From 33ac65e72a5ebf4d5ea3e51f2626d44801c76429 Mon Sep 17 00:00:00 2001 From: Aaron W Chen Date: Thu, 17 Oct 2024 21:59:40 -0700 Subject: [PATCH] Update poetry env with 1bn personas, test nbs --- .gitignore | 2 + main_example.py | 57 +- models_example.py | 51 + nbs/17_fastapi_notebook_refactor.ipynb | 830 + nbs/18_fastapi_edamam_check.ipynb | 29658 +++++++++++++++++++++++ nbs/19_exploring_personahub.ipynb | 689 + poetry.lock | 128 +- pyproject.toml | 1 + 8 files changed, 31359 insertions(+), 57 deletions(-) create mode 100644 models_example.py create mode 100644 nbs/17_fastapi_notebook_refactor.ipynb create mode 100644 nbs/18_fastapi_edamam_check.ipynb create mode 100644 nbs/19_exploring_personahub.ipynb diff --git a/.gitignore b/.gitignore index 24ec246..ed48915 100644 --- a/.gitignore +++ b/.gitignore @@ -178,3 +178,5 @@ models/ /data + +/secrets \ No newline at end of file diff --git a/main_example.py b/main_example.py index 4d8b136..e2270be 100644 --- a/main_example.py +++ b/main_example.py @@ -1,12 +1,22 @@ -from fastapi import FastAPI, HTTPException, Path, Query -from schemas_example import GenreURLChoices, BandBase, BandCreate, BandWithID +from fastapi import FastAPI, HTTPException, Path, Query, Depends +from models_example import GenreURLChoices, BandBase, BandCreate, Band, Album +from sqlmodel import Session, select from typing import Annotated +from contextlib import asynccontextmanager +from db_example import init_db, get_session + + +@asynccontextmanager +async def lifespan(app: FastAPI): + init_db() + yield + # set --port argument, can't use 8000, the default uvicorn # use localhost:{port} in browser # use localhost:{port}/docs to look at the interactive, automatically created documentation -app = FastAPI() +app = FastAPI(lifespan=lifespan) BANDS = [ @@ -28,8 +38,9 @@ async def bands( genre: GenreURLChoices | None = None, # has_albums: bool = False, name_query: Annotated[str | None, Query(max_length=10)] = None, -) -> list[BandWithID]: - band_list = [BandWithID(**b) for b in BANDS] + session: Session = Depends(get_session), +) -> list[Band]: + band_list = session.exec(select(Band)).all() if genre: band_list = [b for b in band_list if b.genre.value.lower() == genre.value] @@ -44,8 +55,11 @@ async def bands( @app.get("/bands/{band_id}") -async def band(band_id: Annotated[int, Path(title="The band ID")]) -> BandWithID: - band = next((BandWithID(**b) for b in BANDS if b["id"] == band_id), None) +async def band( + band_id: Annotated[int, Path(title="The band ID")], + session: Session = Depends(get_session), +) -> Band: + band = session.get(Band, band_id) # Aaron: I'm a little confused, could we use `get` instead? if band is None: @@ -54,15 +68,28 @@ async def band(band_id: Annotated[int, Path(title="The band ID")]) -> BandWithID return band -@app.get("/bands/genre/{genre}") -async def bands_for_genre(genre: GenreURLChoices) -> list[dict]: - # originally this allowed any string to be used as an input and a list comprehension was used to find the value. However, this could result in server/computer waste, so we refactored to use a custom class that was restricted to a known set of genres - return [b for b in BANDS if b["genre"].lower() == genre.value] +# @app.get("/bands/genre/{genre}") +# async def bands_for_genre(genre: GenreURLChoices) -> list[dict]: +# # originally this allowed any string to be used as an input and a list comprehension was used to find the value. However, this could result in server/computer waste, so we refactored to use a custom class that was restricted to a known set of genres +# return [b for b in BANDS if b["genre"].lower() == genre.value] @app.post("/bands") -async def create_band(band_data: BandCreate) -> BandWithID: - id = BANDS[-1]["id"] + 1 - band = BandWithID(id=id, **band_data.model_dump()).model_dump() - BANDS.append(band) +async def create_band( + band_data: BandCreate, session: Session = Depends(get_session) +) -> Band: + + band = Band(name=band_data.name, genre=band_data.genre) + session.add(band) + + if band_data.albums: + for album in band_data.albums: + album_obj = Album( + title=album.title, release_date=album.release_date, band=band + ) + session.add(album_obj) + + session.commit() + session.refresh(band) + return band diff --git a/models_example.py b/models_example.py new file mode 100644 index 0000000..c923a39 --- /dev/null +++ b/models_example.py @@ -0,0 +1,51 @@ +# BugBytes instructor likes to use the schemas.py file and add all the Pydantic schemas to that file +from datetime import date +from enum import Enum +from pydantic import BaseModel, field_validator +from sqlmodel import SQLModel, Field, Relationship + + +class GenreURLChoices(Enum): + ROCK = "rock" + ELECTRONIC = "electronic" + METAL = "metal" + HIP_HOP = "hip-hop" + SHOEGAZE = "shoegaze" + + +class GenreChoices(Enum): + ROCK = "Rock" + ELECTRONIC = "Electronic" + METAL = "Metal" + HIP_HOP = "Hip-Hop" + SHOEGAZE = "Shoegaze" + + +class AlbumBase(SQLModel): + title: str + release_date: date + band_id: int | None = Field(default=None, foreign_key="band.id") + + +class Album(AlbumBase, table=True): + id: int = Field(default=None, primary_key=True) + band: "Band" = Relationship(back_populates="albums") + + +class BandBase(SQLModel): + name: str + genre: GenreChoices + + +class BandCreate(BandBase): + albums: list[AlbumBase] | None = None + + # only pass because it is strictly inheriting and not adding other fields + @field_validator("genre", mode="before") + def title_case_genre(cls, value): + return value.title() + + +class Band(BandBase, table=True): + id: int = Field(default=None, primary_key=True) + albums: list[Album] = Relationship(back_populates="band") diff --git a/nbs/17_fastapi_notebook_refactor.ipynb b/nbs/17_fastapi_notebook_refactor.ipynb new file mode 100644 index 0000000..3f867a9 --- /dev/null +++ b/nbs/17_fastapi_notebook_refactor.ipynb @@ -0,0 +1,830 @@ +{ + "cells": [ + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "# Refactor MeaLeon into notebook friendly\n", + "---\n", + "description: Refactoring Flask App requests into more a notebook friendly iteration\n", + "output-file: template.html\n", + "title: Notebook refactor\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# | default_exp testing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "cannot create 'cython_function_or_method' instances", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 19\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodel_selection\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m train_test_split\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpipeline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m make_pipeline\n\u001b[0;32m---> 19\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mschemas\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m CustomSKLearnAnalyzer, CustomSKLearnWrapper\n\u001b[1;32m 20\u001b[0m \u001b[38;5;66;03m# from src.backend.embedding_creation.apply_stanza import CustomSKLearnAnalyzer\u001b[39;00m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;66;03m# from src.backend.embedding_creation.sklearn_transformer_as_mlflow_model import CustomSKLearnWrapper\u001b[39;00m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mraw_data_cleaning\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mraw_data_preprocessor\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mrdpp\u001b[39;00m\n", + "File \u001b[0;32m~/Repos/Projects/MeaLeon/schemas.py:11\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m List\n\u001b[1;32m 7\u001b[0m \u001b[38;5;66;03m# BACKEND\u001b[39;00m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;129m@dataclass\u001b[39m\n\u001b[0;32m---> 11\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mCustomSKLearnAnalyzer\u001b[39;00m(dataclass):\n\u001b[1;32m 12\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;124;03m This class handles allows sklearn text transformers to incorporate a Stanza pipeline with a custom analyzer\u001b[39;00m\n\u001b[1;32m 14\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;66;03m# Options: require a stanza_lang_str which will be used to download a Stanza model\u001b[39;00m\n", + "\u001b[0;31mTypeError\u001b[0m: cannot create 'cython_function_or_method' instances" + ] + } + ], + "source": [ + "# | hide\n", + "import dagshub\n", + "import dill as pickle\n", + "import joblib\n", + "import mlflow\n", + "from mlflow.models import infer_signature\n", + "import nbdev #; nbdev.nbdev_export()\n", + "from nbdev.showdoc import *\n", + "import pandas as pd\n", + "import re\n", + "from sklearn.feature_extraction.text import (\n", + " CountVectorizer\n", + " , TfidfTransformer\n", + " , TfidfVectorizer\n", + " , \n", + ")\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.pipeline import make_pipeline\n", + "from schemas import CustomSKLearnAnalyzer #, CustomSKLearnWrapper\n", + "# from src.backend.embedding_creation.apply_stanza import CustomSKLearnAnalyzer\n", + "# from src.backend.embedding_creation.sklearn_transformer_as_mlflow_model import CustomSKLearnWrapper\n", + "import src.backend.raw_data_cleaning.raw_data_preprocessor as rdpp\n", + "\n", + "import stanza\n", + "from tqdm import tqdm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Not sure how to resolve the ```TypeError: cannot create 'cython_function_or_method' instances``` as I don't seem to be explicitly calling cython" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Need to call DAGsHub to keep track of what we're doing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Repository initialized!\n",
+       "
\n" + ], + "text/plain": [ + "Repository initialized!\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#@markdown Enter the username of your DAGsHub account:\n", + "DAGSHUB_USER_NAME = \"AaronWChen\" #@param {type:\"string\"}\n", + "\n", + "#@markdown Enter the email for your DAGsHub account:\n", + "DAGSHUB_EMAIL = \"awc33@cornell.edu\" #@param {type:\"string\"}\n", + "\n", + "#@markdown Enter the repo name \n", + "DAGSHUB_REPO_NAME = \"MeaLeon\"\n", + "\n", + "#@markdown Enter the name of the branch you are working on \n", + "BRANCH = \"init_mealeon_to_notebook_refactor\"\n", + "dagshub.init(repo_name=DAGSHUB_REPO_NAME\n", + " , repo_owner=DAGSHUB_USER_NAME)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Things I need to do\n", + "\n", + "1. app.py calls find_similar_dishes, returns a render template\n", + "2. find_similar_dishes needs to call the recipe database, the sklearn model, the model-transformed database (ie, TFIDF word matrix), and the query (which needs to be transformed)\n", + " 1. Little confused by order; why would i need the original database if i can just call the model/vector-transformed version?\n", + " 1. Original database has things like url and ID, which could be needed later\n", + " 2. ~~Future vector data can use the same recipe_id unique key, but only have the ingredient vectors. Use unique key to join original...~~\n", + " 3. Wait, need cuisine filter to improve search results...so vector database should have cuisine and recipe_id\n", + " 4. From that, can call back to original database to get URLs and other metadata\n", + " 1. SQLModel query to join\n", + " 2. Sklearn model (really any model that transforms the query) needs to be loaded from MLflow\n", + " 1. Model will be used to transform query for similarity analysis\n", + " 2. MLflow load\n", + " 3. Vector database needs to be loaded from currently a json, but should switch to Vespa\n", + " 1. Wouldn't this need to be linked to the MLflow Model? DVC + Vespa?\n", + " 2. Mlflow or DVC load?\n", + " 4. Original recipe database might also be DVC?\n", + " 5. \n", + "3. original query should be formatted and stored into recipe database (CRUD)\n", + "4. this is called to edamam API\n", + "5. edamam return is currently model-transformed then cuisine filtered\n", + " 1. Swap this order so we don't have to process as much text\n", + "6. Vector comparison against filtered data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "# this is a custom function to be used with MLflow to get or create experiments (is from the MLflow team)\n", + "def get_mlflow_experiment_id(name):\n", + " # this function allows us to get the experiment ID from an experiment name\n", + " exp = mlflow.get_experiment_by_name(name)\n", + " if exp is None:\n", + " exp_id = mlflow.create_experiment(name)\n", + " return exp_id\n", + " return exp.experiment_id" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Starting DEV stage for TFIDF Encoded model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# mlflow.set_tracking_uri(f'https://dagshub.com/{DAGSHUB_USER_NAME}/MeaLeon.mlflow')\n", + "\n", + "# # starter idea for making an experiment name, can be the git branch, but need more specificity\n", + "# experiment_name = f\"{DAGSHUB_EMAIL}/DVC-MLflow-integration-test\"\n", + "# mlflow_exp_id = get_mlflow_experiment_id(experiment_name)\n", + "\n", + "# # define processed data location and data to be added to DVC\n", + "# processed_data_base = \"../data/processed\"\n", + "# transformed_recipes_parquet_path = processed_data_base + \"/transformed_recipes.parquet.gzip\"\n", + "# combined_df_path = processed_data_base + \"/combined_df.parquet.gzip\"\n", + "\n", + "\n", + "# # define model location\n", + "# model_directory = \"../models/sklearn_model\"\n", + "\n", + "# # Define the required artifacts associated with the saved custom pyfunc\n", + "# sklearn_model_path = model_directory + \"/python_model.pkl\"\n", + "# sklearn_transformer_path = model_directory + \"/sklearn_transformer.pkl\"\n", + "# # transformed_recipes_path = model_directory + \"/transformed_recipes.pkl\"\n", + "# combined_df_sample_path = model_directory + \"/combined_df_sample.parquet\"\n", + "\n", + "# artifacts = {'sklearn_model': sklearn_model_path,\n", + "# 'sklearn_transformer': sklearn_transformer_path,\n", + "# # 'transformed_recipes': transformed_recipes_path,\n", + "# # 'combined_data': combined_df_path,\n", + "# 'combined_data_sample': combined_df_sample_path\n", + "# }\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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dekhedaggregateRatingingredientsprepStepsreviewsCountwillMakeAgainPctingredients_lemmafiedcuisine_namephoto_filenamephoto_creditauthor_namedate_publishedrecipe_url
id
54a2b6b019925f464b373351How does fried chicken achieve No. 1 status? B...Pickle-Brined Fried Chicken3.11[1 tablespoons yellow mustard seeds, 1 tablesp...[Toast mustard and coriander seeds in a dry me...7100tablespoon yellow mustard seed brk tablespoon ...Missing Cuisine51247610_fried-chicken_1x1.jpgMichael Graydon and Nikole HerriottMissing Author Name2014-08-19 04:00:00+00:00https://www.epicurious.com/recipes/food/views/...
