GroupChatDigest

*This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady*

What I Built

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:

1. Key Decisions & News: Core updates so you do not have to read through endless debates.
2. Action Items & Tasks: Direct mentions, follow-ups, and assignments.
3. Funniest Highlight: The best joke, meme, or banter of the day so you do not miss out on the group culture.

It allows users to skip the FOMO and doom-scrolling, saving energy and time every single day.

Demo

• Live App: `

Code

https://github.com/Sangam067/DEVWEEK3.git
`python

app.py snippet

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)}")

`

How I Built It

• Frontend: Streamlit (streamlit) for a fast, responsive user interface.
• AI Model: llama-3.3-70b-versatile open-weight model accessed via the Groq API for sub-second cloud inference.
• Architecture: Built in single-file Python (app.py), accepting large text contexts and processing structured responses instantly.

Why Does Open Innovation Matter?

1. Unbeatable Cloud Economics: Processing thousands of tokens of chat chatter daily across multiple groups using proprietary closed APIs can accumulate high token costs over time. Hosted open-weight models via high-throughput inference engines like Groq cost fractions of a cent per request.
2. Freedom From Lock-in: Because the app is structured around open standards, the underlying LLM can be swapped anytime—from cloud-hosted Llama models on Groq to local models running via Ollama—without changing frontend code or business logic.
3. Large-Context Capacity on Modest Hardware: Running a 70B parameter open-weight model locally on a standard 8GB RAM laptop is impossible. Leveraging cloud-hosted open models grants access to massive context windows and high accuracy without hardware strain.

The Lazy Payoff

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.