Nectar

A versatile solution for comparing product prices, conducting pricing research, and identifying similarities to enhance the shopping experience.

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Inspiration

Our inspiration for creating a comparison website for e-commerce products is to empower consumers with the ability to make well-informed purchasing decisions. We aim to simplify the complex process of product selection by providing transparent and comprehensive comparisons, helping users save time and money while shopping online.

What it does

The user can input a link on the Buying page, and our program will look for other similar products on Amazon and show the user some alternatives and display prices.

Alternatively, the user can provide a short description of what they are looking to sell, and we can find products related to the prompt through Amazon, and show the average price, standard deviation, and volatility of the products.

How we built it

  • Frameworks/Libraries: Flask, Tailwindcss, BeautifulSoup4, jQuery
  • Environment: Venv, Node.js
  • Languages: Python, JavaScript, HTML/CSS

Challenges we ran into

Early on in the hackathon, we struggled with implementing tailwind.css into our front end. This prevented the CSS portion of our website from running. Although the fix was something minimal, it was difficult to find the reason behind this.

A challenge we faced throughout the hackathon was being rated limited when running our program. We were able to resolve this issue by changing the user agent.

We struggled with being able to connect our front end with our back end.

Accomplishments that we're proud of

We successfully got the scraper to work and were able to connect the front end with the back end. Successfully used tailwind CSS and implementing the utility classes into the HTML and making it work.

What we learned

learned:

  • tailwindcss
  • Flask
  • beautifulsoup
  • git
  • node.js
  • webscraping
  • legal issues

What's next for Nectar

In the future, we want to be able to hold a database (using Google Cloud) of websites. This would prove as a way to include sponsored companies or websites, or a catered dataset of websites for specific key tags.

Another functionality we want to implement is a compare two items, and see which one the user should buy based on specific parameters from the webscrape call.

We also would like to properly display the statistical modeling simulation for both our buy and sell, and use a bigger dataset upon further optimization of algorithms.

Finally, we look forward to being able to find products related to a user's prompt on the Selling page, to allow for a more in-depth response when looking for prices to sell a product at.

Make the webpage look more appealing.

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