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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*0.4, | |
| "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), | |
| 'weight' : 30 | |
| } | |
| 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), | |
| 'weight' : 0 | |
| } | |
| 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(201, 210), | |
| "SugarContent": random.uniform(3, 13), | |
| "ProteinContent":random.uniform(11, 25), | |
| 'weight' :10 | |
| } | |
| 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), | |
| 'weight' :40 | |
| } | |
| 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) | |