NutriLens: An AI-Powered Food Intelligence Assistant

Detect. Analyze. Eat Smart. NutriLens is an end-to-end web application that leverages the power of Artificial Intelligence to provide instantaneous food recognition, detailed nutritional analysis, and personalized dietary guidance. Simply upload a picture of your meal, and let NutriLens do the rest!

📋 Table of Contents

• Introduction
• ✨ Key Features
• 🛠️ Tech Stack
• 🚀 Getting Started
  • Prerequisites
  • Installation & Setup
• 📖 User Guide
• 📈 Project Analysis
  • Pros
  • Cons & Future Improvements
• 🤝 Contributors
• 📜 License

Introduction

In a world with countless dietary choices, making informed decisions about nutrition can be overwhelming. NutriLens was built to solve this problem by acting as a personal AI nutritionist in your pocket. It combines state-of-the-art object detection with advanced generative AI to deliver a seamless and intuitive user experience.

This project is not just a concept; it's a fully functional, end-to-end deployed application that showcases a deep understanding of frontend development, backend architecture, API integration, and machine learning model implementation. The core of the application is a custom-trained YOLOv8 model designed to recognize a wide variety of food items, with a special focus on Indian cuisine.

Key Features

NutriLens is packed with features designed to empower users on their health journey:

• 📸 AI-Powered Food Recognition: Upload an image of your meal, and our custom-trained YOLOv8 model instantly identifies the food items with high accuracy. You can also get nutritional data by manually typing the food name.
• 🔬 Detailed Nutritional Analysis: Get a comprehensive breakdown of your meal, including calories, macronutrients (protein, carbs, fat), vitamins, and minerals. The analysis also includes health benefits, potential disadvantages, and fun trivia about the food.
• 🥗 Personalized Diet Planning: Generate a complete diet plan customized to your age, gender, height, weight, activity level, dietary preferences, and fitness goals. The plan includes daily nutritional targets, meal options, and hydration tips.
• 🧪 Ingredient Safety Analysis: Paste the ingredients list from any packaged food item to receive an AI-driven analysis of its contents. The report highlights positive and negative ingredients and provides a final health verdict with suggestions for healthier alternatives.
• 🍲 Recipe Generation & Alternatives: Discover new recipes by simply entering the name of a dish. For any given recipe, you can also request a healthier, alternative version with just one click.
• 🛒 Automated Shopping Cart: Automatically add all ingredients from a generated recipe to a dynamic shopping list. You can also manually add, edit, and remove items, and copy the entire list to your clipboard.

Tech Stack

This project integrates a variety of modern technologies to deliver a robust and feature-rich experience.

Category	Technology / Library
Backend	Python, Flask
AI / ML	Google Gemini API (for generative content), YOLOv8 (Ultralytics), PyTorch, OpenCV
Frontend	HTML5, CSS3, Vanilla JavaScript
Data/Session	Flask Session (for Shopping Cart)
Deployment	Gunicorn

Getting Started

Follow these instructions to get a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

• Python 3.10 or higher
• pip (Python package installer)
• Git version control

Installation & Setup

1. Clone the Repository

   git clone [https://github.com/your-username/NutriLens.git](https://github.com/your-username/NutriLens.git)
   cd NutriLens

2. Create a Virtual Environment It's highly recommended to create a virtual environment to manage project dependencies.

   # For Windows
   python -m venv venv
   venv\Scripts\activate
   
   # For macOS/Linux
   python3 -m venv venv
   source venv/bin/activate

3. Install Dependencies All the required Python packages are listed in the requirements.txt file.

   pip install -r requirements.txt

4. Set Up Environment Variables This application requires API keys to function. Create a file named .env in the root directory of the project. Open the .env file and add the following lines, replacing your_api_key_here with your actual keys:

   GEMINI_API_KEY="your_google_gemini_api_key_here"
   FLASK_SECRET_KEY="any_strong_random_secret_key_here"

   Important: The GEMINI_API_KEY is essential for all generative AI features. The FLASK_SECRET_KEY is used to secure the user session for the shopping cart.

5. Run the Flask Application Once the dependencies are installed and environment variables are set, you can start the Flask server.

   flask run

   The application will be running at http://127.0.0.1:5000. Open this URL in your web browser.

User Guide

Navigating NutriLens is simple and intuitive.

1. Homepage: The homepage provides an overview of all the features. You can navigate to any feature from the "Explore" section.
2. Nutritional Content:
   • Click on "Nutritional Content".
   • Either upload an image of your food or type the name of a food item into the text box.
   • Select a portion size and click "Get Info" to see a detailed nutritional report.
3. Meal Plan:
   • Navigate to the "Meal Plan" page.
   • Fill out the form with your personal details, lifestyle choices, and health goals.
   • Click "Generate Personalized Diet Plan" to receive a comprehensive plan tailored to you.
4. Ingredients Analysis:
   • Go to the "Ingredients" page.
   • Copy and paste the list of ingredients from a food package into the text area.
   • Select the commodity type (e.g., Snacks, Beverage) and click "Analyze Ingredients" for a safety and health report.
5. Shopping Cart:
   • When viewing a recipe, you can automatically add its ingredients to your cart.
   • On the "Shopping Cart" page, you can manually add items, edit quantities, and remove items.
   • Use the "Copy Items" button to copy your shopping list for easy use.

Project Analysis

Pros

• Innovative AI Integration: Effectively combines computer vision (YOLOv8) with a powerful LLM (Gemini) to provide a unique, multi-faceted user experience.
• Custom-Trained Model: The use of a custom-trained YOLOv8 model for food recognition, especially with a focus on Indian cuisine, makes the application highly relevant and accurate for its target audience.
• Comprehensive Feature Set: The application goes beyond simple recognition, offering a suite of genuinely useful tools like diet planning, recipe generation, and ingredient analysis.
• End-to-End Functionality: This is a complete, deployable web application with a functional frontend, a robust backend, and seamless API integration, demonstrating a full-stack development skillset.

Cons & Future Improvements

• No User Accounts: The shopping cart currently relies on Flask's session management. Implementing user authentication and a database (like PostgreSQL or MongoDB) would allow users to save their diet plans, favorite recipes, and track their nutritional history over time.
• API Dependency & Cost: The application's core functionality relies heavily on the Google Gemini API, which may incur costs at scale and is dependent on external service availability.
• Static Frontend: The frontend is built with vanilla JavaScript. Migrating to a modern framework like React or Vue.js could create a more dynamic, responsive, and stateful user interface.
• Batch Image Processing: Currently, images are processed one by one. Future versions could allow users to upload multiple images at once for batch analysis.

Contributors

This project was developed as part of our College academic curriculum by me and my team. It represents our collective effort to apply our software development and machine learning skills to build a real-world, impactful application.

License

This project is licensed under the MIT License.