Skip to content

Engombe23/Decision-IO

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

38 Commits
 
 
 
 
 
 

Repository files navigation

Decision.io - AI-Powered Decision Making Tool

Overview

Decision.io is an intelligent web application that helps users make better decisions using AI and probability theory. It guides users through a structured decision-making process to determine whether they should explore new options or stick with familiar choices.

Key Features

  • AI-powered decision analysis
  • Probability-based recommendations
  • Personalized suggestions based on user inputs
  • Interactive multi-step interface
  • Statistical analysis of decision factors

Technology Stack

Frontend

  • React (TypeScript)
  • CSS for styling
  • Axios for API calls

Backend

  • Node.js with Express
  • Google Generative AI (Gemini)
  • CORS for cross-origin requests

Project Structure

frontend/ ├── src/ │ ├── components/ │ │ └── Home.tsx │ ├── component-css/ │ │ └── Home.css │ ├── App.tsx │ ├── index.tsx │ └── ... (other React files) backendII/ ├── server.js └── package.json

Installation

  1. Clone the repository git clone https://github.com/yourusername/decision-io.git cd decision-io

  2. Install frontend dependencies cd frontend npm install

  3. Install backend dependencies cd ../backendII npm install

  4. Create a .env file in the backend directory with your Google API key: API_KEY=your_google_api_key_here

Running the Application

  1. Start the backend server cd backendII npm start

  2. Start the frontend development server cd ../frontend npm start

  3. Access the application at http://localhost:3000

Key Components

Frontend

  • Home Component: Main interface for user interaction
  • CSS Styling: Custom styles for the application

Backend

  • Server: Handles API requests and AI processing

Decision Making Process

  1. User inputs a decision category

  2. AI generates specific questions based on the category

  3. User answers the questions

  4. System calculates exploration probability using the formula: P(E) = T / (T + k) Where:

    • T = Number of times user chose familiar option
    • k = Exploration adjustment constant
  5. AI generates personalized recommendations based on the analysis

API Endpoints

  • POST /api/category: Generates decision-specific questions
  • POST /api/results: Processes user inputs and returns decision analysis

Contributing

  1. Fork the repository
  2. Create a new branch (git checkout -b feature/YourFeatureName)
  3. Commit your changes (git commit -m 'Add some feature')
  4. Push to the branch (git push origin feature/YourFeatureName)
  5. Open a pull request

License

MIT License

Acknowledgements

  • Google Generative AI for the AI capabilities
  • React and Node.js communities for the development frameworks

About

A tool that is used to determine people's decision on whether they want to explore a new activity or do the same activity again.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

 
 
 

Contributors