GroupChatDigest

A developer built GroupChatDigest, an open-source Streamlit web app that summarizes exported group chat logs into a three-part digest covering key decisions, action items, and a funniest highlight. The tool calls Groq's API with a choice of llama-3.3-70b-versatile or llama-3.1-8b-instant models, using a system prompt that enforces the three section headers. The developer claims it cuts 15 to 20 minutes of daily manual scrolling through hundreds of messages down to a roughly 5-second scan.

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady https://dev.to/challenges/hacktoberfest-week3-2026-10-19 GroupChatDigest — a cloud-hosted, open-weight AI web application that distills overwhelming daily group chat exports and message dumps into a concise, actionable summary. Instead of spending 20 minutes catching up on hundreds of unread messages across active Discord servers, WhatsApp groups, or Telegram chats, GroupChatDigest extracts the signal from the noise and delivers a 3-part digest: It allows users to skip the FOMO and doom-scrolling, saving energy and time every single day. https://github.com/Sangam067/DEVWEEK3.git https://github.com/Sangam067/DEVWEEK3.git python import streamlit as st from groq import Groq st.title "💬 GroupChatDigest" st.caption "Turn hours of unread group chat clutter into a 3-bullet morning digest." api key = st.sidebar.text input "Groq API Key", type="password" model choice = st.sidebar.selectbox "Select Model", "llama-3.3-70b-versatile", "llama-3.1-8b-instant" chat log = st.text area "Paste daily chat log or export here:", height=300 if st.button "⚡ Summarize Chat" : if not api key: st.error "Please enter a valid Groq API key in the sidebar." elif not chat log.strip : st.warning "Please paste some text first " else: try: client = Groq api key=api key response = client.chat.completions.create model=model choice, messages= {"role": "system", "content": "You are a concise assistant. Output exactly 3 section headers: 1. Key Decisions / News, 2. Action Items, 3. Funniest Highlight / Meme."}, {"role": "user", "content": chat log} st.markdown response.choices 0 .message.content except Exception as e: st.error f"Error: {str e }" streamlit for a fast, responsive user interface. llama-3.3-70b-versatile open-weight model accessed via the app.py , accepting large text contexts and processing structured responses instantly. Catching up on 500+ messages across three different group chats normally takes 15 to 20 minutes of daily manual scrolling. GroupChatDigest condenses 20 minutes of reading into a 5-second scan, letting you get straight to the important points.