ShopWise

Inspiration

Students want to make smart, affordable, and ethical purchases—but today that’s nearly impossible. Prices for the same product vary widely across online stores, and information about a brand’s sustainability or labor practices is buried across dozens of websites, reports, and PDFs. This leaves students overpaying and unintentionally supporting unethical brands.

What it does

ShopWise solves this by combining real-time price comparison with ethical and sustainability scoring, giving students a clear view of both the cheapest and the most responsible choice. This empowers students to save money, shop consciously, and drive transparency in the market—making ethical purchasing simple, accessible, and impactful.

How we built it

ShopWise is built on a modular, scalable tech stack. The Python backend performs parallel web scraping on retailers like Amazon, Walmart, Best Buy, and Newegg. It calculates ethical scores and estimates carbon footprint for each product, so students can compare more than just price. We expose this capability through a FastAPI REST API, serving both our interactive Streamlit web UI and a modern React/TypeScript frontend. User profiles and preferences are stored using Neon PostgreSQL for reliable cloud data storage. Claude AI (Anthropic) is integrated for generating personalized recommendations and summaries, making each search student-centric. ShopWise supports multiple access patterns: command line (CLI), web UI, and programmatic REST API—for maximum flexibility and accessibility.

Challenges we ran into

Scraping Reliability: Major retailers like Amazon and Walmart often block scraping bots or change their website structures, breaking simple scripts. Integrating Browser Use helped us render dynamic pages, but required frequent updates to our parsing logic. API Integration Issues: Managing multiple API keys (Anthropic, Galileo, Browser Use) and their secure configuration caused issues, especially when switching between local and cloud environments like Daytona. Sustainability Data Sources: Truly accurate and free datasets for carbon footprint and ethical ratings per product were hard to find. For the MVP, we built heuristics and lookup tables to estimate scores. UI/UX Upgrades: Evolving from a plain results table to an engaging, interactive web interface required experimenting with layouts, sorting, icons, and responsive feedback—all with time constraints.

Accomplishments that we're proud of

Anthropic (Claude): Leveraging Claude's API allows the app to generate clear, personalized summaries, helping students instantly understand their best options. Galileo: Galileo evaluates our AI-generated suggestions for safety, accuracy, and reliability. We send summaries to Galileo and display its quality feedback to users. Daytona: Daytona's cloud-based workspace let us develop, test, and run ShopWise entirely in-browser, making remote teamwork fast and simple without local installs.

What we learned

Team ShopWise gained hands-on experience in parallel web scraping, asynchronous programming, and integrating APIs (Anthropic/Claude, Galileo, Browser Use) into a robust, student-focused tool. We learned the complexities of live retailer data extraction, the challenge and value of sustainability metrics, and the critical role of thoughtful UX for engagement. Daytona also showed us the benefits of cloud collaboration and rapid iteration.

What's next for ShopWise

We're planning to expand ShopWise by: Adding secondhand/rental marketplaces for sustainable, budget-friendly options. Improving sustainability scoring with real-time APIs and broader ethical criteria. Building personalized alerts (e.g., price drop or new green product notifications). Refining onboarding and recommendations for different user types (students, schools, clubs). Exploring deeper AI personalization and deploying mobile-friendly features for global student access.

Built With

• beautiful-soup
• fastapi
• neon
• python
• react
• typescript