Faded Flavours

Recipes Rediscovered, Memories Recovered, Timeless Flavors Uncovered

Comment

Inspiration

The rich culture, ethics, and values associated with food have always intrigued me, and the idea of creating a platform to preserve and share lost recipes was born out of this fascination. Moreover, the increasing trend of fast-food culture and the decline of traditional recipes motivated me to create a space where people can explore and discover diverse recipes from different cuisines. The Faded Flavours is not just a recipe-sharing platform; it is a way to preserve the cultural heritage and values associated with food and promote diversity and inclusivity through culinary arts.

What it does

The Faded Flavours is a web platform that allows users to share and discover unique, forgotten, and traditional recipes from different cultures. Users can add their favorite recipes to the platform, complete with ingredients, preparation steps, images, and other details. The platform also features a recipe recommender system that suggests similar recipes based on user input. Additionally, the platform includes a machine learning model that can predict the type of dish based on the ingredients and preparation time. Overall, the platform aims to preserve cultural culinary heritage and encourage food enthusiasts to explore and try new recipes.

How we built it

The Faded Flavours was built using modern web technologies such as Node.js, Express, MongoDB, and React and Swift for App part and Python in machine learning. The frontend of the platform was developed using React, a popular JavaScript library for building user interfaces, and it communicates with the backend API developed using Express, a fast and flexible Node.js web application framework. MongoDB, a NoSQL database, was used to store and manage recipe data, and the platform was deployed to a cloud hosting service for scalability and availability. Machine learning models were developed using Python to provide recipe type predictions. Overall, the platform was developed with a focus on scalability, user experience, and data-driven features.

Challenges we ran into

We faced challenges in bringing the old recipes back together in dataset for the machine learning and had to finetune the model according to Indian Cuisine. We also had issues in aligning the Front end of both Web and App according to the Indian vibe. We faced lot of issues in merging the model with the application.

Accomplishments that we're proud of

We are proud to have successfully developed both the web and app versions of the project. The UI was designed to reflect an Indian aesthetic and culture. We also fine-tuned the machine learning model to suit the Indian dataset, including flavors and ingredients from all over the country. Creating our own dataset for the model was a significant challenge, but we were able to overcome it with teamwork and dedication. Overall, we are thrilled with the results and look forward to further improving and expanding the platform.

What we learned

During the development of The Faded Flavours, our team learned a lot about various aspects of web development, app development, and machine learning. We gained hands-on experience in designing and developing a user-friendly interface that reflects the cultural values of Indian cuisine. We also learned about the intricacies of deploying a machine learning model on a web platform and integrating it with other features seamlessly. Additionally, we learned how to work effectively as a team and collaborate remotely, overcoming challenges such as time zone differences and communication barriers. Overall, the project helped us enhance our technical skills, teamwork, and project management abilities.

What's next for Faded Flavours

In the future, we plan to expand the Faded Flavours to include traditional recipes from other cultures and cuisines. This will involve adding support for new languages and ingredients, as well as creating new machine learning models to handle the additional data. We also plan to incorporate more advanced search and filtering features, such as the ability to filter recipes based on dietary restrictions or cooking techniques.

Another possible automation is to use natural language processing techniques to extract recipe information from text-based sources such as blogs and social media. This will allow us to automatically add new recipes to the platform without manual data entry.

Share this project:

Updates