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import random |
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import pandas as pd |
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import numpy as np |
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import pickle |
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import os |
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SERVER_FILE_DIR = os.path.dirname(os.path.abspath(__file__)) |
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NUTRITION_MODEL_PATH = os.path.join(SERVER_FILE_DIR, "../resources/models/nutrition_model.pkl") |
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MEALS_JSON_PATH = os.path.join(SERVER_FILE_DIR, "../resources/datasets/meals.json") |
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if not os.path.exists(MEALS_JSON_PATH): |
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raise FileNotFoundError(f"File {MEALS_JSON_PATH} does not exist") |
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df = pd.read_json(MEALS_JSON_PATH) |
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class NutritionModel: |
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def __init__(self): |
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self.load() |
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def generate_plan(self, calories): |
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lunch_attr = { |
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"Calories":calories*0.4, |
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"FatContent":random.uniform(19, 97), |
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"SaturatedFatContent":random.uniform(6, 12), |
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"CholesterolContent": random.uniform(77, 299), |
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"SodiumContent":random.uniform(565, 2299), |
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"CarbohydrateContent":random.uniform(28, 317), |
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"FiberContent": random.uniform(2, 38), |
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"SugarContent": random.uniform(0, 38), |
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"ProteinContent":random.uniform(20, 123), |
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'weight' : 30 |
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} |
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lunch_df = pd.DataFrame(lunch_attr, index=[0]) |
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breakfast_attr = { |
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"Calories":calories*0.30, |
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"FatContent":random.uniform(8.7, 20), |
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"SaturatedFatContent":random.uniform(1.7, 3.7), |
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"CholesterolContent": random.uniform(0, 63), |
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"SodiumContent":random.uniform(163, 650), |
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"CarbohydrateContent":random.uniform(23, 56), |
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"FiberContent": random.uniform(2.6, 8), |
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"SugarContent": random.uniform(3.5, 13), |
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"ProteinContent":random.uniform(6, 25), |
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'weight' : 0 |
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} |
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breakfast_df = pd.DataFrame(breakfast_attr, index=[0]) |
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dinner_attr = { |
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"Calories":calories*0.30, |
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"FatContent":random.uniform(15, 33), |
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"SaturatedFatContent":random.uniform(6, 8), |
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"CholesterolContent": random.uniform(22, 86), |
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"SodiumContent":random.uniform(265, 775), |
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"CarbohydrateContent":random.uniform(14, 44), |
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"FiberContent": random.uniform(201, 210), |
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"SugarContent": random.uniform(3, 13), |
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"ProteinContent":random.uniform(11, 25), |
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'weight' :10 |
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} |
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dinner_df = pd.DataFrame(dinner_attr, index=[0]) |
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snack_attr = { |
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"Calories":random.uniform(90, 190), |
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"FatContent":random.uniform(1.7, 10), |
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"SaturatedFatContent":random.uniform(0.7, 3), |
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"CholesterolContent": random.uniform(2, 16), |
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"SodiumContent":random.uniform(47, 200), |
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"CarbohydrateContent":random.uniform(10, 31), |
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"FiberContent": random.uniform(0.4, 2.5), |
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"SugarContent": random.uniform(5.7, 21), |
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"ProteinContent":random.uniform(3, 20), |
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'weight' :40 |
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} |
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snack_df = pd.DataFrame(snack_attr, index=[0]) |
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lunch = self.nutrition_model.transform(lunch_df) |
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breakfast = self.nutrition_model.transform(breakfast_df) |
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dinner = self.nutrition_model.transform(dinner_df) |
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snack = self.nutrition_model.transform(snack_df) |
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meals = np.concatenate((breakfast, lunch, dinner, snack), axis=0) |
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meals = np.transpose(meals) |
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days = [] |
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for i in range(7): |
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day_meals = df.iloc[meals[i]].to_dict(orient="records") |
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days.append(day_meals) |
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return days |
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def load(self): |
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with open(NUTRITION_MODEL_PATH, "rb") as f: |
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self.nutrition_model = pickle.load(f) |
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