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# import numpy as np
# import pandas as pd
# from sklearn.model_selection import train_test_split
# from sklearn.neural_network import MLPRegressor
# from sklearn.preprocessing import StandardScaler
# # Step 1: Create random food data
# def create_random_food_data(num_samples=100):
# np.random.seed(42)
# areas = ['American', 'Mexican', 'Italian', 'Indian', 'Chinese']
# categories = ['Beef', 'Chicken', 'Vegetarian', 'Seafood', 'Pork']
# vegetarian_categories = ['Vegetarian']
# data = []
# for _ in range(num_samples):
# area = np.random.choice(areas)
# category = np.random.choice(categories)
# ingredients_count = np.random.randint(3, 10)
# calories = np.random.randint(200, 600)
# protein = np.random.randint(10, 40)
# carbs = np.random.randint(20, 70)
# fats = np.random.randint(5, 30)
# data.append([area, category, ingredients_count, calories, protein, carbs, fats])
# df = pd.DataFrame(data, columns=['Area', 'Category', 'IngredientsCount', 'Calories', 'Protein', 'Carbs', 'Fats'])
# return df
# # Step 2: Preprocess the Data
# def preprocess_data(df):
# features = df[['Area', 'Category', 'IngredientsCount']]
# targets = df[['Calories', 'Protein', 'Carbs', 'Fats']]
# # Encode categorical variables
# features = pd.get_dummies(features, columns=['Area', 'Category'])
# return features, targets
# # Step 3: Train the MLP Model
# def train_mlp_model(X, y):
# # Split data into training and test sets
# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# # Standardize the features
# scaler = StandardScaler()
# X_train_scaled = scaler.fit_transform(X_train)
# X_test_scaled = scaler.transform(X_test)
# # Initialize and train the MLP model
# mlp_model = MLPRegressor(hidden_layer_sizes=(100, 50), max_iter=500, random_state=42)
# mlp_model.fit(X_train_scaled, y_train)
# return mlp_model, scaler
# # Step 4: Generate Diet Plan
# def generate_diet_plan(mlp_model, scaler, total_calories, num_meals, region, diet_preference, foods_df):
# meal_names = ['Breakfast', 'Morning Snack', 'Lunch', 'Afternoon Snack', 'Dinner']
# calorie_distribution = [0.25, 0.10, 0.35, 0.10, 0.20]
# # Adjust the distribution if number of meals is less than 5
# if num_meals < 5:
# calorie_distribution = calorie_distribution[:num_meals]
# calorie_distribution = [x / sum(calorie_distribution) for x in calorie_distribution]
# elif num_meals > 5:
# # Evenly distribute the remaining calories across the extra meals
# extra_meals = num_meals - 5
# extra_meal_calories = (sum(calorie_distribution) - 1) / extra_meals
# calorie_distribution.extend([extra_meal_calories] * extra_meals)
# calorie_distribution = [x / sum(calorie_distribution) for x in calorie_distribution]
# meal_names.extend([f'Extra Meal {i+1}' for i in range(extra_meals)])
# diet_plan = []
# # Filter foods based on the user's region and diet preference
# if diet_preference == 'Vegetarian':
# region_foods = foods_df[(foods_df['Area'] == region) & (foods_df['Category'] == 'Vegetarian')]
# else:
# region_foods = foods_df[foods_df['Area'] == region]
# for i in range(num_meals):
# # Randomly select a food from the filtered region and preference
# food = region_foods.sample(1).iloc[0]
# # Adjust the portion to meet the meal calorie requirement
# portion_factor = (total_calories * calorie_distribution[i]) / food['Calories']
# diet_plan.append({
# 'Meal': meal_names[i % len(meal_names)],
# 'Food': food['Category'],
# 'Area': food['Area'],
# 'IngredientsCount': food['IngredientsCount'],
# 'Calories': food['Calories'] * portion_factor,
# 'Protein': food['Protein'] * portion_factor,
# 'Carbs': food['Carbs'] * portion_factor,
# 'Fats': food['Fats'] * portion_factor
# })
# return diet_plan
# # Main Function
# if __name__ == "__main__":
# # Create random food data
# foods_df = create_random_food_data()
# # Preprocess the data
# X, y = preprocess_data(foods_df)
# # Train the MLP model
# mlp_model, scaler = train_mlp_model(X, y)
# # Get user input
# total_calories = float(input("Enter the total daily calories you want to consume: "))
# num_meals = int(input("Enter the number of meals per day: "))
# region = input("Enter your region (American, Mexican, Italian, Indian, Chinese): ")
# diet_preference = input("Enter your diet preference (Vegetarian, Non-Vegetarian): ")
# # Generate and print the diet plan
# diet_plan = generate_diet_plan(mlp_model, scaler, total_calories, num_meals, region, diet_preference, foods_df)
# for meal in diet_plan:
# print(f"{meal['Meal']}: {meal['Food']} ({meal['Area']}) - {meal['Calories']:.2f} kcal, "
# f"{meal['Protein']:.2f}g protein, {meal['Carbs']:.2f}g carbs, {meal['Fats']:.2f}g fats")
import requests
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPRegressor
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error
# Step 1: Fetch Food Data from TheMealDB API
def fetch_mealdb_data():
meals = []
for letter in 'abcdefghijklmnopqrstuvwxyz':
url = f'https://www.themealdb.com/api/json/v1/1/search.php?f={letter}'
response = requests.get(url)
data = response.json()
if data['meals']:
meals.extend(data['meals'])
return meals
