Hackathon - HackCBS 7.0
- Satyam Kumar - ML Developer
- Avinash Sharma - App Developer
- Aryan Jaiswal - App Developer
- Prince Yadav - UI & UX Designer
Ahaar is an intelligent Android application designed to enhance nutrition tracking by detecting foods in real-time, analyzing nutritional values, and helping users maintain a balanced diet. Powered by machine learning, Ahaar identifies food items on a plate, tracks nutritional intake (calories, fats, proteins, etc.) daily, weekly, and monthly, and provides personalized diet recommendations to meet individual health goals.
- Food Detection: Utilizes a machine learning model to accurately detect food items on a plate and analyze their nutritional values.
- Nutritional Analysis: Calculates detailed nutritional information (calories, fats, proteins, carbohydrates, vitamins, etc.) for each meal.
- Progress Tracking: Provides daily, weekly, and monthly summaries of nutritional intake.
- Personalized Diet Suggestions: Suggests food items to help users achieve their diet goals, whether for weight loss, muscle gain, or balanced nutrition.
- User-Friendly Dashboard: Clear and intuitive interface displaying real-time insights and recommendations.
In India, lifestyle diseases such as diabetes, obesity, and heart disease are rising at an alarming rate. Nearly 8-10% of the population is affected by diabetes, and 40% are overweight. These aren’t just numbers; they represent our family members, friends, and neighbors facing serious health challenges every day.
We created Ahaar to empower individuals to take control of their nutrition and, ultimately, their health. Our goal is to simplify meal tracking, making it accessible and insightful for every user. By providing instant nutritional information and personalized recommendations, Ahaar goes beyond calorie counting to guide users in building healthier habits. It bridges the knowledge gap many Indians have about nutrition, especially when it comes to traditional Indian foods.
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Addressing Health Issues: The increase in diabetes, heart disease, and obesity in India highlights the need for proactive nutrition management. Ahaar helps users understand what’s on their plates and make choices that reduce health risks.
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Cultural Relevance: Indian diets include unique foods like roti, dal, and samosas, which often lack clear nutritional data. Ahaar brings this information to users, making it easier to appreciate the health impact of traditional Indian meals.
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Filling the Knowledge Gap: Detailed nutritional knowledge is limited in many regions. Ahaar provides users with instant access to accurate nutritional values, empowering them to make well-informed dietary decisions.
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Time-Saving Convenience: Ahaar streamlines the process of tracking meals and analyzing nutrition, offering quick, reliable insights that help users focus on their health without extra effort.
With Ahaar, we aim to reduce lifestyle diseases by promoting awareness and supporting healthier eating choices for millions of Indians. Whether in urban centers or rural areas, Ahaar makes nutrition tracking easy, accessible, and impactful, enabling everyone to build healthier habits for a better future.
Ahaar aims to make nutrition tracking simpler, more accurate, and goal-oriented by leveraging artificial intelligence. It empowers users to make informed dietary decisions, ultimately supporting a healthier lifestyle.
- Food Detection: Ahaar uses a machine learning model to identify foods from images captured by the device's camera.
- Nutritional Calculation: Once detected, Ahaar calculates the nutritional values of each food item, including macronutrients (carbs, proteins, fats) and micronutrients (vitamins, minerals).
- Goal Tracking: Users can set dietary goals, and Ahaar will track progress across different time periods.
- Diet Recommendations: Based on the user's current intake and goals, Ahaar suggests food choices and modifications to optimize their diet.
- Android: Core application framework.
- Kotlin: Primary programming language for Android development.
- Machine Learning: Custom-trained model for food detection and nutritional estimation.
- TensorFlow Lite: Efficient inference on mobile devices.
- Python: Used for data preprocessing and training machine learning models.
- Pandas: Data manipulation and analysis.
- NumPy: Numerical operations and data handling.
- TensorFlow and Keras: Deep learning frameworks for building and training models.
- Gemini 1.5 Model: Advanced model used for precise food recognition.
- Streamlit: Interface for testing and visualizing model outputs.
- Firebase: Backend for data storage, user authentication, and analytics.
- Retrofit: Library for managing network requests in Android.
- Seaborn and Matplotlib: Visualization libraries for data exploration and analysis.
- Caloric Intake: Tracks daily, weekly, and monthly calories consumed.
- Macronutrient Breakdown: Logs proteins, fats, and carbohydrates.
- Micronutrients: Tracks essential vitamins and minerals.
- Diet Progress: Visualizes trends to help users stay on track with their goals.
- Clone the Repository
git clone https://github.com/your-username/ahaar.git