🥗 Hybrid Diet Recommendation Engine

A locally hosted, hybrid diet recommendation system that generates realistic Indian weekly meal plans using a combination of Content-Based Filtering (CBF) and Collaborative Filtering (CF).

The system calculates BMI, considers dietary goals and activity levels, and produces a 7-day meal plan (2000–2600 kcal/day) tailored to the user.

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🚀 Project Overview

This application:

• ✅ Runs entirely on localhost
• ✅ Uses Java + Spring Boot (backend)
• ✅ Uses HTML / CSS / JavaScript (frontend)
• ✅ Uses static Indian food datasets
• ✅ Generates weekly diet plans with calendar toggle UI
• ✅ Works fully offline

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🎯 Core Features

✅ User Inputs (Mandatory)

Input	Options
Height	cm
Weight	kg
Dietary Goal	Weight Loss, Maintenance, Muscle Gain
Dietary Type	Vegetarian, Non-Vegetarian
Activity Level	Sedentary, Moderate, Active

✅ Optional Inputs

• Preferred foods
• Disliked foods

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🧠 Intelligent Logic

1️⃣ BMI Calculation

BMI is calculated using:

BMI = weight (kg) / (height in meters)²

The system categorizes the user into:

• Underweight (BMI < 18.5)
• Normal (18.5 ≤ BMI < 25)
• Overweight (25 ≤ BMI < 30)
• Obese (BMI ≥ 30)

2️⃣ Calorie Target (Realistic Indian Range)

The daily calorie intake is constrained between 2000 kcal – 2600 kcal.

It varies depending on:

• Dietary goal
• Activity level
• BMI category

Example:

Goal	Calorie Range
Weight Loss	~2000–2200 kcal
Maintenance	~2200–2400 kcal
Muscle Gain	~2400–2600 kcal

All recommendations reflect realistic Indian eating habits.

3️⃣ Hybrid Recommendation Model

🔹 Content-Based Filtering (CBF)

Matches foods based on:

• Calories
• Protein
• Carbs
• Fat
• Dietary type
• Meal type (breakfast/lunch/dinner)

Filters only Indian foods and respects veg/non-veg constraints.

🔹 Collaborative Filtering (CF)

• Uses static user_interaction.csv
• Identifies similar user preferences
• Adjusts ranking scores

🔹 Hybrid Output

Final recommendation score:

Final Score = α × (CBF Score) + (1 - α) × (CF Score)

Where α is configurable (e.g., 0.7), ensuring personalization + nutritional accuracy.

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📅 Weekly Plan Generation

The system generates a 7-day meal plan, with each day including:

• 🌅 Breakfast
• 🍛 Lunch
• 🌙 Dinner

🔄 Calendar Toggle UI

Frontend includes:

• Day-based navigation (Mon–Sun)
• Simple clickable tabs
• Dynamically loads meals for selected day
• No page reload required

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🏗️ System Architecture

Backend (Java + Spring Boot)

src/main/java/com/dietapp/
│
├── controller/
│   ├── UserController.java
│   ├── FoodController.java
│   └── RecommendationController.java
│
├── service/
│   ├── RecommendationService.java
│   ├── ContentBasedFilter.java
│   ├── CollaborativeFilter.java
│   └── MealPlanGenerator.java
│
├── model/
│   ├── UserPreference.java
│   ├── Food.java
│   ├── Meal.java
│   ├── MealPlan.java
│   └── DailyMealPlan.java
│
└── util/
    └── DataLoader.java

Frontend (HTML/CSS/JS)

src/main/resources/static/
│
├── index.html
├── css/
│   └── styles.css
└── js/
    └── script.js

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📊 Dataset Structure

food_data.json

{
  "id": 101,
  "name": "Rajma Chawal",
  "calories": 520,
  "protein": 18,
  "carbs": 85,
  "fat": 8,
  "dietary_type": "vegetarian",
  "meal_type": "lunch"
}

user_interaction.csv

user_id,food_id,rating
1,101,5
1,205,4
2,101,3

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🔧 Installation & Setup

1️⃣ Prerequisites

• Java 11+
• Maven
• Web browser

2️⃣ Clone Repository

git clone <repository-url>
cd DietRecommendationEngine

3️⃣ Build Project

mvn clean install

4️⃣ Run Application

mvn spring-boot:run

5️⃣ Open in Browser

http://localhost:8080

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