Seasonal Recipe Collection

Our website curates a collection of recipes that feature the freshest ingredients available in your area, tailored to the current month.

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Inspiration

Seasonal Recipe Collection was created to make eating both enjoyable and eco-friendly. By choosing recipes that use ingredients in season, we get the freshest and most flavorful food while also cutting down on the environmental impact of transporting produce.

What it does

  1. Find Your Location: We start by getting your current location to understand what’s locally available.
  2. Seasonal Ingredients: We identify which fruits, vegetables, and herbs are in season for the current month in your area.
  3. Recipe Recommendations: We provide a list of recipes that feature these seasonal ingredients. Each recipe comes with a detailed list of all required ingredients and step-by-step instructions.

How we built it

The user interface is developed using React and JavaScript. The back-end of the application is powered by Python and Flask. For an optimized and efficient development experience, we use Vite.

Challenges we ran into

One of the major hurdles was the absence of a dedicated API or database that provides seasonal ingredient information based on location and month. We had to build our own solution from scratch to meet this need. To gather data on seasonal ingredients, we turned to web scraping. However, the website we needed to scrape contained thousands of pages, making it impractical to process them one by one due to the sheer volume of data. Scraping data from such a large site sequentially would have been extremely slow. To address this, we implemented concurrency techniques to scrape up to 500 pages simultaneously.

Accomplishments that we're proud of

As this was our team’s first hackathon, we’re incredibly proud of what we’ve achieved. We successfully built a fully functional application from scratch. Throughout the project, each team member had to learninto new technologies and concepts. From mastering React and JavaScript for the front-end to navigating Python and Flask for the back-end.

What we learned

  1. We gained hands-on experience with React’s component-based architecture, state management, and building interactive user interfaces.
  2. We learned techniques for efficiently extracting data from websites and implemented concurrency to handle large-scale data collection.
  3. We learned to build and manage a back-end server, handle HTTP requests, and integrate Flask with our front-end application.
  4. We explored basic computer vision concepts, expanding our understanding of visual data analysis.

What's next for Seasonal Recipe Collection

  1. We plan to develop a feature where users can upload an image, and the system will identify ingredients in their fridge using computer vision. This will enhance the personalization of recipe suggestions and make meal planning even more convenient.
  2. We aim to build a tool that scrapes existing recipe websites to extract and display only the essential information needed to cook a dish. This will streamline recipe content, making it more accessible and user-friendly.
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