backend.py

# -*- coding: utf-8 -*-
'''
CheeseHacks Diet Reccomender

Created by: Team Cheesy-Weazies
Members:    Mikhail Ortiz-Lunyov, Yixuan Zheng, Mia Chen, Sojung Lee

This program takes in the user's eating habits,
 and then returns suggestions for healthier meals
 as nessesary.
This program assumes that age is 19 or above.

External Sources:
    Data Set:
        https://github.com/soumillll/Diet-Recommendation-System/blob/main/input.csv
    Calories Needed:
        https://www.healthline.com/nutrition/how-many-calories-per-day
        https://www.omnicalculator.com/health/bmr-harris-benedict-equation
'''
import pandas as pd

# Step 1: Take user's input is handled by Flask in views.py

# Step 2: Calculate calorie needs based on the user's parameters
def calculate_calorie_needs(gender, weight, height, age, activity_level):
    # BMR calculation using Harris-Benedict equation
    if gender.lower() == 'male':
        bmr = 88.362 + (13.397 * weight) + (4.799 * height) - (5.677 * age)
    else:
        bmr = 447.593 + (9.247 * weight) + (3.098 * height) - (4.330 * age)

    # Activity level multiplier
    activity_multiplier = {
        'sedentary': 1.2,
        'lightly active': 1.375,
        'moderately active': 1.55,
        'very active': 1.725,
        'extra active': 1.9
    }

    return bmr * activity_multiplier[activity_level.lower()]

# Step 3: Set up a diet plan based on the calorie need
def get_diet_plan(calorie_needs):
    # Read the dataset
    food_data = pd.read_csv(r'/Users/shuyizhang/Downloads/input.csv')

    # Assuming there's an additional column 'Unit' in the dataset
    food_data['Unit'] = food_data['Food_items'].apply(lambda x: 'ml' if 'juice' in x.lower() or 'milk' in x.lower() or 'tea' in x.lower() or 'coffee' in x.lower() else 'g')

    # Define high and low quantity caps
    high_cap = 700
    low_cap = 300

    # List of items to apply the low cap
    low_cap_items = ['egg', 'butter', 'cheese']

    # Calculate the quantity for each food item and update the 'Unit' column
    food_data['Quantity'] = food_data.apply(lambda row: min(40 * (calorie_needs / row['Calories']), high_cap if row['Food_items'].lower() not in low_cap_items else low_cap), axis=1)
    food_data['Unit'] = food_data.apply(lambda row: f"{row['Quantity']:.2f} {row['Unit']}", axis=1)

    # Filter the food items that fit within the calorie need
    suitable_foods = food_data[food_data['Calories'] <= calorie_needs]

    # Constructing a diet plan
    breakfast = suitable_foods[suitable_foods['Breakfast'] == 1].sample(2)  # 2 items for breakfast
    lunch = suitable_foods[suitable_foods['Lunch'] == 1].sample(2)  # 2 items for lunch
    dinner = suitable_foods[suitable_foods['Dinner'] == 1].sample(1)  # 1 item for dinner

    diet_plan = pd.concat([breakfast, lunch, dinner])
    return diet_plan.to_dict('records')

# Step 4: Get feedback from the user is handled by Flask in views.py