Cooking can often be challenging due to factors such as limited cooking skills, lack of time, confusion on what to cook or exhaustion after work. Frequently ordering meals or relying on tiffin services can be expensive and may not always align with personal taste preferences. To address this issue, Chef de Cuisine will serve as an intelligent cooking assistant designed for users of all skill levels, from beginners to experts. This software will provide personalized recipe recommendations based on factors like available ingredients, dietary preferences, and time constraints. After understanding user requirements, the system will offer step-by-step cooking guidance along with instructional videos from professional chefs. By ensuring efficient meal preparation, reducing costs, and promoting home-cooked meals, Chef de Cuisine will make cooking accessible, convenient, and enjoyable.
Name: Garati
Student number: 49555835
Chef de Cuisine, an intelligent cooking assistant will be designed to help users prepare meals effortlessly. It will offer the following key features:
1. User Authentication – Secure login and signup feature, allowing users to manage their profiles.
2. Smart Filtering – Users can refine their search by applying various filters to find recipes that best suit their needs. These include:
• Availability of ingredients – Suggests recipes based on what users have on hand.
• Dietary Preferences – Options include vegetarian, non-vegetarian and vegan.
• Time Constraints – Recommends recipes that fit within the user’s available cooking time.
• Spice Level – Allows users to choose recipes based on their spice tolerance.
• Cuisine Type – Enables the users to explore different cuisines worldwide.
3. Personalized Recommendations and Suggestions – Based on the selected filters, the system will provide a curated list of recipes in the form of certified videos from professional chefs or experienced home cooks, along with detailed transcripts. Additionally, the system will suggest any additional ingredients needed for a chosen recipe.
4. Browsing - Users can directly search for a specific recipe they want to cook. This allows them to quickly navigate to the recipe page without having to go through multiple filters or suggestions.
5. Nutritional Insights - Nutritional value of each recipe will also be specified such as carbs, calories, fat, etc.
6. Profile Management – Users can personalize their experience by managing their profiles. They can save their favorite recipes in a dedicated folder/list for easy access and future reference. Furthermore, they can add comments to their saved recipes to note any mistakes or improvements from previous attempts, helping them refine their cooking skills over time.
Technical Aspects
As it is a recommendation system, AI/ML (Matrix Factorization, Deep Neural Network Models) will be highly useful here and API functionality can be used to retrieve data.
The Minimum Viable Product must provide the following features:
1. User Authentication – Secure login and signup feature, allowing users to manage their profiles.
2. Smart Filtering – Users can refine their search by applying various filters to find recipes that best suit their needs. These include:
• Availability of ingredients – Suggests recipes based on what users have on hand.
• Time Constraints – Recommends recipes that fit within the user’s available cooking time.
• Spice Level – Allows users to choose recipes based on their spice tolerance.
3. Personalized Recommendations and Suggestions – Based on the selected filters, the system will provide a curated list of recipes in the form of certified videos from professional chefs or experienced home cooks, along with detailed transcripts.
4. Browsing - Users can directly search for a specific recipe that they want to cook. This will allow them to quickly navigate to the recipe page without having to go through multiple filters or suggestions.
5. Profile Management – Users can personalize their experience by managing their profiles. They can save their favorite recipes in a dedicated folder/list for easy access and future reference.
The software system aims to provide a seamless, reliable and secure user experience.
Availability
The software will be accessible at all times, from any platform, including web and mobile applications.
It will ensure high availability and smooth functioning even during peak usage.
Deployability
It will be deployed on a cloud platform to enhance user experience, scalability and ease of updates. Also, this will prevent the loss of any data as the application will be running on the servers provide by the cloud platform (like AWS).
Continuous integration and deployment are one of the important aspects for the extended lifespan of a software system, that will allow for quick bug fixes and the release of new features.
Extensibility
The system will support the addition of new recipes, updated instructional videos and future enhancements such as diet tracking, exploration of different cuisines, feature for adding comments on previously viewed content and providing nutritional insights.
Maintainability
The system will require minimum code changes for updates (code reusability) and a well written documentation for improved system management.
Modularity
The core functionalities such as smart filtering, personalized recommendations and suggestions, browsing, will be created as separate components so that each component can be managed independently. This will allow individual bug-fixes and updates to each component without affecting other components or the entire system.
Reliability
The software will consistently deliver its functionality without failure.
Only verified videos from authorized home cooks and professional chefs will be provided, eliminating fake viral recipes.
For instance, if vegetarian meal recipes are requested, the system will ensure that only related recipes are released, excluding any non-vegetarian meal recipes.
Scalability
The system will efficiently handle varying user requests, especially during peak hours.
Cloud deployment will enable automatic scaling to accommodate different load conditions while ensuring smooth operation.
Security
User profile will be protected, and the unauthorized or offensive content will be filtered out. The system will ensure that only high-quality and relevant content is available.
Testibility
The system will have the capability of going through rigorous testing to ensure full functionality and reliability.
Among these quality attributes, availability, extensibility, maintainability, reliability and scalability are the most crucial for the proposed system.
Availability
As the deployment is on a cloud platform, cloud monitoring tools can be used to track the response time under varying loads, uptime and downtime of the system.
The application will be rigorously tested by providing lot many requests to check the load handling capacity during peak hours.
Deployability
Adding new features or updating the existing code should reflect the changes in the software with immediate effect without any delays.
Extensibility
The software built should allow code reusability and the new release should function well without any failures.
Maintainability
Bugs should be resolved in minimum possible time along with regular updates to documentation. Even after years of development, it should function as efficiently as when it was first built.
Reliability
Keep testing the recommendation system by constantly changing the applied filters. The new recommendations should be visible within a fraction of second.
Scalability
Perform tests for load handling capacity and auto scaling.
Security
The user profile should be well maintained. We can test this by creating a profile and then saving the videos to the favorites folder and whether we are able to access those videos later.
Testibility
Unit testing - Testing of individual features.
Integration testing – Verifying that the system functions well as a whole entity and we are able to switch filters easily.