{"id":28024341,"url":"https://github.com/raahulcodez/hippo","last_synced_at":"2025-05-11T02:07:06.118Z","repository":{"id":279199885,"uuid":"938026252","full_name":"raahulcodez/hippo","owner":"raahulcodez","description":null,"archived":false,"fork":false,"pushed_at":"2025-02-24T12:09:22.000Z","size":486,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-05-11T02:07:01.977Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/raahulcodez.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-02-24T09:57:43.000Z","updated_at":"2025-02-24T12:09:25.000Z","dependencies_parsed_at":"2025-02-24T10:50:15.728Z","dependency_job_id":null,"html_url":"https://github.com/raahulcodez/hippo","commit_stats":null,"previous_names":["raahulcodez/hippo"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/raahulcodez%2Fhippo","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/raahulcodez%2Fhippo/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/raahulcodez%2Fhippo/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/raahulcodez%2Fhippo/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/raahulcodez","download_url":"https://codeload.github.com/raahulcodez/hippo/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253505652,"owners_count":21918941,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-05-11T02:07:05.561Z","updated_at":"2025-05-11T02:07:06.098Z","avatar_url":"https://github.com/raahulcodez.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# HIPPO: AI-Powered Interactive Learning \u0026 Lab Assistant  \n### Bridging Knowledge with Intelligent AI  \n\n## 📌 Overview  \nHIPPO 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.  \n\n## 🚀 Problem Statement  \n### **The Gap Between Visual Perception and Actionable Knowledge**  \nCurrent solutions like Google Lens and WikiHow fail to **connect object recognition with personalized learning**.  \n- 🔍 **Object detection models** recognize items but **lack contextual understanding**.  \n- 📚 **Learning resources (YouTube, WikiHow)** require **manual searching** for relevant content.  \n- ❌ Existing solutions **don’t adapt to user expertise, tools, or real-world scenarios**.  \n\n**Example Scenario:**  \n*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.*  \n\n## 🛠️ Proposed Solution  \nHIPPO **analyzes images/videos**, identifies objects, determines intent, retrieves knowledge, and generates personalized tutorials. It operates in **two modes**:  \n\n### **1️⃣ Photo Mode (Static Object Analysis)**  \n- Users capture an image of an object/scene (e.g., a **disassembled circuit board**).  \n- **LLaVA (Vision-Language Model)** detects objects \u0026 extracts contextual metadata.  \n- **Neo4J (Graph Database)** stores object relationships and infers task intent.  \n- **RAG (Retrieval-Augmented Generation)** fetches verified tutorials.  \n- AI generates **interactive, step-by-step guidance** tailored to the user's need.  \n\n### **2️⃣ Video Mode (Real-Time Scene Understanding)**  \n- Users record/upload a video of an ongoing task (e.g., **assembling a 3D printer**).  \n- **AI tracks object interactions over time** and identifies the workflow (e.g., “Screwdriver tightening a bolt”).  \n- A **temporal reasoning module** maps sequential object movements to detect multi-step tasks.  \n- **Real-time instructions overlay** onto the video feed, guiding users dynamically.  \n\n---\n\n## 🔧 Core Workflow  \nHIPPO follows a **structured AI pipeline**:  \n\n1️⃣ **Input Capture:** Users upload an image/video via a **mobile/web interface**.  \n2️⃣ **Object \u0026 Context Analysis:** LLaVA + YOLO detect objects, AI infers the task.  \n3️⃣ **Graph Storage (Neo4J):** Objects and relationships stored for **context-aware retrieval**.  \n4️⃣ **Knowledge Retrieval (RAG):** Fetches **relevant task-specific guides** from WikiHow, research papers, and forums.  \n5️⃣ **Guidance Generation:** Users receive **interactive AI-driven tutorials** with real-time updates.  \n\n---\n\n## 🏆 Why HIPPO is Innovative  \n🚀 **Graph-Based Context Awareness** → Objects **aren’t isolated**; relationships define intent.  \n🎥 **Temporal Scene Analysis** → Detects **object interactions over time** for real-time assistance.  \n🔍 **RAG-Powered Knowledge Retrieval** → **No hallucinations**, only verified knowledge.  \n📲 **Adaptive \u0026 Interactive Guidance** → **Real-time tutorials tailored to user expertise.**  \n\n---\n\n## 🏗️ Tech Stack  \n- **AI Models:** LLaVA (Vision-Language Model), YOLO (Object Detection), GPT-4/Llama-3 (Content Generation)  \n- **Database:** Neo4J for **knowledge graphs \u0026 relationships**  \n- **Backend:** Python, FastAPI  \n- **Frontend:** Streamlit/Gradio UI  \n- **Deployment:** Cloud-based + Edge AI for low-latency inference  \n\n---\n\n## 🎯 Key Use Cases  \n🔬 **STEM \u0026 Lab Environments** → Real-time guidance for chemistry, engineering, and robotics experiments.  \n🛠️ **DIY \u0026 Home Repairs** → Hands-free **AI-powered assembly instructions**.  \n🍳 **Cooking \u0026 Recipe Assistance** → Step-by-step tutorials based on detected ingredients.  \n👩‍🎓 **Education \u0026 E-Learning** → AI-assisted training **adapts to student knowledge levels**.  \n\n---\n\n## 📖 Research \u0026 References  \n- **Visual Instruction Tuning (LLaVA)** – Liu et al., 2023. [arXiv:2304.08485](https://arxiv.org/abs/2304.08485)  \n- **Retrieval-Augmented Generation (RAG)** – Lewis et al., 2020. [arXiv:2005.11401](https://arxiv.org/abs/2005.11401)  \n- **Graph-Based AI Knowledge Representation** – Neo4J AI Use Cases.  \n- **AI in Education \u0026 Learning** – IEEE Research Articles.  \n\n---\n\n## 🔗 Future Improvements  \n✅ **Offline Mode**: On-device AI models for real-time assistance without internet dependency.  \n✅ **Augmented Reality (AR) Integration**: Overlay AI-generated instructions onto **physical objects**.  \n✅ **Expanded Knowledge Sources**: Incorporate **scientific papers, patents, and industry reports**.  \n\n---\n\n## 📌 Get Started  \n1. Clone this repository:  \n   ```bash\n   git clone https://github.com/raahulcodez/hippo.git\n   cd hippo\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fraahulcodez%2Fhippo","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fraahulcodez%2Fhippo","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fraahulcodez%2Fhippo/lists"}