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import random
import pandas as pd
import numpy as np
import pickle
import os
SERVER_FILE_DIR = os.path.dirname(os.path.abspath(__file__))
NUTRITION_MODEL_PATH = os.path.join(SERVER_FILE_DIR, "../resources/models/nutrition_model.pkl")
MEALS_JSON_PATH = os.path.join(SERVER_FILE_DIR, "../resources/datasets/meals.json")
# Ensure the file exists
if not os.path.exists(MEALS_JSON_PATH):
raise FileNotFoundError(f"File {MEALS_JSON_PATH} does not exist")
df = pd.read_json(MEALS_JSON_PATH)
class NutritionModel:
def __init__(self):
self.load()
def generate_plan(self, calories):
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(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)
}
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])
lunch = self.nutrition_model.transform(lunch_df)
breakfast = self.nutrition_model.transform(breakfast_df)
dinner = self.nutrition_model.transform(dinner_df)
snack = self.nutrition_model.transform(snack_df)
meals = np.concatenate((breakfast, lunch, dinner, snack), axis=0)
meals = np.transpose(meals)
days = []
for i in range(7):
day_meals = df.iloc[meals[i]].to_dict(orient="records")
days.append(day_meals)
return days
def load(self):
with open(NUTRITION_MODEL_PATH, "rb") as f:
self.nutrition_model = pickle.load(f)
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