Count - Document Statistics Analyzer

A professional Flask web application for analyzing text documents and providing comprehensive statistics.

🚀 Features

• Multi-Format Support: Analyze TXT, CSV, RTF, and DOCX files
• Comprehensive Statistics:
  • Word count
  • Character count (including spaces)
  • Whitespace count (spaces, tabs, newlines)
  • Line count
  • Additional metrics (average word length, composition analysis)
• Professional UI: Modern, responsive design with Bootstrap
• Drag & Drop: Intuitive file upload with drag-and-drop functionality
• Real-time Processing: Fast document analysis with immediate results
• Error Handling: Robust file validation and error management
• Print Support: Print-friendly results page

📸 Screenshots

Main Upload Interface

Professional upload interface with drag-and-drop functionality and supported file format indicators.

Results Display

Comprehensive statistics display with animated cards and detailed metrics breakdown.

🛠️ Installation

Prerequisites

• Python 3.7 or higher
• pip (Python package installer)

Quick Start

1. Clone the repository

   git clone https://github.com/rrodrig30/count.git
   cd count

2. Install dependencies

   pip install -r requirements.txt

3. Run the application

   python app.py

4. Access the application Open your web browser and navigate to http://localhost:5001

📋 Requirements

Flask==2.3.3
python-docx==0.8.11
striprtf==0.0.24
Werkzeug==2.3.7

🎯 Usage

1. Upload a Document: Click the upload area or drag and drop your file
2. Supported Formats: TXT, CSV, RTF, DOCX files (max 16MB)
3. Analyze: Click "Analyze Document" to process your file
4. View Results: Get comprehensive statistics and metrics
5. Print or Analyze More: Print results or upload another document

📊 Supported File Formats

Format	Extension	Description
Plain Text	.txt	Standard text files with UTF-8/Latin-1 encoding
CSV	.csv	Comma-separated values (analyzes all cell content)
Rich Text	.rtf	Rich Text Format documents
Word Document	.docx	Microsoft Word documents

🏗️ Architecture

The application follows a clean, modular architecture:

count/
├── app.py                 # Main Flask application
├── templates/             # HTML templates
│   ├── index.html        # Main upload interface
│   └── results.html      # Results display page
├── static/               # Static assets (auto-created)
├── requirements.txt      # Python dependencies
├── CLAUDE.md            # Development documentation
└── README.md            # This file

Core Components

• DocumentProcessor: Handles file validation and text extraction
• TextAnalyzer: Performs statistical analysis on extracted text
• Flask Routes: Manages web requests and responses
• Templates: Professional UI with Bootstrap styling

🔧 Configuration

Application Settings

• Host: 0.0.0.0 (accessible from network)
• Port: 5001 (configurable in app.py)
• Debug Mode: Enabled for development
• Max File Size: 16MB
• Allowed Extensions: txt, csv, rtf, docx, doc

Security Features

• Secure filename processing
• File extension validation
• Temporary file cleanup
• CSRF protection

🧪 Testing

A sample test file is included:

# Test with the included sample
# Upload test_sample.txt through the web interface

🤝 Contributing

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

📝 License

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

🙏 Acknowledgments

• Built with Flask web framework
• UI powered by Bootstrap
• Icons from Font Awesome
• Document processing libraries:
  • python-docx for Word documents
  • striprtf for RTF files

📞 Support

If you have any questions or need help with the application:

1. Check the CLAUDE.md file for development guidance
2. Open an issue on GitHub
3. Review the error messages in the application for troubleshooting

🚀 Deployment

Local Development

python app.py

Production Deployment

For production deployment, consider using:

• Gunicorn: gunicorn -w 4 -b 0.0.0.0:5001 app:app
• Docker: Create a Dockerfile for containerized deployment
• Cloud Platforms: Deploy to Heroku, AWS, or Google Cloud

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Copyright © 2025 Count Document Analyzer. All rights reserved.

Built with ❤️ using Flask and modern web technologies