Bloop is a web application designed to analyze exported WhatsApp chat files, offering insights into conversation dynamics, popular words, user activity, and more. It provides a comprehensive look into your chat history.
Try it live at: bloopit.vercel.app
The GitHub Repos: Backend Frontend
How it is built
The backend of Bloop is built with GoLang using the Gin framework. GoLang was chosen for its speed and lightweight nature. Gin is utilized for its high performance and efficiency in handling a large number of requests, making the server suitable for potentially high-traffic scenarios. The frontend is developed using Next.js, a popular React framework, chosen for its capabilities in building fast, scalable, and SEO-friendly web applications.
Current features
Currently, Bloop accepts an exported .txt and .zip WhatsApp chat files and provides the following analyses and features:
- Identification of most used words
- Identification of most used emojis
- Calculation of the total number of messages
- Estimation of average reply duration
- Identification of most active users
- Detection of conversation starters
- Generation of an interaction matrix (likely showing who talks to whom)
- Creation of a histogram visualizing messages over time
- Generation of a word cloud based on chat content
- AI analysis of the chat data
Quirky Hacks
- The backend is hosted on two places: Render.com and my RaspberryPi. The frontend intelligently distributes requests between them.
- The LLM used is
llama-4-scout-17b-16e-instructwhich is provided by groq.com . The free tier currently gives 1000 requests a day with no token limit per day. So it works fine for a small project.