley and pepper in skillet for 30 minutes.\n",
      "- Add shrimp and cook 10 to 15 minutes more, until shrimp are tender.\n",
      "- Serve over brown rice.\n",
      "- Serves 2.\n"
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "recipes = load_dataset(\"corbt/unlabeled-recipes\")[\"train\"]\n",
    "print(\"Recipe dataset shape:\\n------------------\")\n",
    "display(recipes)\n",
    "print(\"First recipe:\\n------------------\", recipes[\"recipe\"][0])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mm, delicious. Anyway, we need to generate a training dataset. We'll call GPT-4 on each of our examples.\n",
    "\n",
    "In this case, I'll ask GPT-4 to classify each recipe along 5 dimensions:\n",
    " - has_non_fish_meat\n",
    " - requires_oven\n",
    " - requires_stove\n",
    " - cook_time_over_30_mins\n",
    " - main_dish\n",
    "\n",
    "That looks like a pretty random list, but there's actually an important unifying thread: I'm looking for meals that my pescatarian brother/co-founder can make in his kitchen-less, near-window-less basement apartment in San Francisco! (If you haven't tried to get an apartment in SF you probably think I'm joking 😂.)\n",
    "\n",
    "I'll use [OpenPipe](https://github.com/openpipe/openpipe) to track the API calls and form a training dataset. To follow along you'll need to create a free OpenPipe account, then copy your API key from https://app.openpipe.ai/project/settings into a file called `.env`. You can see an example in [./.env.example](./.env.example)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifying first recipe:\n",
      "------------------\n",
      "{'has_non_fish_meat': False, 'requires_oven': False, 'requires_stove': True, 'cook_time_over_30_mins': True, 'main_dish': True}\n"
     ]
    }
   ],
   "source": [
    "from openpipe import openai, configure_openpipe\n",
    "import json\n",
    "import os\n",
    "import dotenv\n",
    "\n",
    "# Use `dotenv` to load the contents of the `.env` file into the environment\n",
    "dotenv.load_dotenv()\n",
    "\n",
    "# Configure OpenPipe using the API key from the environment\n",
    "configure_openpipe(api_key=os.environ[\"OPENPIPE_API_KEY\"])\n",
    "\n",
    "# Configure OpenAI using the API key from the environment\n",
    "openai.api_key = os.environ[\"OPENAI_API_KEY\"]\n",
    "\n",
    "\n",
    "def classify_recipe(recipe: str):\n",
    "    completion = openai.ChatCompletion.create(\n",
    "        model=\"gpt-4\",\n",
    "        messages=[\n",
    "            {\n",
    "                \"role\": \"system\",\n",
    "                \"content\": \"Your goal is to classify a recipe along several dimensions.Pay attention to the instructions.\",\n",
    "            },\n",
    "            {\n",
    "                \"role\": \"user\",\n",
    "                \"content\": recipe,\n",
    "            },\n",
    "        ],\n",
    "        functions=[\n",
    "            {\n",
    "                \"name\": \"classify\",\n",
    "                \"parameters\": {\n",
    "                    \"type\": \"object\",\n",
    "                    \"properties\": {\n",
    "                        \"has_non_fish_meat\": {\n",
    "                            \"type\": \"boolean\",\n",
    "                            \"description\": \"True if the recipe contains any meat or meat products (eg. chicken broth) besides fish\",\n",
    "                        },\n",
    "                        \"requires_oven\": {\n",
    "                            \"type\": \"boolean\",\n",
    "                            \"description\": \"True if the recipe requires an oven\",\n",
    "                        },\n",
    "                        \"requires_stove\": {\n",
    "                            \"type\": \"boolean\",\n",
    "                            \"description\": \"True if the recipe requires a stove\",\n",
    "                        },\n",
    "                        \"cook_time_over_30_mins\": {\n",
    "                            \"type\": \"boolean\",\n",
    "                            \"description\": \"True if the recipe takes over 30 minutes to prepare and cook, including waiting time\",\n",
    "                        },\n",
    "                        \"main_dish\": {\n",
    "                            \"type\": \"boolean\",\n",
    "                            \"description\": \"True if the recipe can be served as a main dish\",\n",
    "                        },\n",
    "                    },\n",
    "                    \"required\": [\n",
    "                        \"has_non_fish_meat\",\n",
    "                        \"requires_oven\",\n",
    "                        \"requires_stove\",\n",
    "                        \"cook_time_over_30_mins\",\n",
    "                        \"main_dish\",\n",
    "                    ],\n",
    "                },\n",
    "            }\n",
    "        ],\n",
