Allay(https://devpost.com/software/allay-for-all-the-beautiful-people-out-there)

💡 Inspiration

It is common to see people with a lot of stress have eating disorders. This results in many mental problems and physical problems. We want to help people to overcome this problem by providing a platform for them. We have created an AI-Powered Eating Disorder Support System. The goal of this project is to develop an AI-powered support system to help individuals with disordered eating patterns. This system will provide individuals with disordered eating patterns with a supportive, accessible, and convenient resource that can help them manage their symptoms, improve their physical and mental health, and build positive, sustainable habits around food and eating. Expected Outcome: The ultimate goal of this project is to empower individuals with disordered eating patterns to reclaim control over their relationship with food and live a happier, healthier life.

💻 What it does

The system will use a combination of machine learning algorithms, natural language processing (NLP) techniques, and expert-curated content to provide users with personalized support and guidance. Here are some features of the system:

• Music: based on emotion
• Physical Activities: suggestion of light exercises, yoga, and video games
• Therapy: To help users to overcome their problems
• Cognitive-behavioral therapy (CBT) exercises: The system will offer users access to CBT-based activities and exercises designed to help them reframe negative thoughts and behaviors around food and body image.

⚙️ How we built it

• Frontend: React JS
• Backend: Node JS
• Database: MongoDB
• Machine Learning: Python

🗃️ Best Use of MongoDB Atlas

We relied heavily on MongoDB Atlas for our project, which is one of the reasons we were able to construct such a technically challenging project so quickly. Our database was MongoDB, and we interacted with it via the graphical user interface client Mongo Atlas. As a second layer of caching, we used MongoDB. In this approach, we employ storage and computation more efficiently to give users of our software a super-fast experience. We linked our user login information and user history in DB collections. It was enjoyable to use because of MongoDB's incredibly simple interface.

🌐 Best Domain Name from Domain.com

• stress-free.tech

🧠 Challenges we ran into

• We had a hard time with the machine learning part. We had to learn a lot of new things to make it work.
• Due to different time zones, we had to work at different times. This made it difficult to communicate with each other.
• Building the frontend and backend was a challenge for us.

🏅 Accomplishments that we're proud of

• We are proud of the fact that we were able to build a complete project in such a short time.
• Implementing the machine learning part.
• Making the frontend and backend work together.

📖 What we learned

• We learned how to use MongoDB Atlas.
• We learned how to make a machine-learning model.
• We learned how to connect the frontend and backend.

🚀 What's next for Allay

• More exercises to help users to overcome their problems.
• Improve the machine learning model.