54a408a019925f464b3733bcSpinaci all'EbraicaSpinach Jewish Style3.22[3 pounds small-leaved bulk spinach, Salt, 1/2...[Remove the stems and roots from the spinach. ...580pound small leave bulk spinach brk salt brk cu...ItalianEP_12162015_placeholders_rustic.jpgPhoto by Chelsea Kyle, Prop Styling by Anna St...Edda Servi Machlin2008-09-09 04:00:00+00:00https://www.epicurious.com/recipes/food/views/...
54a408a26529d92b2c003631This majestic, moist, and richly spiced honey ...New Year’s Honey Cake3.62[3 1/2 cups all-purpose flour, 1 tablespoon ba...[I like this cake best baked in a 9-inch angel...10588cup purpose flour brk tablespoon baking powder...KosherEP_09022015_honeycake-2.jpgPhoto by Chelsea Kyle, Food Styling by Anna St...Marcy Goldman2008-09-10 04:00:00+00:00https://www.epicurious.com/recipes/food/views/...
54a408a66529d92b2c003638The idea for this sandwich came to me when my ...The B.L.A.—Bagel with Lox and Avocado4.00[1 small ripe avocado, preferably Hass (see No...[A short time before serving, mash avocado and...7100small ripe avocado hass see note brk teaspoon ...KosherEP_12162015_placeholders_casual.jpgPhoto by Chelsea Kyle, Prop Styling by Rhoda B...Faye Levy2008-09-08 04:00:00+00:00https://www.epicurious.com/recipes/food/views/...
54a408a719925f464b3733ccIn 1930, Simon Agranat, the chief justice of t...Shakshuka a la Doktor Shakshuka2.71[2 pounds fresh tomatoes, unpeeled and cut in ...[1. Place the tomatoes, garlic, salt, paprika,...783pound fresh tomato unpeeled cut quarter ounce ...KosherEP_12162015_placeholders_formal.jpgPhoto by Chelsea Kyle, Prop Styling by Rhoda B...Joan Nathan2008-09-09 04:00:00+00:00https://www.epicurious.com/recipes/food/views/...
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" + ], + "text/plain": [ + " dek \\\n", + "id \n", + "54a2b6b019925f464b373351 How does fried chicken achieve No. 1 status? B... \n", + "54a408a019925f464b3733bc Spinaci all'Ebraica \n", + "54a408a26529d92b2c003631 This majestic, moist, and richly spiced honey ... \n", + "54a408a66529d92b2c003638 The idea for this sandwich came to me when my ... \n", + "54a408a719925f464b3733cc In 1930, Simon Agranat, the chief justice of t... \n", + "\n", + " hed \\\n", + "id \n", + "54a2b6b019925f464b373351 Pickle-Brined Fried Chicken \n", + "54a408a019925f464b3733bc Spinach Jewish Style \n", + "54a408a26529d92b2c003631 New Year’s Honey Cake \n", + "54a408a66529d92b2c003638 The B.L.A.—Bagel with Lox and Avocado \n", + "54a408a719925f464b3733cc Shakshuka a la Doktor Shakshuka \n", + "\n", + " aggregateRating \\\n", + "id \n", + "54a2b6b019925f464b373351 3.11 \n", + "54a408a019925f464b3733bc 3.22 \n", + "54a408a26529d92b2c003631 3.62 \n", + "54a408a66529d92b2c003638 4.00 \n", + "54a408a719925f464b3733cc 2.71 \n", + "\n", + " ingredients \\\n", + "id \n", + "54a2b6b019925f464b373351 [1 tablespoons yellow mustard seeds, 1 tablesp... \n", + "54a408a019925f464b3733bc [3 pounds small-leaved bulk spinach, Salt, 1/2... \n", + "54a408a26529d92b2c003631 [3 1/2 cups all-purpose flour, 1 tablespoon ba... \n", + "54a408a66529d92b2c003638 [1 small ripe avocado, preferably Hass (see No... \n", + "54a408a719925f464b3733cc [2 pounds fresh tomatoes, unpeeled and cut in ... \n", + "\n", + " prepSteps \\\n", + "id \n", + "54a2b6b019925f464b373351 [Toast mustard and coriander seeds in a dry me... \n", + "54a408a019925f464b3733bc [Remove the stems and roots from the spinach. ... \n", + "54a408a26529d92b2c003631 [I like this cake best baked in a 9-inch angel... \n", + "54a408a66529d92b2c003638 [A short time before serving, mash avocado and... \n", + "54a408a719925f464b3733cc [1. Place the tomatoes, garlic, salt, paprika,... \n", + "\n", + " reviewsCount willMakeAgainPct \\\n", + "id \n", + "54a2b6b019925f464b373351 7 100 \n", + "54a408a019925f464b3733bc 5 80 \n", + "54a408a26529d92b2c003631 105 88 \n", + "54a408a66529d92b2c003638 7 100 \n", + "54a408a719925f464b3733cc 7 83 \n", + "\n", + " ingredients_lemmafied \\\n", + "id \n", + "54a2b6b019925f464b373351 tablespoon yellow mustard seed brk tablespoon ... \n", + "54a408a019925f464b3733bc pound small leave bulk spinach brk salt brk cu... \n", + "54a408a26529d92b2c003631 cup purpose flour brk tablespoon baking powder... \n", + "54a408a66529d92b2c003638 small ripe avocado hass see note brk teaspoon ... \n", + "54a408a719925f464b3733cc pound fresh tomato unpeeled cut quarter ounce ... \n", + "\n", + " cuisine_name \\\n", + "id \n", + "54a2b6b019925f464b373351 Missing Cuisine \n", + "54a408a019925f464b3733bc Italian \n", + "54a408a26529d92b2c003631 Kosher \n", + "54a408a66529d92b2c003638 Kosher \n", + "54a408a719925f464b3733cc Kosher \n", + "\n", + " photo_filename \\\n", + "id \n", + "54a2b6b019925f464b373351 51247610_fried-chicken_1x1.jpg \n", + "54a408a019925f464b3733bc EP_12162015_placeholders_rustic.jpg \n", + "54a408a26529d92b2c003631 EP_09022015_honeycake-2.jpg \n", + "54a408a66529d92b2c003638 EP_12162015_placeholders_casual.jpg \n", + "54a408a719925f464b3733cc EP_12162015_placeholders_formal.jpg \n", + "\n", + " photo_credit \\\n", + "id \n", + "54a2b6b019925f464b373351 Michael Graydon and Nikole Herriott \n", + "54a408a019925f464b3733bc Photo by Chelsea Kyle, Prop Styling by Anna St... \n", + "54a408a26529d92b2c003631 Photo by Chelsea Kyle, Food Styling by Anna St... \n", + "54a408a66529d92b2c003638 Photo by Chelsea Kyle, Prop Styling by Rhoda B... \n", + "54a408a719925f464b3733cc Photo by Chelsea Kyle, Prop Styling by Rhoda B... \n", + "\n", + " author_name date_published \\\n", + "id \n", + "54a2b6b019925f464b373351 Missing Author Name 2014-08-19 04:00:00+00:00 \n", + "54a408a019925f464b3733bc Edda Servi Machlin 2008-09-09 04:00:00+00:00 \n", + "54a408a26529d92b2c003631 Marcy Goldman 2008-09-10 04:00:00+00:00 \n", + "54a408a66529d92b2c003638 Faye Levy 2008-09-08 04:00:00+00:00 \n", + "54a408a719925f464b3733cc Joan Nathan 2008-09-09 04:00:00+00:00 \n", + "\n", + " recipe_url \n", + "id \n", + "54a2b6b019925f464b373351 https://www.epicurious.com/recipes/food/views/... \n", + "54a408a019925f464b3733bc https://www.epicurious.com/recipes/food/views/... \n", + "54a408a26529d92b2c003631 https://www.epicurious.com/recipes/food/views/... \n", + "54a408a66529d92b2c003638 https://www.epicurious.com/recipes/food/views/... \n", + "54a408a719925f464b3733cc https://www.epicurious.com/recipes/food/views/... " + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# this part can be done after a dvc pull\n", + "whole_nlp_df = pd.read_parquet(\"../data/processed/cleaned_df.parquet.gzip\")\n", + "whole_nlp_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# only process a handful of recipes using the already created models/artifacts\n", + "# need to call MLFlow experiment/model (which also tests this pathway)\n", + "# i forgot to register the existing model, but we should be able to load the old one\n", + "\n", + "loaded_summarizer = mlflow.pyfunc.load_model(f\"runs:/e3cf27f656504b0d9b6d5a8d4ce1abb2/models/sklearn_model\")\n", + "# loaded_summarizer.predict(xsum_sample['document'][0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "--------------------------------------------------------------------------------\n", + "sklearn fit transform on ingredients:\n", + "\n", + "\n", + "--------------------------------------------------------------------------------\n", + "Input Data: \n", + "id\n", + "54a2b6b019925f464b373351 tablespoon yellow mustard seed brk tablespoon ...\n", + "54a408a019925f464b3733bc pound small leave bulk spinach brk salt brk cu...\n", + "54a408a26529d92b2c003631 cup purpose flour brk tablespoon baking powder...\n", + "54a408a66529d92b2c003638 small ripe avocado hass see note brk teaspoon ...\n", + "54a408a719925f464b3733cc pound fresh tomato unpeeled cut quarter ounce ...\n", + " ... \n", + "59541a31bff3052847ae2107 tablespoon unsalt butter room temperature brk ...\n", + "5954233ad52ca90dc28200e7 tablespoon stick salt butter room temperature ...\n", + "595424c2109c972493636f83 tablespoon unsalted butter more greasing pan b...\n", + "5956638625dc3d1d829b7166 coarse salt brk lime wedge brk ounce tomato ju...\n", + "59566daa25dc3d1d829b7169 bottle millileter sour beer such almanac citra...\n", + "Name: ingredients_lemmafied, Length: 34756, dtype: object\n", + "\n", + "\n", + "--------------------------------------------------------------------------------\n", + "Input Data Shape: \n", + "(34756,)\n", + "\n", + "\n", + "--------------------------------------------------------------------------------\n", + "Random 3 Records from Input Data: \n", + "id\n", + "54a40caa19925f464b374017 boneless muscovy duck breast half pound total ...\n", + "55d4e08063b1ba1b5534b198 tablespoon white wine vinegar brk teaspoon sug...\n", + "54a43ad16529d92b2c019fc3 cup basmati rice ounce brk cup sweeten flake c...\n", + "Name: ingredients_lemmafied, dtype: object\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 34756/34756 [00:03<00:00, 10450.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "--------------------------------------------------------------------------------\n", + "Transformed Data:\n", + " 100g 125g 13x9x2 150g 1pound 1tablespoon \\\n", + "id \n", + "54a2b6b019925f464b373351 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "54a408a019925f464b3733bc 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "54a408a26529d92b2c003631 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "54a408a66529d92b2c003638 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "54a408a719925f464b3733cc 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + " 1teaspoon 200g 250g 2cup ... árbol divide \\\n", + "id ... \n", + "54a2b6b019925f464b373351 0.0 0.0 0.0 0.0 ... 0.0 \n", + "54a408a019925f464b3733bc 0.0 0.0 0.0 0.0 ... 0.0 \n", + "54a408a26529d92b2c003631 0.0 0.0 0.0 0.0 ... 0.0 \n", + "54a408a66529d92b2c003638 0.0 0.0 0.0 0.0 ... 0.0 \n", + "54a408a719925f464b3733cc 0.0 0.0 0.0 0.0 ... 