EcoCloset

Snap, Value, Sustain: Fashion's Sustainable Future

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Inspiration

Did you know the average person spends about $1,800 a year on clothing, often discarding items that contribute to the 92 million tons of textile waste in landfills annually? Enter EcoCloset, the app revolutionizing how we value and recycle our wardrobes. With just a simple photo, our innovative AI-powered solution provides instant clothing valuation and recommendations for reselling, donating, or eco-friendly disposal. We support cultural clothing and gender expression while offering a gamified experience with rewards for sustainable practices. EcoCloset addresses critical issues: only 20% of textiles are collected for reuse or recycling globally, almost 60% of all clothing material is plastic, and clothing waste significantly contributes to air pollution and health risks. Our unique approach, combining multiple databases and AI, fills a gap identified by researchers in 2023. Users love us because we tap into the growing thrifting trend, help save and make money, build a community around sustainable fashion, and offer rewards for eco-friendly choices. Join EcoCloset in closing the loop on fashion waste while fostering a more sustainable and inclusive clothing ecosystem.

What it does

Like no other app in the industry currently as functional and feature packed, EcoCloset is a sustainability-driven platform that helps users appraise and manage their clothing more effectively while promoting eco-conscious decisions. With EcoCloset, users can:

  1. Easily Upload Photos: Users can quickly snap a picture of any garment they want to evaluate, enabling effortless interaction with the platform.
  2. AI-Powered Analysis: Our app uses Machine Learning and AI to analyze the garment’s brand, age, condition, and material. Based on this analysis, the app determines whether the clothing should be resold, recycled, or discarded responsibly.
  3. Appraisal and Value Estimation: EcoCloset provides users with an estimated resale value for their garments, helping them make informed decisions on whether the item is worth selling or donating.
  4. Location-Based Drop-Off Suggestions: The app offers convenient location detection, helping users find nearby thrift stores or donation centers to drop off their clothes, making the process of donating and recycling more accessible.
  5. Gamified Experience and Rewards: EcoCloset includes a leaderboard system that tracks user activity and rewards them with coupons for thrift stores as they donate or recycle more clothes, incentivizing sustainable behavior through gamification.
  6. Educational Features: The app educates users about the environmental impact of different types of materials, particularly the effects of synthetic fibers on the planet. This encourages users to donate instead of disposing of items, thereby reducing their environmental footprint.
  7. By combining convenience, education, and a rewards system, EcoCloset promotes a sustainable lifestyle while helping users declutter and extend the life of their clothing.

How we built it

We built EcoCloset using two different versions, each tailored to specific user needs and technical requirements.

The primary version uses React and Next.js for a modern, responsive front-end that allows users to interact with the app across devices. On the backend, we use Flask and Node.js to handle real-time API requests and manage data processing efficiently. Our machine learning models for evaluating clothing condition were developed in Python using Sklearn, and deployed using ONNX for optimal performance and fast inference times. We integrated the OpenAI API to provide intelligent recommendations based on the condition of the clothing, and store all user data and assessments in MongoDB. We chose Vercel for seamless, scalable deployment, and version control is handled through GitHub.

In addition to this, we built an alternate version of EcoCloset fully on Streamlit, which uses Python from end-to-end. This version focuses on simplicity and rapid prototyping. It allows users to upload clothing images directly into the Streamlit app, where a pre-trained machine learning model—developed in Sklearn—assesses the item’s resale or donation value. We chose Streamlit for its ability to quickly spin up interactive web apps and deploy Python models directly, giving us the flexibility to iterate on features fast. This alternative version runs entirely in Python, allowing for easier modifications for future development, especially in ML-heavy scenarios.

By having two versions—one built with modern web technologies like React and one with Streamlit for rapid experimentation—we’re able to adapt the app to various user requirements and deployment contexts.

Challenges we ran into

We faced several significant challenges while developing EcoCloset. One of the biggest obstacles was setting up cron jobs for scheduled tasks like database updates and notifications. We spent a considerable amount of time troubleshooting this issue, as managing time-sensitive tasks in our system was more complex than expected.

Another major hurdle was training our machine learning model. We lacked access to GPUs and had only about 2,000 images available, which wasn’t enough for the scale of model training we originally envisioned. This forced us to look for pre-trained models that could be adapted to our needs, but finding a model that fit our exact specifications was difficult. Eventually, we had to adjust our approach and leverage ONNX to make deployment smoother.

