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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.

by read1 min views1 publishedOct 25, 2026

*This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady* GroupChatDigest β€” 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 `python

import streamlit as st

from groq import Groq

st.title("πŸ’¬ 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.

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