FoodGAI

Discover quick, easy recipes and meal plans tailored for anyone who just wants to save time and eat by simplifying meal decisions and cooking dilemmas in the kitchen.

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Inspiration

I moved to the US for my Master's with zero cooking skills. Despite managing to learn a bit along the way, the real problem arose when I found myself stuck, wondering what to cook or what to eat. This dilemma became a recurring challenge, especially amidst the hectic schedule of academic and then work life. It was during one of these moments of culinary confusion that the idea for my application began to take shape. I realized that I couldn't be the only one facing this predicament and that there had to be a better way to navigate the world of meal planning and cooking. This realization fueled my determination to develop FoodGAI (Food Generative AI), an application that would leverage AI to generate personalized meal plans and craft custom recipes based on available groceries. Thus, the journey of creating a solution to simplify and streamline the process of meal preparation began.

What it does

FoodGAI not only provides a complete meal plan tailor-made for the user by just answering a few simple questions but also helps the user decide what meal can be made out of the ingredients that are available! Just upload a picture of the ingredients, and FoodGAI will suggest what you can make in just a few seconds. No more ordering in fast food just because it was too much of a task to figure out what to cook or even how to cook it! FoodGAI is a one-stop solution, born from a desire to help not just myself but anyone eat healthier in the easiest way possible.

How I built it

Building this web application involved a combination of Python and Flask, along with the integration of two powerful language models: Gemini 1.0 and 1.5. Leveraging Flask, a lightweight and versatile web framework, provided a solid foundation for developing the backend logic and handling HTTP requests. These large language models enabled advanced natural language processing capabilities, allowing the application to generate personalized meal plans and craft custom recipes based on user inputs and available ingredients

Challenges I ran into

Starting with limited experience in Python, primarily specializing in Java and C#, presented its own unique set of challenges during the development of this application. Finally, when the backend was completed, I realized it had been ages since I had done any UI work. This made it challenging to identify the source of issues each time a problem arose, adding an additional layer of complexity to the development process.

Accomplishments that I'm proud of

Working with Gemini to make this project possible was fun and interesting, however like with any other project there will always be some hiccups along the way. I specialize in everything about Back-end development from API development to Infra - CI/CD and development, and taking on this project single-handedly from scratch was definitely a learning experience. Having to take up multiple roles and responsibilities to complete the entire project and put forth a finished application is something that I am very proud of.

What I learned

Through developing FoodGAI, I learned valuable lessons in project management, back-end development, and problem-solving. Gave me a taste of what Gemini models can do now and probably more in future. It was an enriching experience that allowed me to grow both personally and professionally.

What's next for FoodGAI

In the future, I plan to further enhance FoodGAI by incorporating more advanced algorithms for meal planning and recipe suggestions. I also aim to expand its capabilities to cater to a broader audience and provide even more personalized recommendations for healthier eating habits. Add customer user accounts , newsletters, etc

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