Team APES - Rennybot.co

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  System Architecture

Inspiration

We wanted a calorie app that simplifies cooking decision to reduce decision fatigue.

What it does

Suggest recipes via Gemini with profile preferene, Save recipe, count calories, Compare calories consumed with calorie goal

How we built it

We divided and conquered our tech stack to implement our web app

Challenges we ran into

Implementation of chatbot

Accomplishments that we're proud of

Implementation of chatbot with API endpoint connection to Front end

What we learned

How to implement a chatbot using Gradio and Langchain. Usage of Vite+React for front end Database creation and management using postgresSQL

What's next for Team APES

Scaling up of the application and deploying of database on cloud service

Project Write up

Overall this is a nutrition and wellness app that is designed to help users take control of their dietary goals with ease. There are plenty of personalization and insights to aid their journey. These features include from tailored onboardings to AI-powered recipe suggestions.

Tech Stack elaboration

Our webapp uses the front end Vite+React to create the user interface for the user to interact with. (Frontend) From this interaction it calls the API endpoints implemented using FastAPI to use Python's Langchain library to query Google Gemini 2.0 Flash model to obtain the AI's response. (Middleware) In most instances, the python script in both the frontend and middleware will communicate with our PostgresSQL database to create, read, update and delete data (CRUD) (Backend)

Built With

• bootstrap
• css
• express.js
• fastapi
• html
• javascript
• langchain
• node.js
• postgresql
• python
• react
• vite