# Step 2: Preprocess the Data
# def preprocess_data(meals):
# features = []
# targets = []
# for meal in meals:
# area = meal['strArea'] if meal['strArea'] else 'Unknown'
# category = meal['strCategory'] if meal['strCategory'] else 'Unknown'
# meal_type = np.random.choice(['Breakfast', 'Lunch', 'Dinner', 'Snack'])
# ingredients_count = sum([1 for i in range(1, 21) if meal[f'strIngredient{i}']])
# # Example target values (you can replace these with real data)
# calories = np.random.randint(200, 600)
# protein = np.random.randint(10, 40)
# carbs = np.random.randint(20, 70)
# fats = np.random.randint(5, 30)
# features.append([area, category, meal_type, ingredients_count])
# targets.append([calories, protein, carbs, fats])
# feature_df = pd.DataFrame(features, columns=['Area', 'Category', 'MealType', 'IngredientsCount'])
# target_df = pd.DataFrame(targets, columns=['Calories', 'Protein', 'Carbs', 'Fats'])
# # Encode categorical variables
# feature_df = pd.get_dummies(feature_df, columns=['Area', 'Category', 'MealType'])
# return feature_df, target_df
def preprocess_data(meals):
features = []
targets = []
for meal in meals:
area = meal['strArea'] if meal['strArea'] else 'Unknown'
category = meal['strCategory'] if meal['strCategory'] else 'Unknown'
meal_type = np.random.choice(['Breakfast', 'Lunch', 'Dinner', 'Snack'])
ingredients_count = sum([1 for i in range(1, 21) if meal[f'strIngredient{i}']])
meal_name = meal['strMeal'] if meal['strMeal'] else 'Unknown' # Add meal name
# Example target values (replace with real data if available)
calories = np.random.randint(200, 600)
protein = np.random.randint(10, 40)
carbs = np.random.randint(20, 70)
fats = np.random.randint(5, 30)
# Include meal_name in features
features.append([meal_name, area, category, meal_type, ingredients_count])
targets.append([calories, protein, carbs, fats])
feature_df = pd.DataFrame(features, columns=['MealName', 'Area', 'Category', 'MealType', 'IngredientsCount'])
target_df = pd.DataFrame(targets, columns=['Calories', 'Protein', 'Carbs', 'Fats'])
# Encode categorical variables except MealName
feature_df = pd.get_dummies(feature_df, columns=['Area', 'Category', 'MealType'])
return feature_df, target_df
# Step 3: Train the MLP Model
def train_mlp_model(X, y):
numeric_X = X.select_dtypes(include=[np.number])
X_train, X_test, y_train, y_test = train_test_split(numeric_X, y, test_size=0.2, random_state=42)
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
mlp_model = MLPRegressor(hidden_layer_sizes=(150, 100, 50), activation='relu', solver='adam', max_iter=1000, random_state=42)
mlp_model.fit(X_train_scaled, y_train)
y_pred = mlp_model.predict(X_test_scaled)
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
mae = mean_absolute_error(y_test, y_pred)
print(f"Model Performance:")
print(f"Mean Squared Error (MSE): {mse:.2f}")
print(f"R-squared (R²): {r2:.2f}")
print(f"Mean Absolute Error (MAE): {mae:.2f}")
return mlp_model, scaler
# Step 4: Generate Diet Plan
def generate_diet_plan(mlp_model, scaler, total_calories, num_meals, region, diet_preference, foods_df):
meal_names = ['Breakfast', 'Morning Snack', 'Lunch', 'Afternoon Snack', 'Dinner']
calorie_distribution = [0.25, 0.10, 0.35, 0.10, 0.20]
if num_meals < 5:
calorie_distribution = calorie_distribution[:num_meals]
calorie_distribution = [x / sum(calorie_distribution) for x in calorie_distribution]
elif num_meals > 5:
extra_meals = num_meals - 5
extra_meal_calories = (sum(calorie_distribution) - 1) / extra_meals
calorie_distribution.extend([extra_meal_calories] * extra_meals)
calorie_distribution = [x / sum(calorie_distribution) for x in calorie_distribution]
meal_names.extend([f'Extra Meal {i+1}' for i in range(extra_meals)])
diet_plan = []
if diet_preference == 'Vegetarian':
region_foods = foods_df[(foods_df['Area'] == region) & (foods_df['Category'] == 'Vegetarian')]
else:
region_foods = foods_df[foods_df['Area'] == region]
for i in range(num_meals):
meal_type = meal_names[i % len(meal_names)]
meal_foods = region_foods[region_foods['MealType'] == meal_type]
if meal_foods.empty:
continue
food = meal_foods.sample(1).iloc[0]
portion_factor = (total_calories * calorie_distribution[i]) / food['Calories']
diet_plan.append({
'Meal': meal_type,
'Food': food['Category'],
'Area': food['Area'],
'IngredientsCount': food['IngredientsCount'],
'Calories': food['Calories'] * portion_factor,
'Protein': food['Protein'] * portion_factor,
'Carbs': food['Carbs'] * portion_factor,
'Fats': food['Fats'] * portion_factor
})
return diet_plan
# Main Function
if __name__ == "__main__":
meals = fetch_mealdb_data()
X, y = preprocess_data(meals)
mlp_model, scaler = train_mlp_model(X, y)
total_calories = float(input("Enter the total daily calories you want to consume: "))
num_meals = int(input("Enter the number of meals per day: "))
region = input("Enter your region (American, Mexican, Italian, Indian, Chinese): ")
diet_preference = input("Enter your diet preference (Vegetarian, Non-Vegetarian): ")
diet_plan = generate_diet_plan(mlp_model, scaler, total_calories, num_meals, region, diet_preference, X)
for meal in diet_plan:
print(f"{meal['Meal']}: {meal['Food']} ({meal['Area']}) - {meal['Calories']:.2f} kcal, "
f"{meal['Protein']:.2f}g protein, {meal['Carbs']:.2f}g carbs, {meal['Fats']:.2f}g fats")
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