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import random
import pandas as pd
import numpy as np
import pickle
import sys
import os
import pickle
SERVER_FILE_DIR = os.path.dirname(os.path.abspath(__file__))
NUTRITION_MODEL_PATH = os.path.join(
SERVER_FILE_DIR, *"../resources/models/nutrition_model.pkl".split("/")
)
class NutritionModel:
def generate_plan(self,calories):
the_model = self.nutrition_model
lunch_attr = {"Calories":calories*0.5,
"FatContent":random.uniform(19, 97),
"SaturatedFatContent":random.uniform(6, 12),
"CholesterolContent": random.uniform(77, 299),
"SodiumContent":random.uniform(565, 2299),
"CarbohydrateContent":random.uniform(28, 317),
"FiberContent": random.uniform(2, 38),
"SugarContent": random.uniform(0, 38),
"ProteinContent":random.uniform(20, 123)}
lunch_df = pd.DataFrame(lunch_attr, index=[0])
breakfast_attr = {"Calories":calories*0.30,
"FatContent":random.uniform(8.7, 20),
"SaturatedFatContent":random.uniform(1.7, 3.7),
"CholesterolContent": random.uniform(0, 63),
"SodiumContent":random.uniform(163, 650),
"CarbohydrateContent":random.uniform(23, 56),
"FiberContent": random.uniform(2.6, 8),
"SugarContent": random.uniform(3.5, 13),
"ProteinContent":random.uniform(6, 25)}
breakfast_df = pd.DataFrame(breakfast_attr, index=[0])
dinner_attr = {"Calories":calories*0.30,
"FatContent":random.uniform(15, 33),
"SaturatedFatContent":random.uniform(6, 8),
"CholesterolContent": random.uniform(22, 86),
"SodiumContent":random.uniform(265, 775),
"CarbohydrateContent":random.uniform(14, 44),
"FiberContent": random.uniform(101, 110),
"SugarContent": random.uniform(3, 13),
"ProteinContent":random.uniform(11, 25)}
dinner_df = pd.DataFrame(dinner_attr, index=[0])
snack_attr = {"Calories":random.uniform(90, 190),
"FatContent":random.uniform(1.7, 10),
"SaturatedFatContent":random.uniform(0.7, 3),
"CholesterolContent": random.uniform(2, 16),
"SodiumContent":random.uniform(47, 200),
"CarbohydrateContent":random.uniform(10, 31),
"FiberContent": random.uniform(0.4, 2.5),
"SugarContent": random.uniform(5.7, 21),
"ProteinContent":random.uniform(3, 20)}
snack_df = pd.DataFrame(snack_attr, index=[0])
drinks_attr = {"Calories":random.uniform(60, 125),
"FatContent":random.uniform(0.2, 0.6),
"SaturatedFatContent":random.uniform(0, 0.1),
"CholesterolContent": random.uniform(0, 0.1),
"SodiumContent":random.uniform(3.5, 51),
"CarbohydrateContent":random.uniform(14, 30),
"FiberContent": random.uniform(0.2, 3.6),
"SugarContent": random.uniform(109, 122),
"ProteinContent":random.uniform(0.4, 6)}
drink_df = pd.DataFrame(drinks_attr, index=[0])
lunch = the_model.transform(lunch_df)
breakfast = the_model.transform(breakfast_df)
dinner = the_model.transform(dinner_df)
snack = the_model.transform(snack_df)
drink = the_model.transform(drink_df)
meals = np.concatenate((breakfast, lunch, dinner, snack, drink), axis=0)
meals = np.transpose(meals)
return meals
def load(self):
with open(NUTRITION_MODEL_PATH, "rb") as f:
self.nutrition_model = pickle.load(f)
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