AI-Powered Legal Contract Analyzer An intelligent system developed by CothonSolutions for analyzing legal contracts using natural language processing (NLP) and machine learning (ML). This web-based tool automates the extraction of key clauses, identifies risky or unusual clauses, generates plain-English summaries, and provides an interactive dashboard for legal and business professionals. Features
Text Extraction: Extracts text from PDF and DOCX documents using pdfplumber and docx2txt. Clause Extraction: Identifies key clauses (e.g., indemnity, confidentiality, governing law) using SpaCy’s Named Entity Recognition (NER). Risk Classification: Classifies clauses as risky or unusual using machine learning models trained with Scikit-learn. Contract Summarization: Generates plain-English summaries using HuggingFace’s Legal-BERT. Interactive Dashboard: Visualizes analysis results with Streamlit and Plotly/Matplotlib. Data Storage: Stores extracted data in SQLite and manipulates it with Pandas/JSON.
Requirements
Python 3.9 or higher Required packages (see requirements.txt): streamlit>=1.31.1 pandas>=2.2.0 plotly>=5.18.0 nltk>=3.8.1 PyPDF2>=3.0.1 pdfplumber>=0.10.3 python-docx>=1.1.0 spacy>=3.6.1 scikit-learn>=1.3.0 transformers>=4.30.2 Additional dependencies listed in setup.py
Installation
Clone the repository: git clone https://github.com/cothonsolutions/legal-contract-analyzer.git cd legal-contract-analyzer
Create a virtual environment (recommended): python -m venv .venv source .venv/bin/activate # On Windows: .venv\Scripts\activate
Install dependencies: pip install -r requirements.txt
Install SpaCy model: python -m spacy download en_core_web_sm
Download required NLTK data: python -c "import nltk; nltk.download('punkt'); nltk.download('averaged_perceptron_tagger'); nltk.download('maxent_ne_chunker'); nltk.download('words')"
Usage
Start the Streamlit application: streamlit run src/main.py
Access the web interface:
Open your web browser and navigate to http://localhost:8501 (or the URL shown in the terminal). Upload a contract document (PDF or DOCX). View the extracted clauses, risk analysis, summary, and interactive dashboard.
Project Structure legal-contract-analyzer/ ├── src/ │ └── main.py # Main Streamlit application ├── models/ │ ├── init.py │ ├── ner_model.py # Named Entity Recognition logic │ ├── classifier.py # Clause risk classification │ └── summarizer.py # Contract summarization with Legal-BERT ├── extractors/ │ ├── init.py │ ├── pdf_extractor.py # PDF text extraction │ └── docx_extractor.py # DOCX text extraction ├── data/ │ └── database.sqlite # SQLite database for storing extracted data ├── temp/ # Temporary file storage ├── logs/ │ └── contract_analyzer.log # Application logs ├── requirements.txt # Python dependencies ├── setup.py # Package setup script └── README.md # This file
Error Handling The application includes robust error handling for:
Invalid file types or sizes Text extraction failures Model prediction errors Database operation issues File upload problems
Logging Logs are written to logs/contract_analyzer.log, capturing:
Application startup/shutdown File processing events Model operations Error messages
Contributing
Fork the repository. Create a feature branch (git checkout -b feature/your-feature). Commit your changes (git commit -m "Add your feature"). Push to the branch (git push origin feature/your-feature). Create a Pull Request.
License This project is licensed under the MIT License - see the LICENSE file for details. Acknowledgments Developed by CothonSolutions. Special thanks to the open-source community for providing tools like SpaCy, Scikit-learn, HuggingFace, and Streamlit.