0.0 \n", + "\n", + " árbol seed árbol seed remove árbol stem \\\n", + "id \n", + "54a2b6b019925f464b373351 0.0 0.0 0.0 \n", + "54a408a019925f464b3733bc 0.0 0.0 0.0 \n", + "54a408a26529d92b2c003631 0.0 0.0 0.0 \n", + "54a408a66529d92b2c003638 0.0 0.0 0.0 \n", + "54a408a719925f464b3733cc 0.0 0.0 0.0 \n", + "\n", + " árbol teaspoon árbol teaspoon crush \\\n", + "id \n", + "54a2b6b019925f464b373351 0.0 0.0 \n", + "54a408a019925f464b3733bc 0.0 0.0 \n", + "54a408a26529d92b2c003631 0.0 0.0 \n", + "54a408a66529d92b2c003638 0.0 0.0 \n", + "54a408a719925f464b3733cc 0.0 0.0 \n", + "\n", + " árbol teaspoon crush red árbol wipe \\\n", + "id \n", + "54a2b6b019925f464b373351 0.0 0.0 \n", + "54a408a019925f464b3733bc 0.0 0.0 \n", + "54a408a26529d92b2c003631 0.0 0.0 \n", + "54a408a66529d92b2c003638 0.0 0.0 \n", + "54a408a719925f464b3733cc 0.0 0.0 \n", + "\n", + " árbol wipe clean épice \n", + "id \n", + "54a2b6b019925f464b373351 0.0 0.0 \n", + "54a408a019925f464b3733bc 0.0 0.0 \n", + "54a408a26529d92b2c003631 0.0 0.0 \n", + "54a408a66529d92b2c003638 0.0 0.0 \n", + "54a408a719925f464b3733cc 0.0 0.0 \n", + "\n", + "[5 rows x 78381 columns]\n", + "\n", + "\n", + "--------------------------------------------------------------------------------\n", + "Random Sample of Combined Data:\n", + " 100g 125g 13x9x2 150g 1pound 1tablespoon \\\n", + "id \n", + "54a40caa19925f464b374017 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "54a43ad16529d92b2c019fc3 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "55d4e08063b1ba1b5534b198 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + " 1teaspoon 200g 250g 2cup ... árbol seed \\\n", + "id ... \n", + "54a40caa19925f464b374017 0.0 0.0 0.0 0.0 ... 0.0 \n", + "54a43ad16529d92b2c019fc3 0.0 0.0 0.0 0.0 ... 0.0 \n", + "55d4e08063b1ba1b5534b198 0.0 0.0 0.0 0.0 ... 0.0 \n", + "\n", + " árbol seed remove árbol stem árbol teaspoon \\\n", + "id \n", + "54a40caa19925f464b374017 0.0 0.0 0.0 \n", + "54a43ad16529d92b2c019fc3 0.0 0.0 0.0 \n", + "55d4e08063b1ba1b5534b198 0.0 0.0 0.0 \n", + "\n", + " árbol teaspoon crush árbol teaspoon crush red \\\n", + "id \n", + "54a40caa19925f464b374017 0.0 0.0 \n", + "54a43ad16529d92b2c019fc3 0.0 0.0 \n", + "55d4e08063b1ba1b5534b198 0.0 0.0 \n", + "\n", + " árbol wipe árbol wipe clean épice \\\n", + "id \n", + "54a40caa19925f464b374017 0.0 0.0 0.0 \n", + "54a43ad16529d92b2c019fc3 0.0 0.0 0.0 \n", + "55d4e08063b1ba1b5534b198 0.0 0.0 0.0 \n", + "\n", + " ingredients_lemmafied \n", + "id \n", + "54a40caa19925f464b374017 boneless muscovy duck breast half pound total ... \n", + "54a43ad16529d92b2c019fc3 cup basmati rice ounce brk cup sweeten flake c... \n", + "55d4e08063b1ba1b5534b198 tablespoon white wine vinegar brk teaspoon sug... \n", + "\n", + "[3 rows x 78382 columns]\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "413513de77ec40e097f0fe537db730da", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Downloading artifacts: 0%| | 0/1 [00:00str dicts\n", + " # Will be useful in STAGING/Evaluation\n", + " \n", + " # LOG MODEL\n", + " # Instantiate sklearn TFIDFVectorizer\n", + " sklearn_transformer = TfidfVectorizer(**sklearn_transformer_params)\n", + "\n", + " print('\\n')\n", + " print('-' * 80)\n", + " print('sklearn fit transform on ingredients:')\n", + "\n", + " model_input = whole_nlp_df['ingredients_lemmafied']\n", + "\n", + " print('\\n')\n", + " print('-' * 80)\n", + " print('Input Data: ')\n", + " print(model_input)\n", + "\n", + " print('\\n')\n", + " print('-' * 80)\n", + " print('Input Data Shape: ')\n", + " print(model_input.shape)\n", + "\n", + " random_sample = model_input.sample(3, random_state=200)\n", + "\n", + " print('\\n')\n", + " print('-' * 80)\n", + " print('Random 3 Records from Input Data: ')\n", + " print(random_sample)\n", + "\n", + " # Do fit transform on data\n", + " response = sklearn_transformer.fit_transform(tqdm(model_input)) \n", + " \n", + " transformed_recipe = pd.DataFrame(\n", + " response.toarray(),\n", + " columns=sklearn_transformer.get_feature_names_out(),\n", + " index=model_input.index\n", + " )\n", + "\n", + " signature = infer_signature(model_input=model_input,\n", + " model_output=transformed_recipe\n", + " )\n", + "\n", + " print('\\n')\n", + " print('-' * 80)\n", + " print('Transformed Data:')\n", + " print(transformed_recipe.head())\n", + " \n", + " combined_df = transformed_recipe.join(model_input, how='inner')\n", + " combined_df_sample = transformed_recipe.join(random_sample, how='inner')\n", + "\n", + " print('\\n')\n", + " print('-' * 80)\n", + " print('Random Sample of Combined Data:')\n", + " print(combined_df_sample.head())\n", + "\n", + " with open(sklearn_transformer_path, \"wb\") as fo:\n", + " pickle.dump(sklearn_transformer, fo)\n", + "\n", + " transformed_recipe.to_parquet(path=transformed_recipes_parquet_path, compression=\"gzip\")\n", + "\n", + " combined_df.to_parquet(path=combined_df_path, compression=\"gzip\")\n", + " \n", + " combined_df_sample.to_parquet(path=combined_df_sample_path)\n", + "\n", + " model_info = mlflow.pyfunc.log_model( \n", + " code_path=[\"../src/backend/\"],\n", + " python_model=CustomSKLearnWrapper(),\n", + " input_example=whole_nlp_df['ingredients_lemmafied'][0],\n", + " signature=signature, \n", + " artifact_path=\"sklearn_model\",\n", + " artifacts=artifacts\n", + " ) \n", + "\n", + " # since this uses a custom Stanza analyzer, we have to use a custom mlflow.Pyfunc.PythonModel" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/awchen/Repos/Projects/MeaLeon/.venv/lib/python3.10/site-packages/nbdev/export.py:73: UserWarning: Notebook '/home/awchen/Repos/Projects/MeaLeon/nbs/16_notebook_refactor.ipynb' uses `#|export` without `#|default_exp` cell.\n", + "Note nbdev2 no longer supports nbdev1 syntax. Run `nbdev_migrate` to upgrade.\n", + "See https://nbdev.fast.ai/getting_started.html for more information.\n", + " warn(f\"Notebook '{nbname}' uses `#|export` without `#|default_exp` cell.\\n\"\n" + ] + } + ], + "source": [ + "# | hide\n", + "nbdev.nbdev_export()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/nbs/18_fastapi_edamam_check.ipynb b/nbs/18_fastapi_edamam_check.ipynb new file mode 100644 index 0000000..d53e65c --- /dev/null +++ b/nbs/18_fastapi_edamam_check.ipynb @@ -0,0 +1,29658 @@ +{ + "cells": [ + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "# Refactor MeaLeon into notebook friendly\n", + "---\n", + "description: Refactoring Flask App requests into more a notebook friendly iteration\n", + "output-file: template.html\n", + "title: Notebook refactor\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# | default_exp testing FastAPI validation on Edamam API request" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# | hide\n", + "from datetime import datetime\n", + "from enum import Enum\n", + "import json\n", + "from pydantic import BaseModel\n", + "import requests" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# load Edamam credentials from secrets file\n", + "with open(\"../secrets/edamam.json\",\"r\") as f:\n", + " cred = json.load(f)\n", + " \n", + "app_id = cred[\"id\"]\n", + "app_id_s = f\"&app_id={app_id}\"\n", + "\n", + "app_key = cred[\"key\"]\n", + "app_key_s = f\"&app_key={app_key}\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# test recipe to look for\n", + "dish_name = \"buffalo%20wings\"\n", + "cuisine_name = \"American\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "https://api.edamam.com/api/recipes/v2?type=public&beta=false&q=buffalo%20wings&app_id=abaa034e&app_key=251fdd6b808c90fa3b5863d9943692e5\n", + "https://api.edamam.com/api/recipes/v2?type=public&beta=false&q=buffalo%20wings&app_id=abaa034e&app_key=251fdd6b808c90fa3b5863d9943692e5\n" + ] + } + ], + "source": [ + "now = datetime.now()\n", + "dt_string = now.strftime(\"%d_%m_%Y_%H_%M_%S\")\n", + "\n", + "# lay out the query base\n", + "# api_base = \"https://api.edamam.com/search?\"\n", + "\n", + "# v2 of edamam\n", + "api_base = \"https://api.edamam.com/api/recipes/v2?type=public&beta=false\"\n", + "\n", + "# Edamam query\n", + "# what is the better way to handle this API ID and Key\n", + "q = f\"{api_base}&q={dish_name}{app_id_s}{app_key_s}\"\n", + "print(q)\n", + "\n", + "api_call = api_base + f\"&q={dish_name}\" + app_id_s + app_key_s\n", + "print(api_call)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "working = 'https://api.edamam.com/api/recipes/v2?type=public&beta=false&q=buffalo%20wings&app_id=abaa034e&app_key=251fdd6b808c90fa3b5863d9943692e5'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "q == working" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "resp = requests.get(q)\n", + "resp" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'from': 1,\n", + " 'to': 20,\n", + " 'count': 1000,\n", + " '_links': {'next': {'href': 'https://api.edamam.com/api/recipes/v2?q=buffalo%20wings&app_key=251fdd6b808c90fa3b5863d9943692e5&_cont=CHcVQBtNNQphDmgVQntAEX4BYEtxBgoORGdDBmYSYFZ1AwUAUXlSC2QbNlZyDAAOFzBDUmpBNlIlVgZRSjNIBGZHNgYgAlYVLnlSVSBMPkd5BgNK&type=public&app_id=abaa034e&beta=false',\n", + " 'title': 'Next page'}},\n", + " 'hits': [{'recipe': {'uri': 'http://www.edamam.com/ontologies/edamam.owl#recipe_e2f7014774924ac19ed65b31dfe33667',\n", + " 'label': 'Vegan Cauliflower Buffalo Wings',\n", + " 'image': 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" 'foodId': 'food_axwuni1bjd81cybjn45rwb25o4jb',\n", + " 'image': 'https://www.edamam.com/food-img/768/768bea3c8421a7ae987bc00c4a2b82e8.jpg'},\n", + " {'text': '¼ cup mayonnaise',\n", + " 'quantity': 0.25,\n", + " 'measure': 'cup',\n", + " 'food': 'mayonnaise',\n", + " 'weight': 57.75,\n", + " 'foodCategory': 'condiments and sauces',\n", + " 'foodId': 'food_bu8t61zaplle7dbrzk81dbygq0qj',\n", + " 'image': 'https://www.edamam.com/food-img/577/577308a0422357885c94cc9b5f1f1862.jpg'},\n", + " {'text': '¼ cup sour cream',\n", + " 'quantity': 0.25,\n", + " 'measure': 'cup',\n", + " 'food': 'sour cream',\n", + " 'weight': 57.5,\n", + " 'foodCategory': 'Dairy',\n", + " 'foodId': 'food_adp9fcubzl3lr7bcvzn3rbfiiiwq',\n", + " 'image': 'https://www.edamam.com/food-img/f9d/f9d6183267b041b0aff9a10b89c9c15f.jpg'},\n", + " {'text': '1 tablespoon fresh lemon juice',\n", + " 'quantity': 1.0,\n", + " 'measure': 'tablespoon',\n", + " 'food': 'lemon juice',\n", + " 'weight': 15.2499999997422,\n", + " 'foodCategory': '100% juice',\n", + " 'foodId': 'food_bglm6vxahuauteb0n6ynfbg9eryu',\n", + " 'image': 'https://www.edamam.com/food-img/e31/e310952d214e78a4cb8b73f30ceeaaf2.jpg'},\n", + " {'text': 'Kosher