AutoDataAnalyzer

AutoDataAnalyzer is a cutting-edge tool designed to automate the process of data ingestion, analysis, and visualization using powerful AI/ML models and interactive pipelines. This application provides a seamless experience for uploading datasets, querying data, and generating insightful visualizations.

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Features

• Interactive Data Analysis: Upload datasets and metadata, then query for insights.
• Automated Visualization: Generates high-quality, interactive Plotly visualizations.
• Custom Query Handling: Uses advanced LLMs (Llama 3.1-70B) for natural language query processing.
• Pipeline Integration: End-to-end processing via LangChain and other robust frameworks.
• Dockerized Deployment: Easy setup with Docker support.
• CI/CD Workflow: Fully automated CI/CD pipeline using GitHub Actions.

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Table of Contents

1. Directory Structure
2. Technical Details
3. Requirements
4. Installation
5. Usage
6. Screenshots
7. License

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Directory Structure

RauhanAhmed-Auto-Data-Analyzer/
├── .github/
│   └── workflows/
│       └── ci-cd.yaml
├── app.py
├── requirements.txt
├── setup.py
├── params.yaml
├── config.ini
├── Dockerfile
├── LICENSE
└── src/
    ├── components/
    │   ├── queryChainBuilder.py
    │   ├── dataIngestion.py
    │   ├── __init__.py
    │   └── codeGenerator.py
    ├── __init__.py
    ├── pipelines/
    │   ├── __init__.py
    │   └── pipeline.py
    └── utils/
        ├── logger.py
        ├── exceptions.py
        ├── __init__.py
        └── functions.py

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Technical Details

Tools and Frameworks

• Programming Language: Python 3.10
• Web Framework: Flask
• Interactive Interface: PyWebIO
• Visualization: Plotly
• Large Language Model: Llama 3.1-70B
• Task Management: LangChain (including experimental and community modules)
• Deployment: Docker
• CI/CD: GitHub Actions

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Requirements

• Python 3.10
• Docker
• Dependencies listed in requirements.txt

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Installation

1. Clone the repository:

   git clone https://github.com/RauhanAhmed/AutoDataAnalyzer.git
   cd AutoDataAnalyzer

2. Install dependencies:

   pip install -r requirements.txt

3. Run the application:

   python app.py

4. (Optional) Build and run using Docker:

   docker build -t autodataanalyzer .
   docker run -p 7860:7860 autodataanalyzer

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Usage

1. Start the application by running app.py or the Docker container.
2. Upload your CSV files and metadata.
3. Enter your query in natural language.
4. View the generated visualization and export as needed.
5. Type exit to terminate the application.

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Screenshots

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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Author

Developed by Rauhan Ahmed Siddiqui.