README.md

metadata

title: TransformersDiffusersNDatasets
emoji: 🏢
colorFrom: gray
colorTo: green
sdk: streamlit
sdk_version: 1.43.0
app_file: app.py
pinned: false
license: mit
short_description: 🌳 AI Knowledge Tree Builder 📈🌿

🌳 AI Knowledge Tree Builder 📈🌿

🌟 Overview

The AI Knowledge Tree Builder is a Streamlit app designed to cultivate and visualize hierarchical knowledge structures. It supports growing trees with new nodes, linking them to real-time social networks or search engines, and building AI models from CSV uploads—all visualized with Mermaid graphs and tied to cutting-edge research.

🚀 Features

• 🌱 Tree Growth: Add nodes (e.g., "Feature Engineering" under "ML Engineering") to extend trees dynamically.
  • How-to: Enter a new node name and parent node, click "Grow Tree" → Tree updates instantly!
• 🖼️ Mermaid Visualizations: Render trees as clickable graphs with sanitized text (no invalid characters like parentheses).
  • Tip: Click nodes to explore via your chosen search agent (e.g., X for current events).
• 📱 Node Linking: Connect nodes to high-resolution social networks (default: X) or choose from six agents: ArXiv, Google, YouTube, Bing, TruthSocial, X.
  • Tweet: "Stay current with AI Knowledge Tree Builder! 🌳 Nodes link to X by default for real-time insights. #KnowledgeGraph #AI"
• 🌳 Base Trees: Start with "Health" or "ML Engineering" (default) as foundational knowledge structures.
• 🌱 Project Seeds: Choose your project type to seed the tree:
  • Code Project: Root nodes: app.py, requirements.txt, README.md.
  • Papers Project: Root nodes: markdown, mermaid, huggingface.co.
  • AI Project: Three variations:
    1. Streamlit, Torch, Transformers: Upload a CSV, train a minimal ML model, and demo it.
       • How-to: Upload CSV → Select features & target → Train → Download app.py, requirements.txt, README.md.
       • Tweet: "Build an AI model in minutes! 🌳 Upload a CSV, train with Torch, and deploy with Streamlit. #MachineLearning #AI"
    2. DistillKit, MergeKit, Spectrum: Seeds for distillation model building.
    3. Transformers, Diffusers, Datasets: Seeds for advanced AI projects.
• 📚 Research Links: Root node ties to Hugging Face Profile, TeachingCV, DeepResearchEvaluator.
• 📁 Export: Save trees as Markdown with outlines and Mermaid code.
  • Tweet: "Export your knowledge tree as Markdown! 🌳 Outline + Mermaid graph ready for Git or docs. #AI #Visualization"

📋 Structure

• Base Trees:
  • ML Engineering (Default) 🌐
    • Data Preparation → Load Data 📊, Preprocess Data 🛠️
    • Model Building → Train Model 🤖, Evaluate Model 📈
    • Deployment → Deploy Model 🚀
  • Health 🌿
    • Physical Health → Exercise 🏋️, Nutrition 🍎
    • Mental Health → Meditation 🧘, Therapy 🛋️
• Project Seeds:
  • Code Project: app.py 🐍 → requirements.txt 📦 → README.md 📄
  • Papers Project: markdown 📝 → mermaid 🖼️ → huggingface.co 🤗
  • AI Project:
    • Streamlit 🌐 → Torch 🔥 → Transformers 🤖
    • DistillKit 🧪 → MergeKit 🔄 → Spectrum 📊
    • Transformers 🤖 → Diffusers 🎨 → Datasets 📊

🎉 Announcement Tweet

🚀 Meet the AI Knowledge Tree Builder! 🌳 Grow trees 🌱, link nodes to X 📱 for current events, build AI models from CSVs 🤖, and visualize with Mermaid 🖼️. Start with ML Engineering or Health, export to Markdown, and dive into research! Try it: [link] #AI #MachineLearning #KnowledgeGraph

🛠️ How to Run

1. Clone the repo: git clone [repo-link]
2. Install dependencies: pip install -r requirements.txt
3. Launch the app: streamlit run app.py
4. Select a project type, grow your tree, and explore!