models/nutrition_model.py

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)