    "        function_call={\n",
    "            \"name\": \"classify\",\n",
    "        },\n",
    "        openpipe={\"tags\": {\"prompt_id\": \"classify-recipe\"}, \"cache\": True},\n",
    "    )\n",
    "    return json.loads(completion.choices[0].message.function_call.arguments)\n",
    "\n",
    "\n",
    "print(\"Classifying first recipe:\\n------------------\")\n",
    "print(classify_recipe(recipes[\"recipe\"][0]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's working, so I'll go ahead and classify all 5000 recipes with GPT-4. Using GPT-4 for this is slowwww and costs about $40. The model I'm fine-tuning will be much faster -- we'll see if we can make it as good!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifying recipe 0/5000: Shrimp Creole\n",
      "Classifying recipe 100/5000: Spoon Bread\n",
      "Classifying recipe 200/5000: Quadrangle Grille'S Pumpkin-Walnut Cheesecake\n",
      "Classifying recipe 300/5000: Broccoli Casserole\n",
      "Classifying recipe 400/5000: Paal Payasam (3-Ingredient Rice Pudding)\n",
      "Classifying recipe 500/5000: Dirt Dessert\n",
      "Classifying recipe 600/5000: Dolma, Stuffed Dried Peppers And Eggplants\n",
      "Classifying recipe 700/5000: Party Pecan Pies\n",
      "Classifying recipe 800/5000: Pie Crust\n",
      "Classifying recipe 900/5000: Russian Dressing(Salad Dressing)  \n",
      "Classifying recipe 1000/5000: O'Brien Potatoes\n",
      "Classifying recipe 1100/5000: Monster Cookies\n",
      "Classifying recipe 1200/5000: Striped Fruit Pops\n",
      "Classifying recipe 1300/5000: Cute Heart-Shaped Fried Egg\n",
      "Classifying recipe 1400/5000: Steak Marinade\n",
      "Classifying recipe 1500/5000: Bbq Sauce For Fish Recipe\n",
      "Classifying recipe 1600/5000: Barbecue Ranch Salad\n",
      "Classifying recipe 1700/5000: White Fudge\n",
      "Classifying recipe 1800/5000: Seaton Chocolate Chip Cookies\n",
      "Classifying recipe 1900/5000: Beef Stroganoff\n",
      "Classifying recipe 2000/5000: Lemon Delight\n",
      "Classifying recipe 2100/5000: Cream Cheese Chicken Chili\n",
      "Classifying recipe 2200/5000: Bean Salad\n",
      "Classifying recipe 2300/5000: Green Beans Almondine\n",
      "Classifying recipe 2400/5000: Radish-And-Avocado Salad\n",
      "Classifying recipe 2500/5000: Salsa Rojo\n",
      "Classifying recipe 2600/5000: Pepperoni Bread\n",
      "Classifying recipe 2700/5000: Sabzi Polow\n",
      "Classifying recipe 2800/5000: Italian Vegetable Pizzas\n",
      "Classifying recipe 2900/5000: Hot Fudge Sauce, Soda Shop Style\n",
      "Classifying recipe 3000/5000: Meatball Soup With Vegetables And Brown Rice\n",
      "Classifying recipe 3100/5000: Herbed Potatoes And Onions\n",
      "Classifying recipe 3200/5000: Apple Crunch Pie (2 Extra Servings)\n",
      "Classifying recipe 3300/5000: Pineapple-Orange Punch\n",
      "Classifying recipe 3400/5000: Turkey Veggie Burgers With Avocado Mayo\n",
      "Classifying recipe 3500/5000: Pear & Goat Cheese Salad\n",
      "Classifying recipe 3600/5000: Triple Chocolate Cookies\n",
      "Classifying recipe 3700/5000: Strawberry Banana Yogurt Pops\n",
      "Classifying recipe 3800/5000: Chicken Croquettes\n",
      "Classifying recipe 3900/5000: Mushroom Casserole\n",
      "Classifying recipe 4000/5000: Vegetarian Summer Roll\n",
      "Classifying recipe 4100/5000: Prune Cake\n",
      "Classifying recipe 4200/5000: Strawberry Sorbet\n",
      "Classifying recipe 4300/5000: Lemonade Chicken\n",
      "Classifying recipe 4400/5000: Crock-Pot Vegetarian Chili\n",
      "Classifying recipe 4500/5000: Grandma Dickrell'S Molasses Cake - 1936\n",
      "Classifying recipe 4600/5000: Creamed Corn Casserole\n",
      "Classifying recipe 4700/5000: Homemade Croutons\n",
      "Classifying recipe 4800/5000: Potatoes With Leeks And Gruyere\n",
      "Classifying recipe 4900/5000: Chocolate Oatmeal Cookie\n"
     ]
    }
   ],
   "source": [
    "for i, recipe in enumerate(recipes[\"recipe\"]):\n",
    "    if i % 100 == 0:\n",
    "        recipe_title = recipe.split(\"\\n\")[0]\n",
    "        print(f\"Classifying recipe {i}/{len(recipes)}: {recipe_title}\")\n",
    "    try:\n",
    "        classify_recipe(recipe)\n",
    "    except Exception as e:\n",
    "        print(f\"Error classifying recipe {i}: {e}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, now that my recipes are classified I'll download the training data. \n",
    "\n",
    "Next up I'll train the model -- check out [./train.ipynb](./train.ipynb) for details! Just go to https://app.openpipe.ai/request-logs, select all the logs you created, and click \"Export\". The default 10% testing split is fine for this dataset size.\n",
    "\n",
    "I got two files from that: `train.jsonl` and `test.jsonl`. I moved both of them into this repository under `./data/`."
   ]
  }
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