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README.md

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-# DataLoom
-Project is to design and implement a web-based GUI for data wrangling, aimed at simplifying the process of managing and transforming tabular datasets. This application will serve as a graphical interface for the powerful Python library, allowing users to perform complex data manipulation tasks without the need for in-depth programming knowledge. 
+# DataLoom: Web-Based GUI for Data Wrangling
 
-### Apps and Packages
+## Overview
 
-- `frontend`: a React.js app
-- `backend`:  Python(FastAPI) app
+**DataLoom** is an advanced web-based graphical user interface (GUI) designed to simplify the management and transformation of tabular datasets. By leveraging the powerful Python library, DataLoom enables users to perform complex data wrangling tasks without requiring in-depth programming knowledge. This intuitive application serves both data analysts and non-technical users alike, enabling them to quickly clean, transform, and analyze data in a visual and easy-to-navigate environment.
 
-### Run Application
-**Set Up Environment Variables** :
-Create a `.env` file in the `apps/backend` directory and add details as per `.env.sample` file.
+## Project Structure
 
-**Installing FastApi Backend** : In the `apps/backend` directory, run `python3 -m venv env`, then run `. env/scripts/activate` (On Windows), then ensure all required dependencies are installed by running `pip install -r requirements.txt`.
+The **DataLoom** project is divided into two primary components:
 
-**To run the project**, run the following command:
-```
+- **Frontend**: A React.js application that provides a modern and interactive interface for data wrangling tasks.
+- **Backend**: A Python FastAPI application that handles data processing and serves as the API layer for the frontend.
+
+The frontend communicates with the backend to perform the necessary data manipulation operations and visualize the results in real time. This separation of concerns ensures a scalable and maintainable codebase.
+
+## Prerequisites
+
+To set up and run **DataLoom** on your local machine, ensure that the following software is installed:
+
+- **Node.js** (v18.x or higher): Required for running the frontend React.js application.
+- **Python 3.8 or higher**: Necessary for the backend FastAPI application and the data wrangling functionality.
+- **Git**: To clone the repository and manage the codebase.
+- **TurboRepo**: Required for managing monorepo tasks efficiently.
+
+### Optional:
+
+- **PostgreSQL** (or other databases): If you plan to implement persistent data storage for user sessions or dataset logs.
+
+## Setup Instructions
+
+### Step 1: Clone the Repository
+
+Start by cloning the **DataLoom** repository from GitHub to your local machine:
+
+```bash
+git clone https://github.com/c2siorg/DataLoom.git
 cd DataLoom
-npm run dev
 ```
 
-The backend server will start and be accessible at `http://127.0.0.1:8000`.
+### Step 2: Set Up Environment Variables
+
+Navigate to the `backend` directory and create a `.env` file. This file will store configuration settings required for the backend services.
+
+```bash
+cd backend
+cp .env.sample .env
+```
+
+Open the `.env` file in your preferred text editor and populate the required variables. You may reference the `.env.sample` file for guidance on the structure and required values.
+
+### Step 3: Install TurboRepo and Dependencies
+
+TurboRepo is required for efficient package management in monorepos. Install TurboRepo globally:
+
+```bash
+npm install -g turbo
+```
+
+Or use `npx` to run TurboRepo commands without global installation:
+
+```bash
+npx turbo run dev
+```
+
+Install project dependencies:
+
+```bash
+npm install
+```
+
+
+### Step 4: Install Backend Dependencies
+
+Create a Python virtual environment for the backend services:
+
+```bash
+python3 -m venv env
+```
+
+Activate the virtual environment:
+
+- **On Windows**:
+
+  ```bash
+  .\env\bin\Activate
+  ```
+
+- **On macOS/Linux**:
+
+  ```bash
+  source env/bin/activate
+  ```
+
+Install the required Python dependencies using `pip`:
+
+```bash
+pip install -r requirements.txt
+```
+
+### Step 5: Install Frontend Dependencies
+
+Navigate to the DataLoom directory and install the necessary Node.js packages:
+
+```bash
+cd ..
+npm install
+```
+
+### Step 6: Run the Application with TurboRepo
+
+Now that all dependencies are installed, use TurboRepo to start both the frontend and backend servers efficiently:
+
+```bash
+npx turbo run dev
+```
+
+Alternatively, you can start them manually:
+
+- **Start Backend Server**:
+
+  ```bash
+  cd ../backend
+  uvicorn app.main:app --host 127.0.0.1 --port 8000 --reload
+  ```
+
+- **Start Frontend Server**:
+
+  ```bash
+  cd ..
+  npm run dev
+  ```
+
+Once both servers are running, you can access the following URLs:
+
+- The backend API will be available at: [http://127.0.0.1:8000](http://127.0.0.1:8000)
+- The frontend application will be accessible at: [http://localhost:3000](http://localhost:3000)
+
+## Usage
+
+Upon successful setup, you can access the **DataLoom** interface by opening your browser and navigating to `http://localhost:3000`. The application will allow you to:
+
+- Upload tabular datasets in CSV or Excel formats.
+- Perform a variety of data wrangling operations, including data cleaning, transformation, and enrichment.
+- Visualize the dataset's structure and apply advanced filters with just a few clicks.
+
+The GUI is designed to make data manipulation accessible for non-technical users while still offering advanced features for more experienced users. The backend handles the heavy lifting, ensuring the frontend remains responsive and easy to navigate.
+
+## Contributing
+
+We welcome contributions from the open-source community. If you'd like to contribute to **DataLoom**, please follow these steps:
+
+1. **Fork the repository**: Create a personal copy of the repository.
+2. **Create a new branch**: For any new features or bug fixes, create a new branch from `main`.
+3. **Make your changes**: Implement your changes, ensuring they align with the project's coding standards.
+4. **Commit your changes**: Write clear, concise commit messages to describe your changes.
+5. **Push your changes**: Push your changes to your forked repository.
+6. **Create a pull request**: Open a pull request (PR) from your branch to the main repository.
+
+Please ensure your contributions are well-documented and include relevant tests if applicable.
+
+## License
+
+**DataLoom** is licensed under the Apache License 2.0. You can view the full license details in the [LICENSE](LICENSE) file.
+
+## Acknowledgments
+
+We would like to express our gratitude to the open-source community and all contributors who have made **DataLoom** possible. This project would not be the same without your support and expertise.
+
+We also extend special thanks to the developers and researchers behind the underlying libraries and technologies, including **FastAPI**, **React.js**, and **Python Pandas**, which power the data wrangling and user interface functionalities.
+