An interactive starter application showcasing Python, Streamlit, and Ollama integration with educational examples and mini-applications.

This application includes over 50 comprehensive example pages organized into 4 main categories:

1. 🐍 Python Basics (16 pages) - Core Python programming concepts with interactive examples
2. 📊 Streamlit Basics (18 pages) - Building interactive web apps with Streamlit
3. 🦙 Ollama Python SDK Basics (19 pages) - Working with Ollama's Python SDK for local LLMs
4. 🚀 Ollama AI MiniApps (3 apps) - Complete mini-applications powered by Ollama:
   • 📝 Text Generator
   • 💬 Chatbot
   • 🔍 Text Analyzer

• Python 3.8 or higher
• Ollama installed (for Ollama-related features)

1. Clone the repository:

   git clone https://github.com/r14r/Enrichment_AI-StarterApp.git
   cd Enrichment_AI-StarterApp

2. Install Python dependencies:

   pip install -r requirements.txt

3. Install Ollama (Optional - for AI features):

   • Visit https://ollama.ai to download and install Ollama
   • Pull a model: ollama pull gemma3:1b

streamlit run app.py

The app will open in your browser at http://localhost:8501

Use the sidebar to navigate between different pages:

• 🏠 Home - Overview and introduction
• 🐍 Python Basics - Learn Python fundamentals
• 📊 Streamlit Basics - Explore Streamlit components
• 🦙 Ollama Python SDK Basics - Understand Ollama integration
• 🚀 Ollama AI MiniApps - Try complete AI applications

Interactive examples covering:

• Fundamentals: Data types, lists, loops, functions, classes
• Advanced Concepts: Dictionary operations, string manipulation, file handling
• Error Handling: Exceptions and try-except blocks
• Advanced Features: List comprehensions, lambda functions, decorators
• Standard Library: Modules and imports, JSON handling, regular expressions
• Interactive Tools: Temperature converter

Learn Streamlit through examples:

• Display Elements: Text elements, status elements, data display, charts
• Input Widgets: Text input, number input, sliders, select boxes, checkboxes, buttons
• Layout Components: Columns, tabs, containers, sidebar widgets
• Advanced Features: File upload/download, session state, forms, expanders
• Media: Images, video, audio, camera input
• Optimization: Caching data, performance tips
• Customization: Custom HTML/CSS components
• Navigation: Multipage app structure
• Interactive Examples: Progress indicators, data dashboard

Comprehensive guide to Ollama:

• Getting Started: Installation, basic usage, model management
• Core Features: Chat completions, streaming, text generation, system messages
• Advanced Techniques: Multi-turn conversations, context window management
• Prompt Engineering: Prompt templates, response parsing, structured output
• Embeddings: Semantic search, similarity matching, vector operations
• Model Selection: Model comparison, custom model creation
• Optimization: Performance tuning, batch processing
• Specialized: Vision models for image understanding
• Parameters: Temperature, top-p, top-k, and advanced settings
• Error Handling: Connection testing, retry logic, troubleshooting

Three complete applications:

• Generate creative text with customizable prompts
• Adjustable creativity (temperature)
• Control output length
• Multiple model options

• Interactive conversational AI
• Customizable system prompts
• Real-time streaming responses
• Chat history management
• Multiple model support

• Text summarization
• Key point extraction
• Sentiment analysis
• Topic identification
• Language simplification
• Grammar checking

The app supports multiple Ollama models:

• gemma3:1b - General purpose, recommended for beginners
• mistral - Fast and efficient
• codellama - Specialized for code
• phi - Lightweight option

To use a model, first pull it:

ollama pull gemma3:1b
ollama pull mistral

Customize AI behavior with these parameters:

• Temperature (0.0-2.0): Controls randomness
  • 0.0-0.3: Focused, deterministic
  • 0.4-0.7: Balanced
  • 0.8-2.0: Creative, random
• Max Tokens: Maximum length of generated text
• System Prompt: Defines AI behavior and personality

Enrichment_AI-StarterApp/
├── app.py                  # Main application file
├── requirements.txt        # Python dependencies
├── README.md              # This file
└── pages/                 # Page modules
    ├── __init__.py
    ├── python_basics.py
    ├── streamlit_basics.py
    ├── ollama_basics.py
    └── ollama_miniapps.py

• streamlit - Web application framework
• ollama - Ollama Python SDK
• pandas - Data manipulation
• numpy - Numerical computing
• requests - HTTP library

• Start with Python and Streamlit basics if you're new
• Make sure Ollama is running before using AI features
• Experiment with different models and parameters
• Use lower temperatures for factual tasks, higher for creative ones
• Check the "View Source Code" sections to see implementation details

• Ensure Ollama is installed and running
• Check if the model is pulled: ollama list
• Try pulling the model: ollama pull gemma3:1b

• Make sure all dependencies are installed: pip install -r requirements.txt
• Verify Python version is 3.8 or higher

• Clear cache: streamlit cache clear
• Restart the app

This project is open source and available under the MIT License.

Contributions are welcome! Feel free to:

• Report bugs
• Suggest new features
• Submit pull requests

For questions or issues, please open an issue on GitHub.

Happy Learning! 🎉