salt, freshly ground pepper',\n", + " 'quantity': 0.0,\n", + " 'measure': None,\n", + " 'food': 'Kosher salt',\n", + " 'weight': 25.498356937690627,\n", + " 'foodCategory': 'Condiments and sauces',\n", + " 'foodId': 'food_a1vgrj1bs8rd1majvmd9ubz8ttkg',\n", + " 'image': 'https://www.edamam.com/food-img/694/6943ea510918c6025795e8dc6e6eaaeb.jpg'},\n", + " {'text': '2 tablespoons unsalted butter, melted',\n", + " 'quantity': 2.0,\n", + " 'measure': 'tablespoon',\n", + " 'food': 'unsalted butter',\n", + " 'weight': 28.4,\n", + " 'foodCategory': 'Dairy',\n", + " 'foodId': 'food_awz3iefajbk1fwahq9logahmgltj',\n", + " 'image': 'https://www.edamam.com/food-img/713/71397239b670d88c04faa8d05035cab4.jpg'},\n", + " {'text': '½ cup hot pepper sauce (such as Frank’s)',\n", + " 'quantity': 0.5,\n", + " 'measure': 'cup',\n", + " 'food': 'pepper sauce',\n", + " 'weight': 115.19999999415693,\n", + " 'foodCategory': 'canned soup',\n", + " 'foodId': 'food_a6201h1bu1m0tfbrvis6ma6nvhzv',\n", + " 'image': 'https://www.edamam.com/food-img/946/946c38a4c278da4361d2615d653d685a.jpg'},\n", + " {'text': '½ teaspoon cayenne pepper',\n", + " 'quantity': 0.5,\n", + " 'measure': 'teaspoon',\n", + " 'food': 'cayenne pepper',\n", + " 'weight': 0.9,\n", + " 'foodCategory': 'Condiments and sauces',\n", + " 'foodId': 'food_a8iooz3aris8gba605l07brngnrx',\n", + " 'image': 'https://www.edamam.com/food-img/374/3742b9434a0fb66a45e0dd6d227ba669.jpg'},\n", + " {'text': 'Kosher salt, freshly ground pepper',\n", + " 'quantity': 0.0,\n", + " 'measure': None,\n", + " 'food': 'Kosher salt',\n", + " 'weight': 25.498356937690627,\n", + " 'foodCategory': 'Condiments and sauces',\n", + " 'foodId': 'food_a1vgrj1bs8rd1majvmd9ubz8ttkg',\n", + " 'image': 'https://www.edamam.com/food-img/694/6943ea510918c6025795e8dc6e6eaaeb.jpg'},\n", + " {'text': 'Vegetable oil for frying (about 10 cups)',\n", + " 'quantity': 10.0,\n", + " 'measure': 'cup',\n", + " 'food': 'Vegetable oil',\n", + " 'weight': 2240.000000037872,\n", + " 'foodCategory': 'Oils',\n", + " 'foodId': 'food_bt1mzi2ah2sfg8bv7no1qai83w8s',\n", + " 'image': 'https://www.edamam.com/food-img/6e5/6e51a63a6300a8ea1b4c4cc68dfaba33.jpg'},\n", + " {'text': '¾ cup cornstarch',\n", + " 'quantity': 0.75,\n", + " 'measure': 'cup',\n", + " 'food': 'cornstarch',\n", + " 'weight': 96.0,\n", + " 'foodCategory': 'grains',\n", + " 'foodId': 'food_a6r17hrba37cqeabesko5a2gk233',\n", + " 'image': 'https://www.edamam.com/food-img/f9b/f9b74d9495b40c0aea955c37a1fc39dc.jpg'},\n", + " {'text': '3 pounds chicken wings, tips removed, drumettes and flats separated',\n", + " 'quantity': 3.0,\n", + " 'measure': 'pound',\n", + " 'food': 'chicken wings',\n", + " 'weight': 1360.77711,\n", + " 'foodCategory': 'Poultry',\n", + " 'foodId': 'food_aftnyj9ap60fc6av2a9nfbju90c1',\n", + " 'image': 'https://www.edamam.com/food-img/f92/f928682fc890edded472c5d8baeffed5.jpg'},\n", + " {'text': '4 celery stalks, cut into thin sticks',\n", + " 'quantity': 4.0,\n", + " 'measure': 'stalk',\n", + " 'food': 'celery',\n", + " 'weight': 160.0,\n", + " 'foodCategory': 'vegetables',\n", + " 'foodId': 'food_bffeoksbyyur8ja4da73ub2xs57g',\n", + " 'image': 'https://www.edamam.com/food-img/d91/d91d2aed1c36d8fad54c4d7dc58f5a18.jpg'}],\n", + " 'calories': 6642.749039139966,\n", + " 'totalCO2Emissions': 27046.131871138503,\n", + " 'co2EmissionsClass': 'G',\n", + " 'totalWeight': 2314.574586761791,\n", + " 'totalTime': 0.0,\n", + " 'cuisineType': ['american'],\n", + " 'mealType': ['lunch/dinner'],\n", + " 'dishType': ['main course'],\n", + " 'totalNutrients': {'ENERC_KCAL': {'label': 'Energy',\n", + " 'quantity': 6642.749039139966,\n", + " 'unit': 'kcal'},\n", + " 'FAT': {'label': 'Fat', 'quantity': 575.5493418716198, 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{'label': 'Sodium', 'quantity': 5418.250804120742, 'unit': 'mg'},\n", + " 'CA': {'label': 'Calcium', 'quantity': 662.3804462995172, 'unit': 'mg'},\n", + " 'MG': {'label': 'Magnesium', 'quantity': 272.3436182371924, 'unit': 'mg'},\n", + " 'K': {'label': 'Potassium', 'quantity': 3479.5467540913205, 'unit': 'mg'},\n", + " 'FE': {'label': 'Iron', 'quantity': 7.918456749346747, 'unit': 'mg'},\n", + " 'ZN': {'label': 'Zinc', 'quantity': 18.868422661243443, 'unit': 'mg'},\n", + " 'P': {'label': 'Phosphorus',\n", + " 'quantity': 2065.6186542868368,\n", + " 'unit': 'mg'},\n", + " 'VITA_RAE': {'label': 'Vitamin A',\n", + " 'quantity': 572.0010514745326,\n", + " 'unit': 'µg'},\n", + " 'VITC': {'label': 'Vitamin C',\n", + " 'quantity': 98.84894999552961,\n", + " 'unit': 'mg'},\n", + " 'THIA': {'label': 'Thiamin (B1)',\n", + " 'quantity': 0.8666913628103347,\n", + " 'unit': 'mg'},\n", + " 'RIBF': {'label': 'Riboflavin (B2)',\n", + " 'quantity': 2.01499427997017,\n", + " 'unit': 'mg'},\n", + " 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" 'weight': 100.0,\n", + " 'foodCategory': 'Eggs',\n", + " 'foodId': 'food_bhpradua77pk16aipcvzeayg732r',\n", + " 'image': 'https://www.edamam.com/food-img/a7e/a7ec7c337cb47c6550b3b118e357f077.jpg'},\n", + " {'text': '1/4 cup whole milk',\n", + " 'quantity': 0.25,\n", + " 'measure': 'cup',\n", + " 'food': 'whole milk',\n", + " 'weight': 61.0,\n", + " 'foodCategory': 'Milk',\n", + " 'foodId': 'food_b49rs1kaw0jktabzkg2vvanvvsis',\n", + " 'image': 'https://www.edamam.com/food-img/7c9/7c9962acf83654a8d98ea6a2ade93735.jpg'},\n", + " {'text': 'Oil, for deep-frying',\n", + " 'quantity': 0.0,\n", + " 'measure': None,\n", + " 'food': 'Oil',\n", + " 'weight': 29.624386557686194,\n", + " 'foodCategory': 'Oils',\n", + " 'foodId': 'food_bk9p9aaavhvoq4bqsnprobpsiuxs',\n", + " 'image': 'https://www.edamam.com/food-img/07e/07e106ab3536d57428e5c46d009038f8.jpg'},\n", + " {'text': '1 cup sour cream',\n", + " 'quantity': 1.0,\n", + " 'measure': 'cup',\n", + " 'food': 'sour cream',\n", + " 'weight': 230.0,\n", + " 'foodCategory': 'Dairy',\n", + " 'foodId': 'food_adp9fcubzl3lr7bcvzn3rbfiiiwq',\n", + " 'image': 'https://www.edamam.com/food-img/f9d/f9d6183267b041b0aff9a10b89c9c15f.jpg'},\n", + " {'text': '1/2 cup mayonnaise',\n", + " 'quantity': 0.5,\n", + " 'measure': 'cup',\n", + " 'food': 'mayonnaise',\n", + " 'weight': 115.5,\n", + " 'foodCategory': 'condiments and sauces',\n", + " 'foodId': 'food_bu8t61zaplle7dbrzk81dbygq0qj',\n", + " 'image': 'https://www.edamam.com/food-img/577/577308a0422357885c94cc9b5f1f1862.jpg'},\n", + " {'text': '1 lemon, juiced',\n", + " 'quantity': 1.0,\n", + " 'measure': '',\n", + " 'food': 'lemon',\n", + " 'weight': 84.0,\n", + " 'foodCategory': 'fruit',\n", + " 'foodId': 'food_a6uzc62astrxcgbtzyq59b6fncrr',\n", + " 'image': 'https://www.edamam.com/food-img/70a/70acba3d4c734d7c70ef4efeed85dc8f.jpg'},\n", + " {'text': '1 tablespoon white vinegar',\n", + " 'quantity': 1.0,\n", + " 'measure': 'tablespoon',\n", + " 'food': 'white vinegar',\n", + " 'weight': 14.9,\n", + " 'foodCategory': 'Condiments and sauces',\n", + " 'foodId': 'food_am3vwadag9arxtadrwyfcau2w3b2',\n", + " 'image': 'https://www.edamam.com/food-img/5f6/5f69b84c399d778c4728e9ab4f8065a2.jpg'},\n", + " {'text': '1/4 cup chopped parsley leaves',\n", + " 'quantity': 0.25,\n", + " 'measure': 'cup',\n", + " 'food': 'parsley',\n", + " 'weight': 15.0,\n", + " 'foodCategory': 'vegetables',\n", + " 'foodId': 'food_b244pqdazw24zobr5vqu2bf0uid8',\n", + " 'image': 'https://www.edamam.com/food-img/46a/46a132e96626d7989b4d6ed8c91f4da0.jpg'},\n", + " {'text': '1 teaspoon minced shallots',\n", + " 'quantity': 1.0,\n", + " 'measure': 'teaspoon',\n", + " 'food': 'shallots',\n", + " 'weight': 3.3333333335587603,\n", + " 'foodCategory': 'vegetables',\n", + " 'foodId': 'food_bu2ver7a5f52dfap8q9f0bn085qb',\n", + " 'image': 'https://www.edamam.com/food-img/d23/d23e3be21df05e9e16c05eadb12341e7.jpeg'},\n", + " {'text': '1/2 teaspoon minced garlic',\n", + " 'quantity': 0.5,\n", + " 'measure': 'teaspoon',\n", + " 'food': 'garlic',\n", + " 'weight': 1.4,\n", + " 'foodCategory': 'vegetables',\n", + " 'foodId': 'food_avtcmx6bgjv1jvay6s6stan8dnyp',\n", + " 'image': 'https://www.edamam.com/food-img/6ee/6ee142951f48aaf94f4312409f8d133d.jpg'},\n", + " {'text': '1/4 pound blue cheese, crumbled',\n", + " 'quantity': 0.25,\n", + " 'measure': 'pound',\n", + " 'food': 'blue cheese',\n", + " 'weight': 113.3980925,\n", + " 'foodCategory': 'Cheese',\n", + " 'foodId': 'food_ao35kxmb5lh3lpaeaxh9ka2vfaf4',\n", + " 'image': 'https://www.edamam.com/food-img/b44/b442cfc174a1a691dca574c9b7dcb47a.jpg'},\n", + " {'text': 'Celery sticks, for serving',\n", + " 'quantity': 1.0,\n", + " 'measure': 'stick',\n", + " 'food': 'Celery',\n", + " 'weight': 40.0,\n", + " 'foodCategory': 'vegetables',\n", + " 'foodId': 'food_bffeoksbyyur8ja4da73ub2xs57g',\n", + " 'image': 'https://www.edamam.com/food-img/d91/d91d2aed1c36d8fad54c4d7dc58f5a18.jpg'}],\n", + " 'calories': 6950.19208661663,\n", + " 'totalCO2Emissions': 18428.334732106174,\n", + " 'co2EmissionsClass': 'G',\n", + " 'totalWeight': 2506.963712444194,\n", + " 'totalTime': 85.0,\n", + " 'cuisineType': ['american'],\n", + " 'mealType': ['lunch/dinner'],\n", + " 'dishType': ['main course'],\n", + " 'totalNutrients': {'ENERC_KCAL': {'label': 'Energy',\n", + " 'quantity': 6950.19208661663,\n", + " 'unit': 'kcal'},\n", + " 'FAT': {'label': 'Fat', 'quantity': 606.284040848015, 'unit': 'g'},\n", + " 'FASAT': {'label': 'Saturated',\n", + " 'quantity': 111.92930161513456,\n", + " 'unit': 'g'},\n", + " 'FATRN': {'label': 'Trans', 'quantity': 4.236046970759828, 'unit': 'g'},\n", + " 'FAMS': {'label': 'Monounsaturated',\n", + " 'quantity': 309.2100138585165,\n", + " 'unit': 'g'},\n", + " 'FAPU': {'label': 'Polyunsaturated',\n", + " 'quantity': 152.98062260255685,\n", + " 'unit': 'g'},\n", + " 'CHOCDF': {'label': 'Carbs', 'quantity': 179.3476753641428, 'unit': 'g'},\n", + " 'CHOCDF.net': {'label': 'Carbohydrates (net)',\n", + " 'quantity': 169.27970869753904,\n", + " 'unit': 'g'},\n", + " 'FIBTG': {'label': 'Fiber', 'quantity': 10.067966666603764, 'unit': 'g'},\n", + " 'SUGAR': {'label': 'Sugars', 'quantity': 21.361842962230522, 'unit': 'g'},\n", + " 'PROCNT': {'label': 'Protein',\n", + " 'quantity': 199.13114512821977,\n", + " 'unit': 'g'},\n", + " 'CHOLE': {'label': 'Cholesterol',\n", + " 'quantity': 1476.393569375,\n", + " 