Inspiration We were inspired by the idea of recommending recipes based on the time of day, such as breakfast recipes for the morning, lunch for noon, and dinner for the evening or night.
What It Does The application recommends recipes by detecting the current time and suggesting suitable meals for that time of day.
How We Built It We developed the project using Streamlit, Jupyter Notebook, and Mistral AI. Utilizing RAG and Cortext Search from Snowflake as the recommender.
Challenges We Faced Since many of the concepts were new to us, we faced challenges in understanding and implementing them effectively.
Accomplishments We’re Proud Of We are proud that the project is complete and functional.
What We Learned We gained valuable insights into Retrieval-Augmented Generation (RAG), Cortex search, and Snowflake.
What’s Next for Recipe Recommender Our future plans include improving the user interface, integrating a real-time database, and adding web scraping capabilities for more dynamic content.
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