We also encountered problems with Streamlit, our initial framework choice for a simple and quick MVP. Streamlit doesn’t have built-in support for authentication and user logins, which was crucial for our app. This limitation forced us to switch to a Next.js setup for our main version, where we could implement more complex functionality like authentication and user management. Streamlit, though great for rapid prototyping, also had a few other limitations in terms of UI flexibility and scalability, which made us rethink our framework choices.

Integrating the backend with the frontend came with its own set of difficulties. Processing the AI responses from the Flask API into a user-friendly format on the frontend was challenging, and it took several iterations to get the communication between the backend and frontend smooth and efficient.

Additionally, we had a steep learning curve with Figma as we worked on the design of our user interface. While Figma is a powerful tool, understanding how to create responsive and intuitive designs that work well on different platforms took time and effort.

These challenges taught us the importance of flexibility in choosing technologies and helped us improve our problem-solving skills across the stack.

Accomplishments that we're proud of

We’re incredibly proud of the fact that we built our own machine learning model to analyze clothing conditions rather than taking the easy route and relying solely on pre-built APIs or just wrapping ChatGPT for this task. By developing our own model, we gained far more control over the results and were able to tailor the solution to our specific problem, which gives EcoCloset a unique edge. This decision also allowed us to better understand the complexities of evaluating clothing for resale, donation, or recycling.

Another accomplishment we’re excited about is the development of two fully functional versions of EcoCloset: one built using Next.js for a more robust user experience and another using Streamlit for rapid prototyping and experimentation. Having both versions gives us flexibility and allows us to adapt the solution to different user needs or project requirements.

But perhaps our biggest point of pride is that we are solving a real-world sustainability problem that hasn't been addressed before at this scale. By creating a platform that helps reduce clothing waste and promotes eco-friendly practices, we’re tackling an issue with significant environmental impact. The fact that we’re contributing to a solution that encourages more sustainable fashion consumption and waste management is something we’re extremely proud of.

What we learned

One of the biggest takeaways from this project is that cron jobs are a pain in the bum! Setting up scheduled tasks turned out to be more challenging than expected, and we quickly learned how important it is to have a solid understanding of scheduling tasks for backend operations.

We also learned that training a machine learning model from scratch is no easy feat, especially when you don’t have access to GPUs or a large dataset. Finding a pre-trained model or dataset that fits your needs can also be a huge challenge. It was eye-opening to realize how much work goes into developing models for niche applications like ours, and this experience has deepened our respect for those who specialize in AI and machine learning.

What's next for EcoCloset

Meta RayBans Intergration for easy acess without pulling out your phone (Kinda works but not fully as no SDK)

Looking ahead, there’s a lot of potential for EcoCloset to grow and make an even greater impact. One key goal is expanding our international outreach. We want to bring EcoCloset’s sustainable mission to a global audience, ensuring that users from all over the world can engage with the platform and reduce their fashion waste. We also plan to enhance the app’s diversity aspect by expanding support for more cultural clothing donations and promoting inclusivity in fashion.

Partnering with more companies that prioritize sustainability is a huge next step for us. We aim to promote eco-friendly products and offer users more coupons and rewards for making sustainable choices. We’re also committed to providing better value for users by constantly refining the platform’s features and incentives.

In terms of technology, we’re focused on training a better, more accurate model that can provide users with precise estimates of what they can get for their clothing. The goal is to ensure that if the app quotes a resale value, users can walk into a store and reliably receive that amount. This will strengthen the app’s trustworthiness and provide real-world benefits to users.

Adobe Express Add-On for ECommerce+Marketing. At EcoCloset, our goal is to create a vibrant e-commerce marketplace and community for thrifted and gently used clothing. This will allow users to appraise, sell, showcase, and share their sustainable fashion items within a connected community.

We plan to integrate Adobe Express as an add-on, enabling users to create e-commerce content directly from their uploaded clothing photos. After receiving an item analysis in the EcoCloset app, users can seamlessly export their images to Adobe Express to design custom marketing materials such as ads, social media posts, or product listings. Using stickers, icons, data visualization, and pre-built templates, users can create engaging, professional content to promote their items.

As part of this hackathon theme we have plans to develop a prototype add-on for Adobe Express. This would allow users to generate e-commerce assets with just a few clicks, featuring customizable templates, branding options, and tools for sustainable fashion marketing.

Through this integration, EcoCloset will foster a community-driven marketplace, where users can buy, sell, and showcase their thrifted fashion items, promoting both sustainability and creativity.

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