'unit': 'mg'},\n", + " 'NA': {'label': 'Sodium', 'quantity': 15070.54885480061, 'unit': 'mg'},\n", + " 'CA': {'label': 'Calcium', 'quantity': 1168.1948138149583, 'unit': 'mg'},\n", + " 'MG': {'label': 'Magnesium', 'quantity': 271.4767591905548, 'unit': 'mg'},\n", + " 'K': {'label': 'Potassium', 'quantity': 3394.5197834343444, 'unit': 'mg'},\n", + " 'FE': {'label': 'Iron', 'quantity': 18.300855544977704, 'unit': 'mg'},\n", + " 'ZN': {'label': 'Zinc', 'quantity': 16.48215394797533, 'unit': 'mg'},\n", + " 'P': {'label': 'Phosphorus', 'quantity': 2075.915992972608, 'unit': 'mg'},\n", + " 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4.9059567285000005,\n", + " 'unit': 'µg'},\n", + " 'VITD': {'label': 'Vitamin D', 'quantity': 4.1099904625, 'unit': 'µg'},\n", + " 'TOCPHA': {'label': 'Vitamin E',\n", + " 'quantity': 72.97405340895695,\n", + " 'unit': 'mg'},\n", + " 'VITK1': {'label': 'Vitamin K',\n", + " 'quantity': 393.2425920086059,\n", + " 'unit': 'µg'},\n", + " 'WATER': {'label': 'Water', 'quantity': 1446.9892401575353, 'unit': 'g'}},\n", + " 'totalDaily': {'ENERC_KCAL': {'label': 'Energy',\n", + " 'quantity': 347.5096043308315,\n", + " 'unit': '%'},\n", + " 'FAT': {'label': 'Fat', 'quantity': 932.7446782277153, 'unit': '%'},\n", + " 'FASAT': {'label': 'Saturated',\n", + " 'quantity': 559.6465080756727,\n", + " 'unit': '%'},\n", + " 'CHOCDF': {'label': 'Carbs', 'quantity': 59.78255845471426, 'unit': '%'},\n", + " 'FIBTG': {'label': 'Fiber', 'quantity': 40.27186666641506, 'unit': '%'},\n", + " 'PROCNT': {'label': 'Protein',\n", + " 'quantity': 398.26229025643954,\n", + " 'unit': '%'},\n", + " 'CHOLE': {'label': 'Cholesterol',\n", + " 'quantity': 492.1311897916667,\n", + " 'unit': '%'},\n", + " 'NA': {'label': 'Sodium', 'quantity': 627.9395356166922, 'unit': '%'},\n", + " 'CA': {'label': 'Calcium', 'quantity': 116.81948138149583, 'unit': '%'},\n", + " 'MG': {'label': 'Magnesium', 'quantity': 64.63732361679877, 'unit': '%'},\n", + " 'K': {'label': 'Potassium', 'quantity': 72.22382517945414, 'unit': '%'},\n", + " 'FE': {'label': 'Iron', 'quantity': 101.67141969432058, 'unit': '%'},\n", + " 'ZN': {'label': 'Zinc', 'quantity': 149.8377631634121, 'unit': '%'},\n", + " 'P': {'label': 'Phosphorus', 'quantity': 296.5594275675154, 'unit': '%'},\n", + " 'VITA_RAE': {'label': 'Vitamin A',\n", + " 'quantity': 97.2237597479408,\n", + " 'unit': '%'},\n", + " 'VITC': {'label': 'Vitamin C',\n", + " 'quantity': 459.27562729542035,\n", + " 'unit': '%'},\n", + " 'THIA': {'label': 'Thiamin (B1)',\n", + " 'quantity': 187.53425424861987,\n", + " 'unit': '%'},\n", + " 'RIBF': {'label': 'Riboflavin (B2)',\n", + " 'quantity': 270.3573353203313,\n", + " 'unit': '%'},\n", + " 'NIA': {'label': 'Niacin (B3)',\n", + " 'quantity': 355.7360953235933,\n", + " 'unit': '%'},\n", + " 'VITB6A': {'label': 'Vitamin B6',\n", + " 'quantity': 413.8166795010832,\n", + " 'unit': '%'},\n", + " 'FOLDFE': {'label': 'Folate equivalent (total)',\n", + " 'quantity': 194.54405749133667,\n", + " 'unit': '%'},\n", + " 'VITB12': {'label': 'Vitamin B12',\n", + " 'quantity': 204.41486368750003,\n", + " 'unit': '%'},\n", + " 'VITD': {'label': 'Vitamin D',\n", + " 'quantity': 27.399936416666666,\n", + " 'unit': '%'},\n", + " 'TOCPHA': {'label': 'Vitamin E',\n", + " 'quantity': 486.49368939304634,\n", + " 'unit': '%'},\n", + " 'VITK1': {'label': 'Vitamin K',\n", + " 'quantity': 327.7021600071716,\n", + " 'unit': '%'}},\n", + " 'digest': [{'label': 'Fat',\n", + " 'tag': 'FAT',\n", + " 'schemaOrgTag': 'fatContent',\n", + " 'total': 606.284040848015,\n", + " 'hasRDI': True,\n", + " 'daily': 932.7446782277153,\n", + " 'unit': 'g',\n", + " 'sub': [{'label': 'Saturated',\n", + " 'tag': 'FASAT',\n", + " 'schemaOrgTag': 'saturatedFatContent',\n", + " 'total': 111.92930161513456,\n", + " 'hasRDI': True,\n", + " 'daily': 559.6465080756727,\n", + " 'unit': 'g'},\n", + " {'label': 'Trans',\n", + " 'tag': 'FATRN',\n", + " 'schemaOrgTag': 'transFatContent',\n", + " 'total': 4.236046970759828,\n", + " 'hasRDI': False,\n", + " 'daily': 0.0,\n", + " 'unit': 'g'},\n", + " {'label': 'Monounsaturated',\n", + " 'tag': 'FAMS',\n", + " 'schemaOrgTag': None,\n", + " 'total': 309.2100138585165,\n", + " 'hasRDI': False,\n", + " 'daily': 0.0,\n", + " 'unit': 'g'},\n", + " {'label': 'Polyunsaturated',\n", + " 'tag': 'FAPU',\n", + " 'schemaOrgTag': None,\n", + " 'total': 152.98062260255685,\n", + " 'hasRDI': False,\n", + " 'daily': 0.0,\n", + " 'unit': 'g'}]},\n", + " {'label': 'Carbs',\n", + " 'tag': 'CHOCDF',\n", + " 'schemaOrgTag': 'carbohydrateContent',\n", + " 'total': 179.3476753641428,\n", + " 'hasRDI': True,\n", + " 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'total': 3394.5197834343444,\n", + " 'hasRDI': True,\n", + " 'daily': 72.22382517945414,\n", + " 'unit': 'mg'},\n", + " {'label': 'Iron',\n", + " 'tag': 'FE',\n", + " 'schemaOrgTag': None,\n", + " 'total': 18.300855544977704,\n", + " 'hasRDI': True,\n", + " 'daily': 101.67141969432058,\n", + " 'unit': 'mg'},\n", + " {'label': 'Zinc',\n", + " 'tag': 'ZN',\n", + " 'schemaOrgTag': None,\n", + " 'total': 16.48215394797533,\n", + " 'hasRDI': True,\n", + " 'daily': 149.8377631634121,\n", + " 'unit': 'mg'},\n", + " {'label': 'Phosphorus',\n", + " 'tag': 'P',\n", + " 'schemaOrgTag': None,\n", + " 'total': 2075.915992972608,\n", + " 'hasRDI': True,\n", + " 'daily': 296.5594275675154,\n", + " 'unit': 'mg'},\n", + " {'label': 'Vitamin A',\n", + " 'tag': 'VITA_RAE',\n", + " 'schemaOrgTag': None,\n", + " 'total': 875.0138377314672,\n", + " 'hasRDI': True,\n", + " 'daily': 97.2237597479408,\n", + " 'unit': 'µg'},\n", + " {'label': 'Vitamin C',\n", + " 'tag': 'VITC',\n", + " 'schemaOrgTag': None,\n", 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'title': 'Self'}}}]}" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "next_resp_json = next_resp.json()\n", + "next_resp_json" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'recipe': {'uri': 'http://www.edamam.com/ontologies/edamam.owl#recipe_e2f7014774924ac19ed65b31dfe33667',\n", + " 'label': 'Vegan Cauliflower Buffalo Wings',\n", + " 'image': 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'mg'},\n", + " 'FE': {'label': 'Iron', 'quantity': 5.789086053996101, 'unit': 'mg'},\n", + " 'ZN': {'label': 'Zinc', 'quantity': 11.727216603999784, 'unit': 'mg'},\n", + " 'P': {'label': 'Phosphorus', 'quantity': 1242.1637301999644, 'unit': 'mg'},\n", + " 'VITA_RAE': {'label': 'Vitamin A', 'quantity': 82.0381266, 'unit': 'µg'},\n", + " 'VITC': {'label': 'Vitamin C',\n", + " 'quantity': 7.587999999999999,\n", + " 'unit': 'mg'},\n", + " 'THIA': {'label': 'Thiamin (B1)',\n", + " 'quantity': 0.5557857596000001,\n", + " 'unit': 'mg'},\n", + " 'RIBF': {'label': 'Riboflavin (B2)',\n", + " 'quantity': 1.0432902822,\n", + " 'unit': 'mg'},\n", + " 'NIA': {'label': 'Niacin (B3)',\n", + " 'quantity': 52.9949664299995,\n", + " 'unit': 'mg'},\n", + " 'VITB6A': {'label': 'Vitamin B6',\n", + " 'quantity': 5.177141802199811,\n", + " 'unit': 'mg'},\n", + " 'FOLDFE': {'label': 'Folate equivalent (total)',\n", + " 'quantity': 71.6813067999954,\n", + " 'unit': 'µg'},\n", + " 'FOLFD': {'label': 'Folate (food)',\n", + " 'quantity': 71.6813067999954,\n", + " 'unit': 'µg'},\n", + " 'FOLAC': {'label': 'Folic acid', 'quantity': 0.0, 'unit': 'µg'},\n", + " 'VITB12': {'label': 'Vitamin B12',\n", + " 'quantity': 2.2679618500000003,\n", + " 'unit': 'µg'},\n", + " 'VITD': {'label': 'Vitamin D',\n", + " 'quantity': 0.9071847400000002,\n", + " 'unit': 'µg'},\n", + " 'TOCPHA': {'label': 'Vitamin E',\n", + " 'quantity': 41.21123958398056,\n", + " 'unit': 'mg'},\n", + " 'VITK1': {'label': 'Vitamin K',\n", + " 'quantity': 2.7879999999999994,\n", + " 'unit': 'µg'},\n", + " 'WATER': {'label': 'Water', 'quantity': 733.4006025799827, 'unit': 'g'}},\n", + " 'totalDaily': {'ENERC_KCAL': {'label': 'Energy',\n", + " 'quantity': 169.4589671818475,\n", + " 'unit': '%'},\n", + " 'FAT': {'label': 'Fat', 'quantity': 428.9351580306319, 'unit': '%'},\n", + " 'FASAT': {'label': 'Saturated',\n", + " 'quantity': 213.78369734797099,\n", + " 'unit': '%'},\n", + " 'CHOCDF': {'label': 'Carbs', 'quantity': 16.102405833141862, 'unit': '%'},\n", + " 'FIBTG': {'label': 'Fiber', 'quantity': 5.467399999995131, 'unit': '%'},\n", + " 'PROCNT': {'label': 'Protein',\n", + " 'quantity': 329.01990899999817,\n", + " 'unit': '%'},\n", + " 'CHOLE': {'label': 'Cholesterol',\n", + " 'quantity': 335.65835380000004,\n", + " 'unit': '%'},\n", + " 'NA': {'label': 'Sodium', 'quantity': 142.28215339999412, 'unit': '%'},\n", + " 'CA': {'label': 'Calcium', 'quantity': 19.554144639961578, 'unit': '%'},\n", + " 'MG': {'label': 'Magnesium', 'quantity': 46.44355557141776, 'unit': '%'},\n", + " 'K': {'label': 'Potassium', 'quantity': 44.34625188934862, 'unit': '%'},\n", + " 'FE': {'label': 'Iron', 'quantity': 32.161589188867225, 'unit': '%'},\n", + " 'ZN': {'label': 'Zinc', 'quantity': 106.61106003636168, 'unit': '%'},\n", + " 'P': {'label': 'Phosphorus', 'quantity': 177.45196145713777, 'unit': '%'},\n", + " 'VITA_RAE': {'label': 'Vitamin A', 'quantity': 9.1153474, 'unit': '%'},\n", + " 'VITC': {'label': 'Vitamin C', 'quantity': 8.431111111111111, 'unit': '%'},\n", + " 'THIA': {'label': 'Thiamin (B1)',\n", + " 'quantity': 46.31547996666668,\n", + " 'unit': '%'},\n", + " 'RIBF': {'label': 'Riboflavin (B2)',\n", + " 'quantity': 80.25309863076923,\n", + " 'unit': '%'},\n", + " 'NIA': {'label': 'Niacin (B3)',\n", + " 'quantity': 331.2185401874969,\n", + " 'unit': '%'},\n", + " 'VITB6A': {'label': 'Vitamin B6',\n", + " 'quantity': 398.2416770922932,\n", + " 'unit': '%'},\n", + " 'FOLDFE': {'label': 'Folate equivalent (total)',\n", + " 'quantity': 17.92032669999885,\n", + " 'unit': '%'},\n", + " 'VITB12': {'label': 'Vitamin B12',\n", + " 'quantity': 94.49841041666669,\n", + " 'unit': '%'},\n", + " 'VITD': {'label': 'Vitamin D', 'quantity': 6.047898266666667, 'unit': '%'},\n", + " 'TOCPHA': {'label': 'Vitamin E',\n", + " 'quantity': 274.74159722653707,\n", + " 'unit': '%'},\n", + " 'VITK1': {'label': 'Vitamin K',\n", + " 'quantity': 2.323333333333333,\n", + " 'unit': '%'}},\n", + " 'digest': [{'label': 'Fat',\n", + " 'tag': 'FAT',\n", + " 'schemaOrgTag': 'fatContent',\n", + " 