An internal tool designed to streamline the process of extracting structured data from PDF documents. This application provides a user-friendly interface for uploading files, processing them through a backend script, and viewing the extracted information in a clear, tabular format. It also includes user authentication and an audit trail for tracking activities.

• Secure User Authentication: Login and Signup functionality to ensure only authorized personnel can access the tool.
• File Upload: Easily upload multiple PDF documents at once.
• Data Extraction: Processes uploaded PDFs to extract relevant data points.
• Tabular Data Display: View the extracted data in a clean, organized table.
• CSV Export: Download the extracted data as a CSV file for offline analysis or use in other applications.
• Audit Log: Tracks key user actions such as logins, file processing, and downloads for accountability.
• Responsive UI: A modern and responsive interface built with React and styled with custom CSS for a seamless user experience.

• Frontend:
  • React
  • Vite
  • JavaScript (ES6+)
  • CSS3 with custom properties for theming
• Backend (assumed):
  • Python
  • Flask (or a similar web framework)

To get a local copy up and running, follow these simple steps.

• Node.js and npm (or yarn) installed.
• Python and pip installed.
• A running backend server that handles the file processing.

1. Clone the repository:

   git clone [https://github.com/aaronseq12/pdf_data_extractor_vite_react_tool.git](https://github.com/aaronseq12/pdf_data_extractor_vite_react_tool.git)

2. Navigate to the project directory:

   cd pdf_data_extractor_vite_react_tool

3. Install NPM packages:

   npm install

4. Start the development server:

   npm run dev

   The application will be available at http://localhost:5173 (or another port if 5173 is in use).

This project requires a backend to handle the file processing. The frontend is configured to communicate with a server running on http://127.0.0.1:5000.

1. Create a Python virtual environment (recommended):

   python -m venv venv
   source venv/bin/activate # On Windows use `venv\Scripts\activate`

2. Install necessary Python packages: You will need to create a requirements.txt file based on your Python script's dependencies.

   pip install Flask flask-cors PyPDF2 pandas # etc.

3. Run the Flask application:

   python your_backend_app.py

1. Create an Account: Use the "Create an account" link on the login page.
2. Login: Sign in with your SSOID and password.
3. Upload Files: On the main page, click "Click to Upload Documents" to select the PDFs you want to process.
4. Process Files: Once files are selected, click the "Process Files" button.
5. View and Download: The application will display the extracted data in a table. You can then download this data as a CSV file using the "Download Excel" button.
6. Audit Log: Click "Show Audit Log" to view a history of actions performed within the tool.

The application is structured into several reusable React components:

• App.jsx: The main component that handles routing and state management.
• Login.jsx: Handles user login.
• SignUp.jsx: Handles new user registration.
• FileUploader.jsx: A component for selecting and uploading files.
• PdfSubmit.jsx: The main dashboard view for authenticated users.
• Processing.jsx: A loading indicator shown while files are being processed.
• ExtractedData.jsx: Displays the extracted data in a table and provides download/logout functionality.
• AuditLog.jsx: Displays the audit trail.

Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request