📄 Scan-PDF-Paper

A powerful document analysis tool that extracts text from various document formats (PDF, DOCX, TXT) and performs intelligent topic classification and keyword matching analysis.

🌟 Features

• Multi-format Document Support: Extract text from PDF, DOCX, and TXT files
• Intelligent Topic Classification: AI-powered topic classification using Sentence Transformers
• Keyword Matching Analysis: Calculate topic relevance based on predefined keyword sets
• Interactive Web Interface: User-friendly Streamlit-based web application
• Real-time Analysis: Get instant results with visual progress indicators
• Multiple Analysis Pages: Main analysis page and advanced features

🚀 Quick Start

Prerequisites

• Python 3.8 or higher
• pip package manager

Installation

1. Clone the repository

   git clone https://github.com/NhanPhamThanh-IT/Scan-PDF-Paper.git
   cd Scan-PDF-Paper

2. Install dependencies

   pip install -r requirements.txt

3. Run the application

   streamlit run app/main.py

4. Open your browser and navigate to http://localhost:8501

📖 Usage

Main Analysis Page

1. Select a Topic: Choose from predefined topics including:

   • AI & Technology
   • Healthcare
   • Finance
   • Environment
   • Cybersecurity
   • Software Development
   • And more...

2. Upload Document: Support for multiple file formats:

   • PDF files
   • Microsoft Word documents (.docx)
   • Plain text files (.txt)

3. Get Analysis Results: View detailed analysis including:

   • Total word count
   • Keyword matches found
   • Topic relevance percentage
   • Detailed breakdown of analysis

Advanced Features

Access the Advanced page for additional functionality and enhanced analysis options.

🏗️ Project Structure

Scan-PDF-Paper/
├── app/
│   ├── main.py                 # Main Streamlit application
│   ├── assets/
│   │   └── themes.css          # CSS styling
│   ├── core/
│   │   ├── AI/
│   │   │   └── TopicClassifier.py  # AI-powered topic classification
│   │   └── Utils/
│   │       ├── DataHandling.py     # Data processing utilities
│   │       ├── FileHandling.py     # File extraction utilities
│   │       └── TextHandling.py     # Text processing utilities
│   ├── dataset/
│   │   ├── metadata/
│   │   │   └── topics.json         # Available topics list
│   │   └── topics_keywords/        # Keyword datasets for each topic
│   │       ├── AI.json
│   │       ├── Healthcare.json
│   │       ├── Finance.json
│   │       └── ...
│   ├── pages/
│   │   ├── MainPage.py             # Main analysis interface
│   │   ├── AdvancesPage.py         # Advanced features
│   │   └── HelpsPage.py            # Help and documentation
│   ├── settings/
│   │   └── ThemeManager.py         # Theme management
│   └── ui/
│       ├── PageHeaderComponent.py  # Reusable header component
│       ├── ResultComponent.py      # Results display component
│       └── TabsComponent.py        # Navigation tabs component
├── requirements.txt                # Python dependencies
└── README.md                      # Project documentation

🛠️ Technical Details

Core Technologies

• Streamlit: Web framework for the user interface
• PyMuPDF (fitz): PDF text extraction
• python-docx: Microsoft Word document processing
• Sentence Transformers: AI-powered text analysis
• spaCy: Natural language processing and stop words removal

AI-Powered Classification

The application uses the all-MiniLM-L6-v2 model from Sentence Transformers to:

• Generate embeddings for input text
• Compare against predefined topic embeddings
• Calculate cosine similarity scores
• Provide confidence percentages for topic classification

Text Processing Pipeline

1. Document Parsing: Extract raw text from uploaded files
2. Text Preprocessing: Remove stop words and normalize text
3. Keyword Analysis: Match against topic-specific keyword sets
4. AI Classification: Use machine learning for intelligent topic detection
5. Results Generation: Calculate relevance scores and generate insights

📊 Supported Topics

The application supports analysis across 21+ topic categories:

• Technology: AI, Software, Cybersecurity
• Sciences: Healthcare, Environment, Science
• Business: Finance, Economy, Business
• Society: Education, Politics, Law, Culture
• Lifestyle: Sports, Travel, Food, Art
• Others: Media, Religion, Agriculture, Energy, Security

🧪 Testing

Run the test suite using pytest:

pytest

Test configuration is available in pytest.ini.

📋 Requirements

Core Dependencies

• streamlit>=1.47.0 - Web application framework
• PyMuPDF - PDF processing
• python-docx - Word document processing
• sentence-transformers>=2.6.1 - AI text analysis
• torch>=2.0.0 - Machine learning backend
• spacy - Natural language processing

Development Dependencies

• pytest - Testing framework
• pytest-mock - Testing utilities

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some 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.

🆘 Support

• Documentation: Visit the Help & Documentation page in the application
• Issues: Report bugs or request features via GitHub Issues
• Discussions: Join project discussions on GitHub

🔮 Future Enhancements

• Support for additional file formats (RTF, ODT)
• Batch processing capabilities
• Export results to various formats
• Custom topic creation
• Advanced visualization features
• REST API integration

⚡ Performance Notes

• First-time loading may take longer due to AI model initialization
• Large documents (>10MB) may require additional processing time
• Recommended RAM: 4GB+ for optimal performance

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Built with ❤️ using Python and Streamlit