Initialize AI Applications Hub structure

- Updated README with AI application focus
- Added project directory structure for AI/ML work
- Created requirements.txt with core ML dependencies
- Added comprehensive .gitignore for Python/AI projects
- Set up organized folders for applications, experiments, models, etc.

.gitignore

@@ -0,0 +1,65 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Virtual environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# PyCharm
+.idea/
+
+# VS Code
+.vscode/
+
+# macOS
+.DS_Store
+
+# Model files (large files)
+*.pt
+*.pth
+*.h5
+*.pkl
+*.joblib
+
+# Data files
+*.csv
+*.json
+*.parquet
+datasets/*.zip
+datasets/*.tar.gz
+
+# Logs
+*.log
+logs/
+
+# API keys and secrets
+.env
+secrets.json
+config.json

README.md

@@ -1,10 +1,63 @@
 ---
-title: README
-emoji: 🌖
+title: AI Applications Hub
+emoji: 🤖
 colorFrom: blue
-colorTo: gray
+colorTo: purple
 sdk: docker
 pinned: false
+license: mit
 ---
 
-Edit this `README.md` markdown file to author your organization card.
+# AI Applications Hub
+
+A repository for developing and showcasing AI-powered applications and experiments.
+
+## Overview
+
+This repository serves as a central hub for AI application development, featuring:
+
+- 🤖 Machine Learning models and experiments
+- 🔬 AI research prototypes
+- 📊 Data science projects
+- 🛠️ AI tooling and utilities
+- 🚀 Production-ready AI applications
+
+## Getting Started
+
+### Prerequisites
+
+- Python 3.8+
+- Git
+- Docker (optional, for containerized deployments)
+
+### Installation
+
+```bash
+git clone https://github.com/your-username/README.git
+cd README
+pip install -r requirements.txt
+```
+
+## Project Structure
+
+```
+├── applications/          # Production AI applications
+├── experiments/           # Research and prototyping
+├── models/               # Trained models and checkpoints
+├── datasets/             # Data processing and datasets
+├── notebooks/            # Jupyter notebooks for exploration
+├── utils/                # Shared utilities and tools
+└── docs/                 # Documentation and guides
+```
+
+## Contributing
+
+1. Fork the repository
+2. Create a feature branch
+3. Make your changes
+4. Add tests if applicable
+5. Submit a pull request
+
+## License
+
+This project is licensed under the MIT License - see the LICENSE file for details.

applications/README.md

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+# Applications
+
+Production-ready AI applications and deployments.
+
+## Structure
+
+Each application should have its own directory with:
+- `app.py` - Main application entry point
+- `requirements.txt` - Application-specific dependencies
+- `Dockerfile` - Container configuration
+- `README.md` - Application documentation

experiments/README.md

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+# Experiments
+
+Research prototypes, proof-of-concepts, and experimental AI work.
+
+## Guidelines
+
+- Use descriptive directory names with dates (e.g., `2024-01-15-transformer-experiment`)
+- Include a README.md in each experiment explaining the hypothesis and results
+- Keep experiments self-contained with their own dependencies when possible

models/README.md

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+# Models
+
+Trained models, checkpoints, and model artifacts.
+
+## Organization
+
+- `pretrained/` - Downloaded pre-trained models
+- `checkpoints/` - Training checkpoints
+- `fine-tuned/` - Custom fine-tuned models
+
+## Note
+
+Large model files should be stored using Git LFS or external storage (HuggingFace Hub, etc.) and referenced here.

requirements.txt

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+# Core AI/ML dependencies
+numpy>=1.21.0
+pandas>=1.3.0
+scikit-learn>=1.0.0
+matplotlib>=3.4.0
+seaborn>=0.11.0
+
+# Deep Learning
+torch>=1.9.0
+torchvision>=0.10.0
+transformers>=4.20.0
+
+# Jupyter and development
+jupyter>=1.0.0
+ipykernel>=6.0.0
+ipywidgets>=7.6.0
+
+# Data processing
+requests>=2.25.0
+beautifulsoup4>=4.9.0
+pillow>=8.3.0
+
+# Utilities
+tqdm>=4.62.0
+python-dotenv>=0.19.0

utils/__init__.py

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+# Shared utilities for AI applications