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https://github.com/worksona/cmf_framework
Cognitive Milestone Framework and Streamlit App
https://github.com/worksona/cmf_framework
cognitive logging-framework streamlit
Last synced: about 1 month ago
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Cognitive Milestone Framework and Streamlit App
- Host: GitHub
- URL: https://github.com/worksona/cmf_framework
- Owner: worksona
- Created: 2024-11-17T14:54:28.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2024-11-17T15:49:44.000Z (about 2 months ago)
- Last Synced: 2024-11-17T16:22:32.265Z (about 2 months ago)
- Topics: cognitive, logging-framework, streamlit
- Language: Jupyter Notebook
- Homepage: https://cognitivemilestones.streamlit.app/
- Size: 60.6 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Cognitive Milestone Framework (CMF)
## QUICKSTART INSTRUCTIONS
```bash
# Install dependencies
pip install -r requirements.txt
# Configure environment variables in .env
# (File is already configured with default settings)
# Start the app
streamlit run app.py
```
## Overview
The Cognitive Milestone Framework (CMF) is a systematic tool designed to help knowledge workers develop key cognitive skills, track milestones, and improve decision-making. It provides a structured approach to logging and analyzing learning activities, milestones, and reflections.
## Project Evolution
### 1. Initial Development (CMF_framework.ipynb)
The framework was initially developed as a Jupyter notebook that introduced core concepts and basic functionality:
- Cognitive Skills Tracking
- Milestone Management
- Habit Reminder System
- Reflection and Decision Logging
- Daily Workflow Integration
### 2. UI Prototype (CMF_framework_UI.ipynb)
The framework was then evolved into a Streamlit-based UI prototype, introducing:
- Interactive web interface
- Tab-based navigation
- Real-time logging capabilities
- JSON-based data storage
### 3. Production Application (app.py)
The final version is a production-ready Streamlit application with enhanced features:
- Robust error handling
- Safe file operations
- Data validation
- Improved user feedback
- Atomic write operations
- Proper path handling
## Installation
1. Clone the repository:
```bash
git clone
cd CMF-Framework
```
2. Create a virtual environment:
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. Install required packages:
```bash
pip install -r requirements.txt
```
4. Create a .env file with required environment variables (see Environment Setup section)
## Requirements
```
streamlit>=1.24.0
python-dotenv>=0.19.0
pathlib>=1.0.1
```
## Environment Setup
Create a `.env` file in the project root with the following variables:
```
# App Configuration
APP_NAME=Cognitive Milestone Framework
DEBUG=False
# File Paths (optional - defaults to current directory)
LOG_DIR=logs
SKILL_LOG_FILE=cognitive_skills_log.json
MILESTONE_LOG_FILE=milestones.json
REFLECTION_LOG_FILE=reflection_logs.json
```
## Usage
1. Start the Streamlit application:
```bash
streamlit run app.py
```
2. Access the application at http://localhost:8501
3. Use the different tabs to:
- Log cognitive skill tasks
- Track milestones
- Record reflections
- View logged data
## Features
### Cognitive Skills Tracking
- Log daily tasks associated with specific cognitive skills
- Categories include: REMEMBER, UNDERSTAND, APPLY, ANALYZE, EVALUATE, CREATE
- Timestamp tracking for progress monitoring
### Milestone Management
- Set and track important milestones
- Update status (In Progress, Completed, Pending)
- Chronological tracking of milestone updates
### Reflection System
- Log detailed reflections on learning and progress
- Timestamp-based organization
- Searchable reflection history
### Data Management
- JSON-based storage for all logs
- Atomic write operations for data safety
- Automatic file initialization
- Data validation and sanitization
## File Structure
```
CMF-Framework/
├── app.py # Main Streamlit application
├── .env # Environment variables
├── requirements.txt # Python dependencies
├── README.md # This documentation
└── logs/ # Log files directory
├── cognitive_skills_log.json
├── milestones.json
└── reflection_logs.json
```
## Best Practices
1. Regular Logging: Make it a habit to log tasks and reflections daily
2. Specific Descriptions: Provide detailed descriptions for tasks and milestones
3. Consistent Reviews: Regularly review logs to identify patterns and areas for improvement
4. Backup: Periodically backup your log files
5. Categorization: Use consistent skill categories for better tracking
## Troubleshooting
- If the application fails to start, ensure all required packages are installed
- If logs aren't saving, check file permissions in the logs directory
- For any JSON-related errors, ensure the log files are properly formatted
- If the UI is slow, consider clearing old log entries
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## License
This project is licensed under the MIT License - see the LICENSE file for details.