0.1.1 Align on Product Framework

[ArpitaDhir]

Start
It is important to understand what we are solving and for who. This library needs to be robust and should adapt based on the use case we plan to solve. I want you to build this keep users and scale in mind.

LDP Product Mission
Enable small businesses to easily store, search, migrate, gain insights and use the insights directly into their products. Basically serve all business requirements, locally i.e you don't need a cloud account or authentication.

[Discussion Update] We are testing LDP integration with @ArpitaDhir's problem statement of Taste Archives (private repo) and it is for young adults (23-35 yo) learning to cook or exploring new recipes to improve their hosting game.

[Current Status] Awaiting Taste Archives POC Demo

[ArpitaDhir]

#28 @tusharchou

[ArpitaDhir]

@tusharchou @mrutunjay-kinagi can you demo using this dataset?

[mrutunjay-kinagi]

@ArpitaDhir @tusharchou Take a look at this, I've done some data clean-up/transformation for ease.
https://colab.research.google.com/drive/1Bu2Jw9lPqGLiu2X1P7_uhgwIwNih5QdS?usp=sharing

[tusharchou]

@mrutunjay-kinagi thank you for creating a place for us to explore the data together.

[ArpitaDhir]

@ArpitaDhir @tusharchou Take a look at this, I've done some data clean-up/transformation for ease. https://colab.research.google.com/drive/1Bu2Jw9lPqGLiu2X1P7_uhgwIwNih5QdS?usp=sharing

@mrutunjay-kinagi Thank you for sharing this document. Super helpful - I think these are great data points to start with.

I wonder if we can explore the below too

1. Which course/type of meal i.e breakfast, lunch, snack, beverage or dinner?
2. Level of difficulty
3. Do you also have prep time and cook time i.e prep is marination etc and cook is the process
4. Cuisine
5. Is it possible to categorise based on keywords like bake, saute, deep fry, grind, chop etc?

[mrutunjay-kinagi]

@ArpitaDhir @tusharchou Take a look at this, I've done some data clean-up/transformation for ease. https://colab.research.google.com/drive/1Bu2Jw9lPqGLiu2X1P7_uhgwIwNih5QdS?usp=sharing

@mrutunjay-kinagi Thank you for sharing this document. Super helpful - I think these are great data points to start with.

I wonder if we can explore the below too

1. Which course/type of meal i.e breakfast, lunch, snack, beverage or dinner?
2. Level of difficulty
3. Do you also have prep time and cook time i.e prep is marination etc and cook is the process
4. Cuisine
5. Is it possible to categorise based on keywords like bake, saute, deep fry, grind, chop etc?

@ArpitaDhir Sure, I'll work on this and get back to you. I need to review the data for feasibility, and I might need your input.

[mrutunjay-kinagi]

Hi @tusharchou , since we are trying to align with the product framework. I think we require a dimension class which can load the data mentioned above.

from local_data_platform.store.dimension import Dimension
from local_data_platform.store.fact import Fact
from typing import List, Dict
from pathlib import Path
import pytest

class Dimension:
    def __init__(self, name: str, csv_path: Path):
        self.name = name
        self.csv_path = csv_path
        self.data = [{
                    'name': 'baked ham glazed with pineapple and chipotle peppers',
                    'minutes': 85,
                    'submitted': '2005-11-28',
                    'tags': "['ham', 'time-to-make', 'course', 'main-ingredient', 'cuisine', 'preparation', 'occasion', 'north-american', 'lunch', 'main-dish', 'pork', 'american', 'mexican', 'southwestern-united-states', 'tex-mex', 'oven', 'holiday-event', 'easter', 'stove-top', 'spicy', 'christmas', 'meat', 'taste-mood', 'sweet', 'equipment', 'presentation', 'served-hot', '4-hours-or-less']",
                    'n_steps': 7,
                    'steps': "['mix cornstarch with a little cold water to dissolve', 'place all ingredients except for ham in a blender and blend smooth , in a small saucepan over medium heat bring to a boil them simmer till thickened', 'preheat oven to 375 f', 'place ham , cut end down , in a large baking pan and score skin', 'bake ham for 15 minutes', 'brush glaze over ham and bake for another hour or until internal temperature reads 140 f', 'baste half way through baking']",
                    'description': 'sweet, smokey and spicy! go ahead and leave the seeds in if you enjoy the heat.',
                    'ingredients': "['smoked ham', 'brown sugar', 'crushed pineapple', 'chipotle chile in adobo', 'adobo sauce', 'nutmeg', 'fresh ginger', 'cornstarch', 'salt']",
                    'n_ingredients': 9,
                    'average_rating': 5.0,
                    'votes': 27,
                    'Score': 4.85275401,
                    'calories': 712.5,
                    'category': 'Non-veg'
                    }]
    
    def get(self):
        return self.data
    
    def get_name(self):
        return self.name
    
    def get_csv_path(self):
        return self.csv_path



def test_load_csv_data_to_dimension():
    csv_path = 'data/food_recipes.csv'
    recipes_table = Dimension('Recipes', csv_path)
    
    
    assert type(recipes_table.get()) == List[Dict]


recipe_table = Dimension("food","data/food_recipes.csv")
print(type(recipes_table.get()))

[tusharchou]

@mrutunjay-kinagi added this requirement in #83