'total': 278.8078527199107,\n", + " 'hasRDI': True,\n", + " 'daily': 428.9351580306319,\n", + " 'unit': 'g',\n", + " 'sub': [{'label': 'Saturated',\n", + " 'tag': 'FASAT',\n", + " 'schemaOrgTag': 'saturatedFatContent',\n", + " 'total': 42.756739469594194,\n", + " 'hasRDI': True,\n", + " 'daily': 213.78369734797099,\n", + " 'unit': 'g'},\n", + " {'label': 'Trans',\n", + " 'tag': 'FATRN',\n", + " 'schemaOrgTag': 'transFatContent',\n", + " 'total': 1.7976740285193156,\n", + " 'hasRDI': False,\n", + " 'daily': 0.0,\n", + " 'unit': 'g'},\n", + " {'label': 'Monounsaturated',\n", + " 'tag': 'FAMS',\n", + " 'schemaOrgTag': None,\n", + " 'total': 166.03377032793574,\n", + " 'hasRDI': False,\n", + " 'daily': 0.0,\n", + " 'unit': 'g'},\n", + " {'label': 'Polyunsaturated',\n", + " 'tag': 'FAPU',\n", + " 'schemaOrgTag': None,\n", + " 'total': 50.62621495598472,\n", + " 'hasRDI': False,\n", + " 'daily': 0.0,\n", + " 'unit': 'g'}]},\n", + " {'label': 'Carbs',\n", + " 'tag': 'CHOCDF',\n", + " 'schemaOrgTag': 'carbohydrateContent',\n", + " 'total': 48.30721749942559,\n", + " 'hasRDI': True,\n", + " 'daily': 16.102405833141862,\n", + " 'unit': 'g',\n", + " 'sub': [{'label': 'Carbs (net)',\n", + " 'tag': 'CHOCDF.net',\n", + " 'schemaOrgTag': None,\n", + " 'total': 46.940367499426806,\n", + " 'hasRDI': False,\n", + " 'daily': 0.0,\n", + " 'unit': 'g'},\n", + " {'label': 'Fiber',\n", + " 'tag': 'FIBTG',\n", + " 'schemaOrgTag': 'fiberContent',\n", + " 'total': 1.3668499999987826,\n", + " 'hasRDI': True,\n", + " 'daily': 5.467399999995131,\n", + " 'unit': 'g'},\n", + " {'label': 'Sugars',\n", + " 'tag': 'SUGAR',\n", + " 'schemaOrgTag': 'sugarContent',\n", + " 'total': 29.265034999554132,\n", + " 'hasRDI': False,\n", + " 'daily': 0.0,\n", + " 'unit': 'g'},\n", + " {'label': 'Sugars, added',\n", + " 'tag': 'SUGAR.added',\n", + " 'schemaOrgTag': None,\n", + " 'total': 26.371874999554134,\n", + " 'hasRDI': False,\n", + " 'daily': 0.0,\n", + " 'unit': 'g'}]},\n", + " {'label': 'Protein',\n", + " 'tag': 'PROCNT',\n", + " 'schemaOrgTag': 'proteinContent',\n", + " 'total': 164.50995449999908,\n", + " 'hasRDI': True,\n", + " 'daily': 329.01990899999817,\n", + " 'unit': 'g'},\n", + " {'label': 'Cholesterol',\n", + " 'tag': 'CHOLE',\n", + " 'schemaOrgTag': 'cholesterolContent',\n", + " 'total': 1006.9750614000001,\n", + " 'hasRDI': True,\n", + " 'daily': 335.65835380000004,\n", + " 'unit': 'mg'},\n", + " {'label': 'Sodium',\n", + " 'tag': 'NA',\n", + " 'schemaOrgTag': 'sodiumContent',\n", + " 'total': 3414.7716815998588,\n", + " 'hasRDI': True,\n", + " 'daily': 142.28215339999412,\n", + " 'unit': 'mg'},\n", + " {'label': 'Calcium',\n", + " 'tag': 'CA',\n", + " 'schemaOrgTag': None,\n", + " 'total': 195.54144639961578,\n", + " 'hasRDI': True,\n", + " 'daily': 19.554144639961578,\n", + " 'unit': 'mg'},\n", + " {'label': 'Magnesium',\n", + " 'tag': 'MG',\n", + " 'schemaOrgTag': None,\n", + " 'total': 195.0629333999546,\n", + " 'hasRDI': True,\n", + " 'daily': 46.44355557141776,\n", + " 'unit': 'mg'},\n", + " {'label': 'Potassium',\n", + " 'tag': 'K',\n", + " 'schemaOrgTag': None,\n", + " 'total': 2084.273838799385,\n", + " 'hasRDI': True,\n", + " 'daily': 44.34625188934862,\n", + " 'unit': 'mg'},\n", + " {'label': 'Iron',\n", + " 'tag': 'FE',\n", + " 'schemaOrgTag': None,\n", + " 'total': 5.789086053996101,\n", + " 'hasRDI': True,\n", + " 'daily': 32.161589188867225,\n", + " 'unit': 'mg'},\n", + " {'label': 'Zinc',\n", + " 'tag': 'ZN',\n", + " 'schemaOrgTag': None,\n", + " 'total': 11.727216603999784,\n", + " 'hasRDI': True,\n", + " 'daily': 106.61106003636168,\n", + " 'unit': 'mg'},\n", + " {'label': 'Phosphorus',\n", + " 'tag': 'P',\n", + " 'schemaOrgTag': None,\n", + " 'total': 1242.1637301999644,\n", + " 'hasRDI': True,\n", + " 'daily': 177.45196145713777,\n", + " 'unit': 'mg'},\n", + " {'label': 'Vitamin A',\n", + " 'tag': 'VITA_RAE',\n", + " 'schemaOrgTag': None,\n", + " 'total': 82.0381266,\n", + " 'hasRDI': True,\n", + " 'daily': 9.1153474,\n", + " 'unit': 'µg'},\n", + " {'label': 'Vitamin C',\n", + " 'tag': 'VITC',\n", + " 'schemaOrgTag': None,\n", + " 'total': 7.587999999999999,\n", + " 'hasRDI': True,\n", + " 'daily': 8.431111111111111,\n", + " 'unit': 'mg'},\n", + " {'label': 'Thiamin (B1)',\n", + " 'tag': 'THIA',\n", + " 'schemaOrgTag': None,\n", + " 'total': 0.5557857596000001,\n", + " 'hasRDI': True,\n", + " 'daily': 46.31547996666668,\n", + " 'unit': 'mg'},\n", + " {'label': 'Riboflavin (B2)',\n", + " 'tag': 'RIBF',\n", + " 'schemaOrgTag': None,\n", + " 'total': 1.0432902822,\n", + " 'hasRDI': True,\n", + " 'daily': 80.25309863076923,\n", + " 'unit': 'mg'},\n", + " {'label': 'Niacin (B3)',\n", + " 'tag': 'NIA',\n", + " 'schemaOrgTag': None,\n", + " 'total': 52.9949664299995,\n", + " 'hasRDI': True,\n", + " 'daily': 331.2185401874969,\n", + " 'unit': 'mg'},\n", + " {'label': 'Vitamin B6',\n", + " 'tag': 'VITB6A',\n", + " 'schemaOrgTag': None,\n", + " 'total': 5.177141802199811,\n", + " 'hasRDI': True,\n", + " 'daily': 398.2416770922932,\n", + " 'unit': 'mg'},\n", + " {'label': 'Folate equivalent (total)',\n", + " 'tag': 'FOLDFE',\n", + " 'schemaOrgTag': None,\n", + " 'total': 71.6813067999954,\n", + " 'hasRDI': True,\n", + " 'daily': 17.92032669999885,\n", + " 'unit': 'µg'},\n", + " {'label': 'Folate (food)',\n", + " 'tag': 'FOLFD',\n", + " 'schemaOrgTag': None,\n", + " 'total': 71.6813067999954,\n", + " 'hasRDI': False,\n", + " 'daily': 0.0,\n", + " 'unit': 'µg'},\n", + " {'label': 'Folic acid',\n", + " 'tag': 'FOLAC',\n", + " 'schemaOrgTag': None,\n", + " 'total': 0.0,\n", + " 'hasRDI': False,\n", + " 'daily': 0.0,\n", + " 'unit': 'µg'},\n", + " {'label': 'Vitamin B12',\n", + " 'tag': 'VITB12',\n", + " 'schemaOrgTag': None,\n", + " 'total': 2.2679618500000003,\n", + " 'hasRDI': True,\n", + " 'daily': 94.49841041666669,\n", + " 'unit': 'µg'},\n", + " {'label': 'Vitamin D',\n", + " 'tag': 'VITD',\n", + " 'schemaOrgTag': None,\n", + " 'total': 0.9071847400000002,\n", + " 'hasRDI': True,\n", + " 'daily': 6.047898266666667,\n", + " 'unit': 'µg'},\n", + " {'label': 'Vitamin E',\n", + " 'tag': 'TOCPHA',\n", + " 'schemaOrgTag': None,\n", + " 'total': 41.21123958398056,\n", + " 'hasRDI': True,\n", + " 'daily': 274.74159722653707,\n", + " 'unit': 'mg'},\n", + " {'label': 'Vitamin K',\n", + " 'tag': 'VITK1',\n", + " 'schemaOrgTag': None,\n", + " 'total': 2.7879999999999994,\n", + " 'hasRDI': True,\n", + " 'daily': 2.323333333333333,\n", + " 'unit': 'µg'},\n", + " {'label': 'Sugar alcohols',\n", + " 'tag': 'Sugar.alcohol',\n", + " 'schemaOrgTag': None,\n", + " 'total': 0.0,\n", + " 'hasRDI': False,\n", + " 'daily': 0.0,\n", + " 'unit': 'g'},\n", + " {'label': 'Water',\n", + " 'tag': 'WATER',\n", + " 'schemaOrgTag': None,\n", + " 'total': 733.4006025799827,\n", + " 'hasRDI': False,\n", + " 'daily': 0.0,\n", + " 'unit': 'g'}]},\n", + " '_links': {'self': {'href': 'https://api.edamam.com/api/recipes/v2/504036b2fe3acea29c4440e204e10b59?type=public&app_id=abaa034e&app_key=251fdd6b808c90fa3b5863d9943692e5',\n", + " 'title': 'Self'}}}]" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# actual results\n", + "results = resp_json['hits']\n", + "results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/awchen/Repos/Projects/MeaLeon/.venv/lib/python3.10/site-packages/nbdev/export.py:73: UserWarning: Notebook '/home/awchen/Repos/Projects/MeaLeon/nbs/16_notebook_refactor.ipynb' uses `#|export` without `#|default_exp` cell.\n", + "Note nbdev2 no longer supports nbdev1 syntax. Run `nbdev_migrate` to upgrade.\n", + "See https://nbdev.fast.ai/getting_started.html for more information.\n", + " warn(f\"Notebook '{nbname}' uses `#|export` without `#|default_exp` cell.\\n\"\n" + ] + } + ], + "source": [ + "# | hide\n", + "nbdev.nbdev_export()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/nbs/19_exploring_personahub.ipynb b/nbs/19_exploring_personahub.ipynb new file mode 100644 index 0000000..0439e80 --- /dev/null +++ b/nbs/19_exploring_personahub.ipynb @@ -0,0 +1,689 @@ +{ + "cells": [ + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "---\n", + "description: test\n", + "output-file: template.html\n", + "title: Template\n", + "\n", + "---\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# | default_exp core" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# | hide\n", + "import dagshub\n", + "import mlflow\n", + "import nbdev\n", + "from nbdev.showdoc import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# | export\n", + "def foo():\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# | hide\n", + "# this function allows us to get the experiment ID from an experiment name\n", + "def get_experiment_id(name):\n", + " exp = mlflow.get_experiment_by_name(name)\n", + " if exp is None:\n", + " exp_id = mlflow.create_experiment(name)\n", + " return exp_id\n", + " return exp.experiment_id" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# | hide\n", + "nbdev.nbdev_export()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Stages of Pipeline Deployment\n", + "\n", + "For LLMs, this is a data augmentation pipeline. Raw data will be augmented to compute one or more new columns. This needs to go through the familiar stages of Development, Staging, and Produciton.\n", + "\n", + "### Development\n", + "\n", + "LLMOps goals for Development/Evaluation are\n", + "\n", + "1. track what is being done carefully for later auditing and reproducibility\n", + "2. package models or pipelines in a format which will make future deployment easier. \n", + "\n", + "We will:\n", + "* Load data\n", + "* Build an LLM pipeline\n", + "* Test applying the pipeline to data and log queries and results to MLflow Tracking\n", + "* Log the pipeline to the MLflow tracking server as an MLflow model\n", + "\n", + "The EDA/desired transformations are not really done in this step. The example video mentions that the processing is done during the **course** and not in the LLMOps video. The video starts the workflow focusing on tracking.