Allay - For all the Beautiful People out there

We have realized that a lot of people, women especially stress a lot about their weight and diet which also affects their mental health. So, We created Allay - For all the Beautiful People out there

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Allay

💡 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.
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