An offline AI-powered video analysis tool with object detection (YOLO), image captioning (BLIP), speech transcription (Whisper), audio event detection (PANNs), and AI-generated summaries (LLMs via Ollama). It ensures privacy and offline use with a user-friendly GUI.

The YouTube Video Transcribe & Summarizer LLM app uses Google Gemini Pro to transcribe and summarize YouTube videos, providing concise, accurate content insights. It leverages advanced language models for real-time video processing and summarization.

This package would process text input, such as a research paper title or abstract snippet, and generate a structured summary of the core idea or problem addressed. It uses an LLM to interpret the inpu

A new package would process text inputs describing unconventional or controversial business or social initiatives, such as the 2012 program where homeless individuals were employed as mobile Wi-Fi hot

text2structured-summary generates structured summaries from unstructured text using an LLM.

A modern web app that transcribes meeting audio and generates intelligent, actionable summaries using state-of-the-art AI.

Multimodal RAPTOR for Disaster Documents using ColVBERT & BLIP. Hierarchical retrieval system over 46 tsunami-related PDFs (2378 pages), combining BLIP-based image captioning, ColVBERT embeddings, and GPT-OSS-20b long-context summarization. Optimized for fast multimodal tree construction and disaster knowledge preservation.

📄 Extract clear summaries of unconventional initiatives' core problems using a compact utility that simplifies complex ideas.

An advanced RAG (Retrieval-Augmented Generation) system using RAPTOR algorithm to hierarchically organize and retrieve lessons from the 2011 Great East Japan Earthquake and Tsunami for educational purposes.

📝 Generate structured summaries from unstructured text effortlessly with an LLM for enhanced clarity and organization.