\n", + "\n", + "### Staging\n", + "\n", + "LLMOps goals for staging/testing/QA are\n", + "1. track the LLM's progress through testing and towards production\n", + "2. work programmatically to demonstrated the APIs needed for future CI/CD automation\n", + "\n", + "We will:\n", + "* Register the pipeline to the MLflow Model Registry\n", + "* Test the pipeline on sample data\n", + "* Promote the registered model (pipeline) to production\n", + "\n", + "### Production\n", + "\n", + "LLMOps goals for production are \n", + "1. write scale-out code that can meet scaling demands in the future\n", + "2. simplify deployment by using MLflow to write model-agnostic deployment code\n", + "\n", + "We will:\n", + "1. Load the latest production LLM pipeline from the Model Registry\n", + "2. Apply the pipeline to an Apache Spark Dataframe\n", + "3. Append the results to a Delta Lake Table\n", + "\n", + "\n", + "## Notes about this workflow\n", + "\n", + "### Notebook vs modular scripts\n", + "For a demo, everything in the workflow is divided into notebook sections, but this should really be split into separate notebooks or scripts\n", + "\n", + "### Models vs code\n", + "Since the path here is tracked via MLflow Model Registry, this workflow promotes models over code. See \"The Big Book of MLOps\" for more discussion over the distinction (one difference is Model Registry vs Git)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# | Below this are blocks to use DagsHub with MLflow" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#@markdown Enter the username of your DAGsHub account:\n", + "DAGSHUB_USER_NAME = \"AaronWChen\" #@param {type:\"string\"}\n", + "\n", + "#@markdown Enter the email for your DAGsHub account:\n", + "DAGSHUB_EMAIL = \"awc33@cornell.edu\" #@param {type:\"string\"}\n", + "\n", + "#@markdown Enter the repo name \n", + "DAGSHUB_REPO_NAME= \"\" #@param {type:\"string\"}\n", + "\n", + "#@markdown Enter the name of the branch you are working on \n", + "BRANCH= \"\" #@param {type:\"string\"}\n", + "dagshub.init(repo_name=DAGSHUB_REPO_NAME\n", + " , repo_owner=DAGSHUB_USER_NAME)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mlflow.set_tracking_uri(f'https://dagshub.com/{DAGSHUB_USER_NAME}/{DAGSHUB_REPO_NAME}.mlflow')\n", + "\n", + "# starter idea for making an experiment name can be the git branch, but need more specificity\n", + "DAGSHUB_TEST_NAME = \"stanza_quadgrams_small_set_v1\" #@param {type:\"string\"}\n", + "experiment_name = f\"{DAGSHUB_EMAIL}/{DAGSHUB_TEST_NAME}\"\n", + "mlflow_exp_id = get_experiment_id(experiment_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## DEVELOPMENT" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# import necessary libraries to handle raw data\n", + "import dill as pickle\n", + "import dvc.api\n", + "import pandas as pd\n", + "from sklearn.feature_extraction.text import (\n", + " CountVectorizer\n", + " , TfidfTransformer\n", + " , TfidfVectorizer\n", + " ,\n", + ")\n", + "from src.custom_stanza_mlflow import StanzaWrapper\n", + "import src.dataframe_preprocessor as dfpp\n", + "import tqdm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prepare data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# load raw data and preprocess/clean\n", + "data = dvc.api.read(\n", + " path='../data/raw/recipes-en-201706/epicurious-recipes_m2.json',\n", + " mode='r')\n", + "raw_df = pd.read_json(data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create subset for dev\n", + "dev_df = raw_df[0:50]\n", + "\n", + "# pre_proc_df is cleaned dataframe\n", + "pre_proc_df = dfpp.preprocess_dataframe(dev_df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Convert data to Delta format?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pyspark.pandas as ps\n", + "\n", + "# save and log preprocessed dataframe(s)\n", + "prod_data_path = \"../../data/processed/prod_data\"\n", + "test_spark_dataset = ps.from_pandas(pre_proc_df)\n", + "test_spark_dataset.to_delta(path=prod_data_path,\n", + " mode='overwrite',\n", + " index='id')\n", + "mlflow.log_artifacts(\"../../data/processed/prod_data\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Develop the pipeline\n", + "\n", + "MLflow Tracking is organized as follows:\n", + "* An **experiment** generlaly corresponds to the creation of 1 primary model or pipeline. It contains some number of /*runs*/\n", + " * A **run** generally corresponds to the creation of 1 sub-model, such as 1 trial during hyperparameter tuing in traditional ML. In this example, running the notebook once only creates 1 run, but a second execution of the notebook would create a second run. This version tracking can be useful during iterative development. Each run contains some number of logged parameters, metrics, tags, models, artifacts, and other metadata\n", + " * A **parameter** is an input to the model or pipeline, such as a regularization parameter in traditional ML \n", + " * A **metric** is an output of evaluation, such as accuracy or loss\n", + " * An **artifact** is an arbitrary file stored alongside a run's metadata, such as the serialized model itself\n", + " * A **flavor** is an MLflow format for serializing models. This format uses the underlying ML library's (ie, PyTorch, TensorFlow, Hugging Face) format plus metadata\n", + " * As of MLflow 2.3.1, there is an experimental API for logging Predictions\n", + " * Wrap model development with a call to ```with mlflow.start_run():``` This context manager syntax starts and ends the MLflow run explicitly, which is a best practice for code which may be moved to production. See API doc" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# create pipelines relevant to library used\n", + "# MLflow example uses HuggingFace\n", + "# below is example for MeaLeon with Stanza and sklearn NLP pipeline\n", + "\n", + "# mlflow.set_experiment(\"\") this can be in the start_run though\n", + "\n", + "# cv_params are parameters for the sklearn CountVectorizer or TFIDFVectorizer\n", + "cv_params = {\n", + " 'strip_accents':\"unicode\",\n", + " 'lowercase':True,\n", + " 'analyzer': StanzaWrapper().stanza_analyzer(stanza_pipeline=nlp, minNgramLength=1, maxNgramLength=4),\n", + " 'min_df':10,\n", + "}\n", + "\n", + "# pipeline_params are parameters that will be logged in MLFlow and are a superset of library parameters\n", + "pipeline_params = {\n", + " 'stanza_model': 'en',\n", + " 'language': 'english',\n", + " 'sklearn-transformer': 'TfidfVectorizer'\n", + "}\n", + "\n", + "# update the pipeline parameters with the library-specific ones so that they show up in MLflow Tracking\n", + "pipeline_params.update(cv_params)\n", + "\n", + "with mlflow.start_run(experiment_id=mlflow_exp_id):\n", + " # LOG PARAMETERS\n", + " mlflow.log_params(pipeline_params)\n", + "\n", + " # LOG INPUTS (QUERIES) AND OUTPUTS\n", + " # MLflow example uses a list of strings or a list of str->str dicts\n", + " # useful for complex learning, say with few-shot pipeline\n", + "\n", + " # mlflow.llm.log_predictions(inputs=, outputs=, prompts=[])\n", + " \n", + " # LOG MODEL\n", + " # useful to log a \"signature\" with the model telling MLflow the input and output schema for the model\n", + " # signature = mlflow.models.infer_signature(\n", + " # pre_proc_df[\"ingredients\"][0],\n", + " # mlflow.transformers.generate_signature_output(\n", + " # summarizer, pre_proc_df[\"ingredients\"][0]\n", + " # )\n", + " # )\n", + " # print(f\"Signature:\\n{signature}\\n\")\n", + "\n", + " # for mlflow.transformers, if there are inference-time configurations,\n", + " # those need to be saved specially in the log_model call\n", + " # this ensures that the pipeline will use these same configurations when re-loaded\n", + " # inference_config = {\n", + " # \"min_length\": min_length,\n", + " # \"max_length\": max_length,\n", + " # \"truncation\": truncation,\n", + " # \"do_sample\": do_sample\n", + " # }\n", + "\n", + " # logging a model returns a handle \"model_info\" to the model metadata in the tracking server. This 'model_info' will be useful later in the notebook to retrieve the logged model\n", + " model_info = mlflow.transformers.log_model(\n", + " transformers_model=summarizer,\n", + " artifact_path='summarizer',\n", + " task='summarization',\n", + " inference_config=inference_config,\n", + " signature=signature\n", + " input_example=\"This is an example of a long this pipeline can summarize\"\n", + " )\n", + "\n", + " # since this uses a custom Stanza analyzer, we have to use a custom mlflow.Pyfunc.PythonModel\n", + " # Instantiate sklearn TFIDFVectorizer\n", + " tfidf_vectorizer_model = TfidfVectorizer(**cv_params)\n", + "\n", + " # Do fit transform on data\n", + " test_tfidf_transform = tfidf_vectorizer_model.fit_transform(tqdm(pre_proc_df[\"ingredients\"]))\n", + "\n", + " word_matrix = ps.DataFrame(\n", + " test_tfidf_transform.toarray()\n", + " , columns=tfidf_vectorizer_model.get_feature_names_out()\n", + " , index=pre_proc_df.index\n", + " )\n", + "\n", + " with open(\"../joblib/tfidf_transformer_small_test.pkl\", \"wb\") as fo:\n", + " pickle.dump(tfidf_vectorizer_model, fo)\n", + " mlflow.log_artifact(\"../joblib/tfidf_transformer_small_test.pkl\", artifact_path=\"sklearn_dill_pkls\")\n", + "\n", + " with open(\"../joblib/database_word_matrix_small_test.pkl\", \"wb\") as fo:\n", + " pickle.dump(word_matrix, fo)\n", + " mlflow.log_artifact(\"../joblib/database_word_matrix_small_test.pkl\", artifact_path=\"sklearn_dill_pkls\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loading the pipeline back" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "loaded_summarizer = mlflow.pyfunc.load_model(model_uri=model_info.model_uri)\n", + "loaded_summarizer.predict(xsum_sample['document'][0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`predict()` method can handle more than 1 document at a time" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "results = loaded_summarizer.predict(xsum_sample.to_pandas()['document'])\n", + "display(pd.DataFrame(results, columns=['generated_summary']))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Moving to Staging\n", + "\n", + "Begin by registering the model in the MLflow Model Registry" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the name for the model in the Model Registry\n", + "# We filter out some special characters that cannot be used in model names\n", + "\n", + "model_name = f'summarizer - {DA.username}'\n", + "model_name = model_name.replace(\"/\", \"_\").replace(\":\",\"-\")\n", + "print(model_name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Register a new model under the given name, or a new model version if the name exists already\n", + "mlflow.register_model(model_uri=model_info.model_uri, name=model_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test the Pipeline\n", + "During the Staging step of development, our goal is to move code and/or models from Development to Production. In order to do so, we must test the code and/or models to make sure they are ready for Production\n", + "\n", + "We track our progress here using the MLflow Model Registry. This metadata and model store organizes models as follows:\n", + "* **A registered model** is a named model in the registry (here, the summarization model). It may have multiple /*versions*/.