Meal planner

This is a "meal_plan" application that plans meals for you using a meal dataset from Fineli

Demo: https://meal.riko.io/meal-plan

Sign in: https://meal.riko.io/

Run locally as a website

After you have Docker on your local machine, clone the repo and run the following command in the folder:

$ make run

After this the Meal Planner UI is served in your local port http://0.0.0.0:5000/ and http://0.0.0.0:5000/meal-plan.

Run on a server as a website

First, you have to create an Nginx-proxy to your server for controlling the traffic (simple instruction here). After you have gone through the instructions and updated docker-compose.yml, you can just download this repo to your server and run the following command in the folder.

$ make server

Then just enjoy your meals :D

Ps. It might not work right away, because it takes a few minutes to get the https certificate for the website, so be patient.

Run tests

python -m unittest

Details about the Meal Plan application

Requirements:

1. Total calories per day should be as close to 2000 kcal as possible.

• The system is capable to produce exact number but due to roundings and unclear energy calculations (see Details section 7.) these are not always exactly 2000 kcal.

2. Energy division by macronutrients, as accurately as possible:

• 50 % of energy should be from carbs
• 30 % from proteins
• 20 % from fat

3. At least 20 g of fiber per day

• Due to rounding error, this might sometimes be under 20g and hence, the requirement is currently 25g in the algorithm.

4. At most 500 g of a single food item per day

• This limitation has no problems

5. No items from the same food group (e.g. “cake” or “cheese”) on successive days

• If only looking at the successive days, the algorithm would only suggest two different meals every other day. Hence, to improve this requirement I have added categories (see Details section 3.) and expanded the restrictions of previous meals (see Details section 4.).

6. At least 3 different food groups need to be chosen for every day

• This constraint is satisfied naturally throughout all the other constraints

7. Sufficient tests for loading the data from the url & seeing it fits the schema. Elegantly handle such a situation where these fail.

• All the input data is run through a schema every time it is used in the algorithm. If there are any problems in the data, the system will inform what are the problems. Data import tests can be also ran with python -m unittest.

Details

1. Alcohol

Food that has over 0.4g/100g of alcohol will be removed (including tiramisu that includes rum).

2. Salt and sodium

If the amount of salt/sodium is not limited, the algorithm may suggest way too much salt and sodium. Hence, I will add an option to limit salt.

3. Improved food categories

The categories in the dataset seem to work as following:

Food_1: "Hamburger, Chicken Burger, Mcdonald'S" --> category "Hamburger"
Food_2: "Yoghurt, Flavoured, 7% Fat" --> category "Yoghurt"

So it seems that the first section if the category (when divided by comma) and the following sections are some extra info.

To improve the recommendations, on top of the current constraints, I will list some common food ingredients to an "extra_categoty". This is to make sure that there is enough variation in the food so that it is healthier and more appealing.

These "extra_categoty" ingredients are: chicken, pork, beef, ham, kebab, fish, cheese, shrimp, porridge, hamburger, pasta, salmon, cake, lamb, tofu.

Constraint of this improvement is that one meal can get only 1 extra category. E.g. "Chicken Hamburger, Mcdonald'S" get chicken an not hamburger because it is first in the list.

Count of these ingredient in categories (first section) and foods (the whole food name)

Ingredient	Category count this occurs	Food count this occurs
chicken	80	125
beef	78	142
pork	54	106
cheese	44	108
cake	38	118
fish	37	83
porridge	33	81
ham	26	40
pasta	22	47
shrimp	14	15
salmon	19	34
hamburger	10	39
lamb	10	16
tofu	9	11
kebab	6	7

However, these categories could be improved even more but that is something to do in future implementations.

Distribution for ingredient count per category

4. Previous meals

To improve your meal plans I have added a constraint so that the system will not recommend similar meal plans that you have had in the same week. This is done by locking used meals and releasing them evenly during a long period of time in random order.

Without these improved constraints, the meal planner will suggest two different meals every other day.

5. Allergies

Now I have added one optional allergy constraint which gives you a possibility to rule out any food that has Lactose.

6. Improving the meal plans with better data

In future these meal plans could be improved by going throught that Fineli data and cleaning. Also trying to find more relevant food/meal databanks could improve the quality and variety of these meal plans.

7. The actula amount of energy of every meal

The amount of energy (kcal or kJ) from a 1 gram of macronutrient is generally calculated as follows:

• 1g of carbs = 4 kcal (17 kJ)
• 1g of protein = 4 kcal (17 kJ)
• 1g of alcohol = 7 kcal (30 kJ)
• 1g of fat = 9 kcal (38 kJ)

When calculating the energy sum of macronutrients of each food item in the Fineli dataset, most of them are close to the informer energy,calculated (kJ). To be exact 96.8% of the informed energy was within -10% to +30% of the calculated energy sum of macronutrients.

However, there are some products that are extreme outliers. This might be caused by some ingredients that are not listed in macronutrient but are added to the energy,calculated (kJ). For example fibre, xylitol, and sorbitol are not included in either sugar or carbs even though it clearly exists in the product energy calculated (kJ).

