"StyleSync" is an innovative solution that bridges the gap between discovering fashion trends on Instagram and purchasing them on Myntra. This way users can stay updated with the latest trends and easily find similar or identical items on Myntra.
- Social Shopping Integration
- Engagement on a Shopping Platform
Almost 67% of GenZ use Instagram to discover trends and get recommendations from influencers. "StyleSync," bridges the gap between simply seeing a trend and buying it. With "StyleSync," one can stay on top of the latest trends and easily locate them to purchase.
It is all about checking out the latest outfits from popular influencers on Instagram and turning your feed into fashion!
We start by scraping Instagram feeds of popular fashion influencers to identify trending fashion items. This is achieved using instascrapper.ipynb Jupyter notebook, which leverages Instagram scraping tools to extract images from the profiles.
The scraped images are then saved locally for further processing. These images contain the outfits and fashion items worn by influencers.
The Instagram data scraping generates three output files:
profile.txt: Contains raw scraped data.profiles.csv: Contains profile information of influencers.datetime.csv: Contains timestamps of the scraping process.
Next, we use a TensorFlow model to perform feature extraction on the saved images. This model identifies key fashion items and attributes from the images, such as types of clothing, patterns, and colors.
The extracted features are then integrated with our API, which is powered by server.js. This API handles requests to match the identified fashion items with similar or identical products available on Myntra.
Finally, when a user queries the system, the API retrieves detailed product information from Myntra, allowing users to easily find and purchase the items they saw on Instagram.
- Python
- Jupyter Notebook
- TensorFlow
- Node.js
- Selenium
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent