HootHive — Lecture Summarizer, Chapter Notes & Video Q&A

Upload and manage your lecture recordings with AI-powered chapter generation, study notes, and Q&A — powered by TwelveLabs AI.

📌 About

HootHive is an AI-powered learning assistant designed for students.
Instead of relying on YouTube links, students can upload their own lecture recordings (in bulk), and HootHive will automatically:

• Generate chapter timestamps for each lecture
• Create summaries and structured notes
• Build study packs (flashcards, quizzes, and PDFs)
• Provide a Q&A interface to quickly find relevant lecture segments
• Share lecture notes, timestamps, and PDFs directly in Discord with HootBot

Perfect for students who need to manage a large library of lectures, HootHive turns raw video files into organized, searchable, and shareable study material.

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✨ Key Features

🎬 Core Lecture Processing

• Bulk Lecture Uploads: Upload multiple lecture files at once (MP4, MOV, MKV, etc.)
• Automatic Chapter Generation: AI detects logical breaks (topics, sections) in each lecture
• Lecture Management Dashboard: View, search, and organize all uploaded lectures
• Video Segmentation: Export specific sections as downloadable clips

📚 Learning & Study Material Creation

• Lecture Summaries: Short, medium, and detailed summaries for each lecture
• Chapterwise Notes: Auto-generated notes per topic/section
• Highlight Extraction: Capture the most important insights and examples
• Study Pack Builder:
  • Flashcards (Q/A style)
  • Quizzes (MCQs and short-answer)
  • Topic-based study guides
• PDF Export: Bundle notes, summaries, flashcards, and quizzes into downloadable PDFs

🤖 Lecture Q&A Explorer

• Natural Language Questions: e.g., “Where does the professor explain recursion?”
• Relevant Segment Retrieval: Exact timestamps and snippets returned from your lectures
• Confidence Scoring: Results ranked by accuracy (0–100%)
• Auto-Snippets: Convert retrieved lecture parts into mini-clips
• Multi-modal Search: Works across audio (speech), visuals (slides), and transcripts

🐦 Discord Collaboration (HootBot)

• HootBot Integration: Connects HootHive with your study group’s Discord server
• Lecture Sharing: Post summaries, PDFs, and flashcards directly to a channel
• Group Study Mode: Collaborate on shared notes and highlight important parts together
• Slash Commands: e.g., /hoothive search <lecture> <query> to quickly retrieve answers

⚡ Real-time Features

• Bulk Processing Queue: Upload multiple lectures and track their status
• Live Progress Bars: See upload and analysis progress for each lecture
• Instant Q&A: Low-latency answers to your questions

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🛠️ Tech Stack

• Frontend: Streamlit
• AI / Multimodal: TwelveLabs AI Platform
• Video Processing: MoviePy
• Backend: Python (FastAPI optional for batch APIs)
• Config: dotenv for environment variables
• Integrations: Discord.py (HootBot), MongoDB Atlas (lecture metadata & indexing), AWS/GCP storage

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🚀 Quick Setup

1. Prerequisites

• Python 3.8+
• TwelveLabs account + API key
• Git
• (Optional) Discord bot token for HootBot
• (Optional) MongoDB Atlas for persistent lecture storage

2. Installation

``

Clone repository

git clone https://github.com/notzabir/Hack_Rice.git

cd HootHive-Lecture-QA

Install dependencies

pip install -r requirements.txt

HootHive Demo -

https://www.youtube.com/watch?v=aJLemxw8aEg

Features

🎯 Chapter Generation: Automatically detect and create timestamps with highlights for video chapters.

🔍 Content Segmentation: Identify key points in the video based on its content.

🚀 Streamlined Navigation: Enhance the viewing experience with clickable chapters for easier navigation.

Tech Stack

• Frontend: Streamlit
• Backend: Python
• Deployment: Streamlit Cloud

Instructions on running project locally

To run HootHive locally, follow these steps -

Step 1 - Clone the project

git clone https://github.com/notzabir/Hack_Rice.git

Step 2 -

Install dependencies:

 cd Hack_Rice

 pip install -r requirements.txt

Step 3 -

Set up your Twelve Labs account -

Create an account on the Twelve Labs Portal Navigate to the Twelve Labs Playground Create a new index, select Marengo 2.6 and Pegasus 1.1

Step 4 -

Get your API Key from the Twelve Labs Dashboard Find your INDEX_ID in the URL of your created index

Step 5 -

Configure the application with your API credentials:

1. Copy the .env.example file to .env:

cp .env.example .env

2. Edit the .env file and add your credentials:

API_KEY=your_twelvelabs_api_key_here
INDEX_ID=your_index_id_here

Important: Never commit your .env file to version control. It's already included in .gitignore.

Step 6 -

Run the Streamlit application

  streamlit run app.py

Step 7 -

Access the application at:

  http://localhost:8501/

License