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https://github.com/user2745/jarvis-intelligence-system
https://github.com/user2745/jarvis-intelligence-system
Last synced: about 21 hours ago
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- Host: GitHub
- URL: https://github.com/user2745/jarvis-intelligence-system
- Owner: user2745
- Created: 2024-12-26T01:39:18.000Z (about 1 month ago)
- Default Branch: JARVIS-intelligence-system
- Last Pushed: 2024-12-26T05:47:28.000Z (about 1 month ago)
- Last Synced: 2024-12-26T06:28:21.181Z (about 1 month ago)
- Language: Python
- Size: 5.86 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
Here's a polished and descriptive README for the **JARVIS-like Intelligence System**:
---
# JARVIS-Like Intelligence System
A modular and extensible framework for building real-time, adaptive intelligence systems. Inspired by fictional systems like JARVIS, this framework combines **context gathering**, **decision-making**, and **action execution** into a cohesive, highly customizable structure.
---
## Features
- **Contextual Awareness**:
- Gathers real-time data from multiple sources (e.g., APIs, device states).
- **Dynamic Decision-Making**:
- Combines rule-based logic and AI-driven insights for actionable recommendations.
- **Proactive Conversations**:
- Engages in two-way interactions with the user, seeking approval for key decisions.
- **Modularity**:
- Add or replace engines and decision units to suit any domain (e.g., IoT, finance, network management).
- **Scalability**:
- Easily extendable for more complex use cases or additional integrations.
---
## How It Works
### 1. **Contextual Engines**
Contextual engines gather data from various sources and provide structured information to decision-making units.
- **Example**: Fetching weather data, system stats, or smart home metrics.
### 2. **Decision-Making Units (DMUs)**
DMUs analyze the context to generate actionable insights or decisions.
- **Example**:
- Rule-Based: “Turn off non-essential devices if energy usage is high.”
- AI-Powered: “Based on weather trends, suggest carrying an umbrella.”
### 3. **Action Executor**
The Action Executor takes decisions and executes them while enabling proactive user interaction.
- **Example**:
- Automatically dimming lights.
- Sending alerts or reminders.
---
## Example Workflow
```plaintext
Starting JARVIS-like Intelligence System...
--- Gathering Context ---
Weather Context: {'weather': 'Rainy'}
System Context: {'cpu_usage': 40, 'memory_usage': 70}
--- Making Decisions ---
Basic Decision: Turn off non-essential devices
AI Decision: Energy usage is high. Dim the lights.
--- Executing Actions ---
Executing: Turn off non-essential devices
Proactive Alert: Energy usage is high. Dim the lights? (yes/no): yes
Action Confirmed: Dim the lights
Actions completed.
--- Executed Actions Log ---
Confirmed: Dim the lights
--- End of Log ---
```
---
## Installation
### Requirements
- Python 3.8+
- Install dependencies:
```bash
pip install -r requirements.txt
```
### File Structure
```plaintext
.
├── README.md # Documentation
├── main.py # Main entry point for the system
├── context_engines/ # Contextual engines for gathering data
│ ├── weather_engine.py # Weather context engine
│ ├── stock_engine.py # stock market context engine
│ ├── smart_home_engine.py # Smart home context engine
├── decision_units/ # Decision-making logic
│ ├── basic_dmu.py # Rule-based decision-making unit
│ ├── ai_dmu.py # AI-powered decision-making unit
├── action_executor/ # Executes decisions and engages with the user
│ ├── action_executor.py # Core action executor logic
├── requirements.txt # Python dependencies
```
---
## Usage
1. **Run the System**:
```bash
python main.py
```
2. **Customize Engines and Units**:
- Modify or add new engines and decision-making units in their respective directories.
- Update `main.py` to include your changes.
3. **Test Proactive Conversations**:
- Interact with the system during execution for personalized decision-making.
---
## Extending the System
### Add a New Contextual Engine
1. Create a new file in the `context_engines/` directory (e.g., `network_engine.py`).
2. Implement a `run()` method that gathers and returns data.
3. Update `main.py` to include the new engine.
### Add a New Decision-Making Unit
1. Create a new file in the `decision_units/` directory (e.g., `network_dmu.py`).
2. Implement a `run(context)` method to analyze data and return decisions.
3. Update `main.py` to include the new DMU.
---
## Real-World Applications
- **Home Automation**:
- Optimize smart home devices based on energy usage and weather conditions.
- **Network Management**:
- Monitor device states and reroute traffic dynamically.
- **Financial Analytics**:
- Analyze market trends and suggest portfolio adjustments.
---
## License
This project is open-source under the [MIT License](LICENSE).
---
## Acknowledgments
Inspired by the idea of building a real-time, autonomous system akin to fictional AI assistants like JARVIS. Built with a focus on simplicity, extensibility, and real-world applicability.
---
Let me know if you’d like to tweak or expand the README! 🚀