https://github.com/raahulcodez/hippo
https://github.com/raahulcodez/hippo
Last synced: about 1 year ago
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- Host: GitHub
- URL: https://github.com/raahulcodez/hippo
- Owner: raahulcodez
- Created: 2025-02-24T09:57:43.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-24T12:09:22.000Z (over 1 year ago)
- Last Synced: 2025-05-11T02:07:01.977Z (about 1 year ago)
- Language: Python
- Size: 475 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# HIPPO: AI-Powered Interactive Learning & Lab Assistant
### Bridging Knowledge with Intelligent AI
## 📌 Overview
HIPPO is an AI-driven **learning engagement platform** that leverages **real-time object detection, knowledge graphs, and AI-generated tutorials** to provide **context-aware guidance**. Unlike traditional object recognition models, HIPPO understands the **real-world intent** behind objects, retrieves relevant knowledge, and generates interactive, step-by-step tutorials for learning and task execution.
## 🚀 Problem Statement
### **The Gap Between Visual Perception and Actionable Knowledge**
Current solutions like Google Lens and WikiHow fail to **connect object recognition with personalized learning**.
- 🔍 **Object detection models** recognize items but **lack contextual understanding**.
- 📚 **Learning resources (YouTube, WikiHow)** require **manual searching** for relevant content.
- ❌ Existing solutions **don’t adapt to user expertise, tools, or real-world scenarios**.
**Example Scenario:**
*A student in a lab needs guidance on handling a chemical reaction but struggles to find instructions specific to their available equipment. HIPPO solves this by detecting lab equipment, retrieving a structured tutorial, and guiding them step by step.*
## 🛠️ Proposed Solution
HIPPO **analyzes images/videos**, identifies objects, determines intent, retrieves knowledge, and generates personalized tutorials. It operates in **two modes**:
### **1️⃣ Photo Mode (Static Object Analysis)**
- Users capture an image of an object/scene (e.g., a **disassembled circuit board**).
- **LLaVA (Vision-Language Model)** detects objects & extracts contextual metadata.
- **Neo4J (Graph Database)** stores object relationships and infers task intent.
- **RAG (Retrieval-Augmented Generation)** fetches verified tutorials.
- AI generates **interactive, step-by-step guidance** tailored to the user's need.
### **2️⃣ Video Mode (Real-Time Scene Understanding)**
- Users record/upload a video of an ongoing task (e.g., **assembling a 3D printer**).
- **AI tracks object interactions over time** and identifies the workflow (e.g., “Screwdriver tightening a bolt”).
- A **temporal reasoning module** maps sequential object movements to detect multi-step tasks.
- **Real-time instructions overlay** onto the video feed, guiding users dynamically.
---
## 🔧 Core Workflow
HIPPO follows a **structured AI pipeline**:
1️⃣ **Input Capture:** Users upload an image/video via a **mobile/web interface**.
2️⃣ **Object & Context Analysis:** LLaVA + YOLO detect objects, AI infers the task.
3️⃣ **Graph Storage (Neo4J):** Objects and relationships stored for **context-aware retrieval**.
4️⃣ **Knowledge Retrieval (RAG):** Fetches **relevant task-specific guides** from WikiHow, research papers, and forums.
5️⃣ **Guidance Generation:** Users receive **interactive AI-driven tutorials** with real-time updates.
---
## 🏆 Why HIPPO is Innovative
🚀 **Graph-Based Context Awareness** → Objects **aren’t isolated**; relationships define intent.
🎥 **Temporal Scene Analysis** → Detects **object interactions over time** for real-time assistance.
🔍 **RAG-Powered Knowledge Retrieval** → **No hallucinations**, only verified knowledge.
📲 **Adaptive & Interactive Guidance** → **Real-time tutorials tailored to user expertise.**
---
## 🏗️ Tech Stack
- **AI Models:** LLaVA (Vision-Language Model), YOLO (Object Detection), GPT-4/Llama-3 (Content Generation)
- **Database:** Neo4J for **knowledge graphs & relationships**
- **Backend:** Python, FastAPI
- **Frontend:** Streamlit/Gradio UI
- **Deployment:** Cloud-based + Edge AI for low-latency inference
---
## 🎯 Key Use Cases
🔬 **STEM & Lab Environments** → Real-time guidance for chemistry, engineering, and robotics experiments.
🛠️ **DIY & Home Repairs** → Hands-free **AI-powered assembly instructions**.
🍳 **Cooking & Recipe Assistance** → Step-by-step tutorials based on detected ingredients.
👩🎓 **Education & E-Learning** → AI-assisted training **adapts to student knowledge levels**.
---
## 📖 Research & References
- **Visual Instruction Tuning (LLaVA)** – Liu et al., 2023. [arXiv:2304.08485](https://arxiv.org/abs/2304.08485)
- **Retrieval-Augmented Generation (RAG)** – Lewis et al., 2020. [arXiv:2005.11401](https://arxiv.org/abs/2005.11401)
- **Graph-Based AI Knowledge Representation** – Neo4J AI Use Cases.
- **AI in Education & Learning** – IEEE Research Articles.
---
## 🔗 Future Improvements
✅ **Offline Mode**: On-device AI models for real-time assistance without internet dependency.
✅ **Augmented Reality (AR) Integration**: Overlay AI-generated instructions onto **physical objects**.
✅ **Expanded Knowledge Sources**: Incorporate **scientific papers, patents, and industry reports**.
---
## 📌 Get Started
1. Clone this repository:
```bash
git clone https://github.com/raahulcodez/hippo.git
cd hippo