AutoDash

AutoDash is an advanced tool designed to revolutionize data analysis by automating the creation of interactive dashboards from multiple data sources. Leveraging a fine-tuned Llama3 LLM specifically tailored for data analytics, AutoDash transforms complex data into actionable insights through real-time updates and intuitive natural language interactions. The tool integrates a Python-based RAG (Retrieval-Augmented Generation) framework, enabling seamless querying and generation of SQL, NoSQL, and other database queries using natural language prompts. This versatility allows for efficient and precise analysis across a wide range of data platforms. AutoDash also prioritizes security, ensuring that all data processing remains within the company's servers, providing dynamic visualizations and empowering businesses to make informed decisions effortlessly.

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Table of Contents

• Project Overview
• Prerequisites
• Setup Instructions
  • Backend Repository - Python (AutoDash API)
  • Frontend Repository - React (AutoDash Frontend)
  • Backend Repository - Java (AutoDash Java API)
• Tech Stack
• Usage
• Contribution Guidelines
• FAQs
• Roadmap

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Project Overview

AutoDash is a cutting-edge solution designed to simplify the data analysis process by automating the creation of interactive, real-time dashboards. With a secure in-house processing system, AutoDash ensures that your data remains within your company’s infrastructure, offering both efficiency and peace of mind. The tool's AI-driven insights and dynamic visualizations empower businesses to make informed decisions quickly and accurately.

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Prerequisites

Before you begin, ensure you have the following installed:

• Python 3.8+
• Node.js and NPM
• JDK 21
• Maven
• MySQL
• Nginx

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Setup Instructions

1. Backend Repository - Python (AutoDash API)

Repository Link: AutoDash API

Steps:

1. Clone the Repository:

   git clone https://github.com/techcodebhavesh/AutoDash.git
   cd AutoDash

2. Create a Python Virtual Environment:

   python3 -m venv pyenv
   source pyenv/bin/activate

3. Install Required Packages:

   pip install -r requirements.txt

4. Environment Configuration:

   • Copy the example environment file:

     cp .env.example .env

   • Edit the .env file with your credentials and other required configurations.

5. Run the Flask Server:

   python run.py

6. Nginx Setup:

   • Ensure Nginx is installed and configured to proxy requests to the Flask server.

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2. Frontend Repository - React (AutoDash Frontend)

Repository Link: AutoDash Frontend

Steps:

1. Clone the Repository:

   git clone https://github.com/Vaishnavi4008/Autodash_frontend.git
   cd Autodash_frontend

2. Environment Configuration:

   • Copy the example environment file:

     cp .env.example .env

   • Edit the .env file with your credentials or any required configuration changes.

3. Install Dependencies:

   npm install

4. Run the Development Server:

   npm run dev

   • This will start the Vite development server, and the frontend will be accessible at http://localhost:5173.

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3. Backend Repository - Java (AutoDash Java API)

Repository Link: AutoDash Java API

Steps:

1. Clone the Repository:

   git clone https://github.com/SpectacularVoyager/AutodashJava.git
   cd AutodashJava

2. Install JDK 21:

   • Ensure that JDK 21 is installed on your system.

3. Build the Project:

   mvn clean install

4. Run the Spring Boot Application:

   mvn spring-boot:run

5. Database Setup:

   • SQL files are located in the res/sql.sql directory.
   • Configure MySQL settings in src/main/resources/jdbc.properties.

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Tech Stack

• Backend: Flask (Python), Spring Boot (Java)
• Frontend: React (JavaScript), Vite
• Database: MySQL
• Other: Nginx, Maven, JDK 21

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Usage

Once all services are running, you can access AutoDash through your browser at http://localhost:5173.

• Data Integration: Connect multiple data sources through the dashboard.
• Natural Language Queries: Use the fine-tuned Llama3 LLM for intuitive data queries.
• Dynamic Visualizations: Customize and interact with data visualizations in real time.

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Contribution Guidelines

We welcome contributions to AutoDash! Please follow these steps to contribute:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes and commit them (git commit -m 'Add some feature').
4. Push to the branch (git push origin feature-branch).
5. Open a Pull Request.

For more details, refer to our CONTRIBUTING.md file.

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FAQs

Q: What if I encounter a ModuleNotFoundError? A: Ensure all dependencies are installed as per the requirements.txt or package.json files.

Q: How do I configure MySQL for the Java backend? A: Edit the jdbc.properties file in src/main/resources with your MySQL credentials.

Q: How do I set up Nginx for the Python backend? A: Follow standard Nginx setup procedures, ensuring it proxies requests to the Flask server.

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Roadmap

• Version 2.0: Multi-tenant support and enhanced security features.
• Version 3.0: Expanded LLM capabilities for more complex data queries.
• Future Plans: Integration with more data sources and enhanced visualization options.