This makes sense to some level because fibre does not really contribute calories to the body, in a roundabout way (source). On the other hand, Sorbitol and Xylitol include calories, about 2.5 calories per gram, but that is significantly less than normal sugar which is about 4 calories per gram, but they don't seem to be calculated as sugar at all. See the problem cases under here:

Row	name	energy,calculated (kJ)	fat, total (g)	carbohydrate, available (g)	protein, total (g)	fibre, total (g)	sugars, total (g)	alcohol (g)
2369	Oat, Coarse-Ground Oat, Kaurakuitunen	1075	3.3	16.1	7.2	69.7	0.6	0
2542	Pastille Sweetened With Xylitol	1000	0.8	0	0.5	0	0	0
3472	Sorbitol	1000	0	0	0	0	0	0

Most significant outliers

Row	name	energy,calculated (kJ)	fat, total (g)	carbohydrate, available (g)	protein, total (g)	fibre, total (g)	sugars, total (g)	alcohol (g)	energy / sum of macronutrients
3074	Rowanberry, Dried, Rowanberry Powder	1103	6.9	8.2	8.8	50.3	7.5	0	2.001089
2369	Oat, Coarse-Ground Oat, Kaurakuitunen	1075	3.3	16.1	7.2	69.7	0.6	0	2.061361
3382	Seaweed, Wakame, Dried	696	2.0	0	14.4	47.1	0	0	2.169576
941	Chokeberry, Dried, Chokeberry Powder	1013	2.4	16.3	5.1	49.1	14.9	0	2.226374
2502	Parsley	114	0.2	1.1	1.4	8.0	0.8	0	2.275449
3075	Rowanberry, Sorbus	313	1.2	4.2	1.1	6.5	4.1	0	2.306559
972	Coffee, Instant, Drink	4	<0.1	<0.1	0.1	0	0	0	2.352941
1736	Lemon, Without Skin	138	0.2	2.2	0.6	2.8	2.2	0	2.500000
1614	Jerusalem Artichoke	218	0.1	2.9	1.8	16.4	2.8	0	2.604540
1737	Lemon, With Skin	90	0.1	1.4	0.4	1.8	1.4	0	2.616279
1739	Lemon Juice, Unsweetened, Undiluted	91	0	1.6	0.3	0.1	1.6	0	2.817337
2543	Pastille Sweetened With Xylitol, Added Vitamin C	974	0.8	8.0	1.1	0	8.0	0	5.262021
3939	Vinegar, Wine Vinegar	86	0	0.5	0	0	0.5	0	10.117647
2791	Psyllium Husks	728	0.6	0	1.5	85.0	0	0	15.072464
3938	Vinegar	138	0	0.5	0	0	0.5	0	16.235294
1406	Full- Xylitol Pastille	937	0.8	0	0.5	0	0	0	24.087404
2542	Pastille Sweetened With Xylitol	1000	0.8	0	0.5	0	0	0	25.706941
3300	Salty Liqourice Pastille, Unsweetened	799	0.2	0	0.1	NaN	0	0	85.913978
1275	Fitness Drink With Added Vitamins, Artificiall...	4	0	0	0	0.5	0	0	inf
3662	Sweet, Candy, Sugar-Free	812	0	0	0	34.0	0	0	inf
3733	Tea	1	0	0	0.1	0	0	0	inf
3734	Tea, Green Tea	1	0	0	0.1	0	0	0	inf
3472	Sorbitol	1000	0	0	0	0	0	0	inf
782	Chewing Gum, Xylitol Sweetened	773	0	0	0	2.4	0	0	inf
76	Baking Soda, Bicarbonate Of Soda	0	0	0	0	0	0	0	NaN
1184	Erythritol	0	0	0	0	0	0	0	NaN
1631	Juice Drink, Sugar-Free, Artificially Sweetened	0	0	0	0	0	0	0	NaN
2123	Mineral Water	0	0	0	0	0	0	0	NaN
2124	Mineral Water, Low Sodium	0	0	0	0	0	0	0	NaN
2129	Mineral Water, Novelle Plus, With Added Calcium	0	0	0	0	0	0	0	NaN
2130	Mineral Water, Novelle Plus, With Added Vitami...	0	0	0	0	0	0	0	NaN
3293	Salt, Rock Salt, Without Iodine	0	0	0	0	0	0	0	NaN
3379	Seasalt, Without Iodine	0	0	0	0	0	0	0	NaN
3465	Soft Drink, Light, Sugar-Free	0	0	0	0	0	0	0	NaN
3678	Sweetener, Cyclamate	0	0	0	0	0	0	0	NaN
3680	Sweetener, Hermesetas Liquid, Saccharin And Cy...	0	0	0	0	0	0	0	NaN
3681	Sweetener, Saccharin	0	0	0	0	0	0	0	NaN
3735	Tea, Herbal Tea	0	0	0	0	0	0	0	NaN
3953	Water, Tap Water	0	0	0	0	0	0	0	NaN

Outliers dirtribution