\n", + " * **A model version** is an instance of a given model. As you update your model, you will create new versions. Each version is designated as being in a particular /*stage*/ of deployment.\n", + " * **A stage** is a stage of deployment: `None` (development), `Staging`, `Production`, or `Archived`\n", + "\n", + "The model we registered above starts with 1 version in stage `None` (Development).\n", + "\n", + "In the workflow below, we programmatically transition the model from development to staging to production. For more information on the Model Registry API, see the [Model Registry docs](link). Alternatively, you can edit the registry and make model stage transitions via the UI. To access the UI, click the Experiments menu option in the left-hand sidebar, and search for your model name." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from mlflow import MlflowClient\n", + "\n", + "client = MlflowClient()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "client.search_registered_models(filter_string=f\"name = {model_name}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example, we will run manual tests, but it would be reasonable to run both automated evaluation and human evaluation in practice. Once tests pass, we will promote the model to stage `Production` to mark it ready for user-facing applications\n", + "\n", + "/*Model URIs*/: Below, we use model URIs to tell MLflow which model and version we are rferring to. Two common URI patterns for the MLflow Model Registry are:\n", + "* `f\"models:/{model_name}/{model_version}\"` to refer to a specific model version by number\n", + "* `f\"models:/{model_name}/{model_stage}\"` to refer to the latest model in a given stage" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model_version = 1\n", + "dev_model = mlflow.pyfunc.load_model(model_uri=f\"models:/{model_name}/{model_version}\")\n", + "dev_model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "/*Note about model dependencies*/ When you load the model via MLflow above, you may see warnings about the Python environment. Make sure the environments for development, staging, and production match!\n", + "\n", + "MLflow saves these libraries and versions alongside the logged model. See the [MLflow docs on model storage](link) for more information." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Transition to Staging\n", + "We will move the model to `Staging` to indicate that we are actively testing it" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "client.transition_model_version_stage(model_name, model_version, \"staging\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "staging_model = dev_model\n", + "\n", + "# an actual CD/CD workflow might load the `staging_model` programmatically, like:\n", + "# mlflow.pyfunc.load_model(model_uri=f\"models:/{model_name}/{Staging}\")\n", + "# or\n", + "# mlflow.pyfunc.load_model(model_uri=f\"models:/{model_name}/{model_version}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now \"test\" the model manually on sample data. Here, we simply print out the results and compare them with the original data. In a more realistic setting, we might use a set of human evaluators (or a combination of automated metrics and human evaluators) to decide whether the model outperformed the previous model or system" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results = staging_model.predict(xsum_sample.to_pandas()['document'])\n", + "display(pd.DataFrame(results, columns=['generated_summary']))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If the results look good, they can be transitioned to production" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Transition to Production" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "client.transition_model_version_stage(model_name, model_version, \"production\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create a production workflow for batch inference\n", + "Once the pipeline is in Production, it may be used by one or more production jobs or serving endpoints. Common deployment locations are:\n", + "* batch or streaming inference jobs\n", + "* model serving endpoints\n", + "* edge devices\n", + "\n", + "Example shows batch inference using Apache Spark DataFrames with Delta Lake format. Spark allows simple scale-out inference for high-throughput, low-cost jobs, and Delta allows us to append to and modify inference refult tables with ACID transactions. See the [Apache Spark page](link) and the [Delta Lake page](link) for more information on these technologies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load our data as a Spark DataFrame\n", + "# Recall that we saved this as Delta at the start of the notebook\n", + "# Also note that it has a ground-truth summary column\n", + "\n", + "prod_data = spark.read.format(\"delta\").load(prod_data_path).limit(10)\n", + "display(prod_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below, we load the model using `mlflow.pyfunc.spark_udf`. This returns the model as a Spark User Defined Function which can be applied efficiently to big data. /*Note that the deployment code is library-agnostic: it never references that the model is a Hugging Face pipeline*/. This simplified deployment is possible because MLflow logs environment metadata and \"knows\" how to load the model and run it" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# MLflow lets you grab the latest model version in a given stage. Here, we grab the latest Production version\n", + "prod_model_udf = mlflow.pyfunc.spark_udf(\n", + " spark,\n", + " model_uri=f\"models:/{model_name}/Production\",\n", + " env_manager=\"virtualenv\",\n", + " result_type=\"string\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run inference by appending a new column to the DataFrame\n", + "batch_inference_results = prod_data.withColumn(\"generated_summary\", prod_model_udf(\"document\"))\n", + "display(batch_inference_results)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now write out our inference results to another Delta table. Here, we append the results to an existing table (and create the table if it does not exist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inference_results_path = f\"{DA.paths.working_dir}/m6-inference-results\".replace(\"/dbfs\", \"dbfs:\")\n", + "batch_inference_results.write.format(\"delta\").mode(\"append\").save(inference_results_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Delta is chosen because it works well with Spark and other scale-out technologies, and works well in supporting both batch and streaming pipelines, allowing concurrent reads and writes to and from a table with ACID transactions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And that's it!\n", + "\n", + "To create a productionjob, we could for example take the new lines of code above, put them in a new notebook, and schedule it as an automated workflow. MLflow can be integrated with essentially any deployment system, but for more intformation specific to this Databricks workspace, see the \"Use model for inference\" documentation for AWS, Azure, or GCP.\n", + "\n", + "We did not cover model serving for real-time inference, but MLflow models can be deployed to any cloud or on-prem serving systems. For more information, see the [open-source MLflow Model Registry docs](link) or the [Databricks Model Serving docs](link)\n", + "\n", + "For other topics not covered, see [\"The Big Book of MLOps\"](link)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "We have now walked through a full example of going from development to production. Our LLM pipeline was very simple, but LLM Ops for a more complex workflow (such as fune-tuning a custom model) would be very similar. You still follow the basic Ops steps of:\n", + "* Development: Creating the pipeline or model, tracking the process in the MLflow Tracking server, and seving the final pipeline or model\n", + "* Staging: Registering a new model or version in the MLflow Model Registry, testing it, and promoting it through Staging to Production. \n", + "* Production: Creating an inference job, or creating a model serving endpoint" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/poetry.lock b/poetry.lock index 91facbf..f9284c4 100644 --- a/poetry.lock +++ b/poetry.lock @@ -7050,61 +7050,105 @@ files = [ [[package]] name = "sqlalchemy" -version = "1.4.50" +version = "2.0.34" description = "Database Abstraction Library" optional = false -python-versions = 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"sha256:7d37c882a30c43464d143e35e9ecaf945d88035e20117bf5ec2834a23cbe505e"}, +] + +[package.dependencies] +pydantic = ">=1.10.13,<3.0.0" +SQLAlchemy = ">=2.0.14,<2.1.0" [[package]] name = "sqlparse" @@ -8962,4 +9006,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p [metadata] lock-version = "2.0" python-versions = "~3.10" -content-hash = "51a5014423b07f4bdc33dc49edb621a1de0754b68da54076fe1ed38985562968" +content-hash = "079a5eac87fa50791439784722036d1e9631fdb938e0f22cd4a059514042ad28" diff --git a/pyproject.toml b/pyproject.toml index 5f8af6e..6ff476d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -55,6 +55,7 @@ more-itertools = "^10.2.0" flagembedding = "^1.2.5" pyvespa = "^0.39.0" uvicorn = "^0.30.4" +sqlmodel = "^0.0.22" [tool.poetry.dev-dependencies] black = "^21.12b0"