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AI-Powered Plagiarism Detection System

A comprehensive plagiarism detection system that combines multiple similarity algorithms, web search integration, and AI-powered suggestions to analyze text documents for potential plagiarism. Built with Python using a single-agent LangGraph workflow architecture and featuring a modern web interface.

Overview

This system provides multi-layered plagiarism detection through:

  • Statistical Analysis: Jaccard similarity and TF-IDF cosine similarity
  • Semantic Analysis: Sentence embeddings for contextual similarity
  • Web Verification: Google Custom Search integration for online source checking
  • AI Enhancement: Integrated OpenAI API for content improvement
  • Automated Workflow: Single LangGraph agent with state management for reliable processing

Architecture

Agent-Based Design

The system uses 1 primary LangGraph agent (PlagiarismDetectionAgent) that manages the entire workflow through state transitions and conditional routing. This single-agent architecture ensures consistent state management and simplified error handling across the entire pipeline.

Workflow DAG (Directed Acyclic Graph)

Plagiarism Detection Workflow

Place the workflow diagram image as workflow_dag.png in your repository root directory

The workflow follows a directed acyclic graph pattern with the following processing stages:

  1. parse_input: Entry point that validates and categorizes input data
  2. extract_text: File processing for PDF, DOCX, and TXT formats
  3. preprocess_text: Text cleaning, tokenization, and chunking
  4. detect_plagiarism: Core similarity analysis using multiple algorithms
  5. analyze_results: Results processing and metric extraction
  6. generate_suggestions: AI-powered improvement recommendations (conditional based on plagiarism score)
  7. create_report: Final report generation and formatting
  8. handle_error: Error handling and recovery with multiple entry points

The diagram shows the complete workflow with:

  • Solid arrows: Direct sequential flow
  • Dotted arrows: Conditional routing based on decision points
  • 3 decision points: File type routing, error handling, and suggestion generation threshold

Core Components

Detection Engine (plagiarism_detector.py)

  • Multiple similarity algorithms (Jaccard, TF-IDF, Semantic)
  • Text preprocessing and chunking
  • Web search integration
  • OpenAI API integration for suggestions

Single Agent Workflow (langgraph_plagiarism_agent.py)

  • LangGraph-based state management with single agent
  • Conditional routing and error handling
  • File format support (PDF, DOCX, TXT)
  • Batch processing capabilities

Web Interface (streamlit_plagiarism_app.py)

  • Interactive Streamlit dashboard
  • Real-time analysis visualization
  • Export functionality
  • Progress tracking

Agent Processing Pipeline

Input Text/File → Parse & Validate → Extract Text (if file) → 
Preprocess & Clean → Detect Plagiarism → Analyze Results → 
Generate AI Suggestions (if needed) → Create Final Report

The single agent handles all state transitions, conditional branching, and error recovery through the LangGraph framework.

🚀 Features

Detection Methods

  • Jaccard Similarity: Word overlap analysis between texts
  • TF-IDF Cosine Similarity: Document vector comparison
  • Semantic Similarity: Context-aware similarity using sentence transformers
  • Web Search Verification: Compare against online sources via Google API

File Support

  • PDF Files: Automatic text extraction using PyPDF2
  • Word Documents: DOCX file processing with python-docx
  • Plain Text: Direct text file reading
  • Raw Text Input: Direct text paste functionality

AI Features

  • Smart Suggestions: OpenAI-powered rewriting recommendations
  • Context Awareness: Suggestions tailored to specific flagged content
  • Multiple Options: Various paraphrasing and restructuring approaches

User Interface

  • Real-time Dashboard: Interactive web interface with live updates
  • Visual Analytics: Plotly charts showing similarity scores and source distribution
  • Batch Processing: Analyze multiple documents simultaneously
  • Export Options: JSON, CSV, and formatted text reports

📋 Requirements

System Requirements

  • Python 3.8 or higher
  • Internet connection (for web search and AI features)
  • 2GB RAM minimum (4GB recommended)

Dependencies

streamlit>=1.28.0
plotly>=5.15.0
pandas>=2.0.0
openai>=1.3.0
sentence-transformers>=2.2.0
scikit-learn>=1.3.0
nltk>=3.8.0
langgraph>=0.0.40
PyPDF2>=3.0.0
python-docx>=0.8.11
requests>=2.31.0
python-dotenv>=1.0.0

⚙️ Installation

1. Clone Repository

git clone <repository-url>
cd plagiarism-detection-system

2. Create Virtual Environment

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

3. Install Dependencies

pip install -r requirements.txt

4. Download NLTK Data

python -c "import nltk; nltk.download('punkt'); nltk.download('punkt_tab'); nltk.download('stopwords')"

5. Configure API Keys

Create a .env file in the project root:

# Required: OpenAI API Key
OPENAI_API_KEY=your_openai_api_key_here

# Optional: Google Custom Search (recommended for enhanced detection)
GOOGLE_API_KEY=your_google_api_key_here
GOOGLE_CSE_ID=your_custom_search_engine_id_here

Getting API Keys

OpenAI API Key:

  1. Visit OpenAI API Dashboard
  2. Create account and generate API key
  3. Add billing information for usage

Google Custom Search (Optional):

  1. Go to Google Cloud Console
  2. Enable "Custom Search JSON API"
  3. Create API credentials
  4. Set up Custom Search Engine at cse.google.com
  5. Configure to search entire web (* in sites to search)

🏃 Usage

Web Interface (Recommended)

streamlit run streamlit_plagiarism_app.py

Access the application at http://localhost:8501

Command Line Interface

# Analyze text directly
python langgraph_plagiarism_agent.py --text "Your text here" --openai-key "your_key"

# Analyze a file
python langgraph_plagiarism_agent.py --file "document.pdf" --openai-key "your_key"

# Batch processing
python langgraph_plagiarism_agent.py --batch "input_files.json" --openai-key "your_key"

Python API

from plagiarism_detector import PlagiarismDetector

# Initialize detector
detector = PlagiarismDetector(
    openai_api_key="your_openai_key",
    google_api_key="your_google_key",  # Optional
    google_cse_id="your_cse_id"        # Optional
)

# Analyze text
results = detector.analyze_text("Your text to analyze")
print(f"Plagiarism: {results['overall_plagiarism_percentage']:.1f}%")

# Generate report
report = detector.format_report(results)
print(report)

📊 Understanding Results

Plagiarism Scores

  • 0-15%: Low risk (likely original content)
  • 15-30%: Medium risk (requires review)
  • 30%+: High risk (significant similarities found)

Similarity Methods

  • Jaccard: Measures word overlap between texts
  • TF-IDF: Compares document term frequency patterns
  • Semantic: Analyzes contextual meaning similarity

Output Formats

  • JSON: Complete analysis data with metadata
  • CSV: Tabular chunk-by-chunk analysis
  • Text Report: Human-readable formatted summary

🔧 Configuration

Detection Settings

Edit configuration in the web interface sidebar:

  • Chunk Size: Number of sentences per analysis chunk (2-6)
  • Web Search: Enable/disable online source checking
  • Similarity Thresholds: Adjust sensitivity levels
  • Detection Mode: Standard vs Enhanced analysis

Advanced Settings

Modify thresholds in plagiarism_detector.py:

PLAGIARISM_THRESHOLD = 0.5  # Consider as plagiarized
WEB_SEARCH_THRESHOLD = 0.3  # Include in web search results
SUGGESTION_THRESHOLD = 20   # Generate suggestions above this percentage

🎨 Workflow Visualization

Generate workflow diagrams:

python graph.py

This creates:

  • Mermaid diagram code (for web visualization)
  • ASCII art workflow chart
  • Processing pipeline overview

🧪 Testing

Run Test Suite

# Test installation
python test_plagiarism.py

# Test OpenAI integration
python debug_suggestions.py

# Test web interface
python test_streamlit.py

Sample Analysis

# Quick test with sample text
python simple_example.py

📁 Project Structure

plagiarism-detection-system/
├── plagiarism_detector.py           # Core detection engine
├── enhanced_plagiarism_detector.py  # Extended detection methods
├── langgraph_plagiarism_agent.py    # LangGraph workflow agent
├── streamlit_plagiarism_app.py      # Web interface
├── graph.py                         # Workflow visualization
├── requirements.txt                 # Python dependencies
├── .env                            # API keys (create this)
├── README.md                       # This file
├── test_scripts/
│   ├── test_plagiarism.py          # Installation tests
│   ├── debug_suggestions.py        # OpenAI debugging
│   └── simple_example.py           # Quick start example
└── docs/
    ├── setup_guide.md              # Detailed setup instructions
    └── api_documentation.md        # API reference

🔍 Troubleshooting

Common Issues

Import Errors:

pip install --upgrade -r requirements.txt

NLTK Data Missing:

python -c "import nltk; nltk.download('all')"

OpenAI API Errors:

  • Verify API key is correct
  • Check billing status in OpenAI dashboard
  • Monitor rate limits and usage

Google Search Errors:

  • Ensure Custom Search JSON API is enabled
  • Verify CSE ID format (should be alphanumeric with colon, not email)
  • Check API quotas in Google Cloud Console

Streamlit Issues:

streamlit --version
pip install --upgrade streamlit

Performance Tips

For Faster Analysis:

  • Disable web search for local-only checking
  • Use smaller chunk sizes for shorter texts
  • Limit batch processing to 10 documents at once

For Better Accuracy:

  • Enable all detection methods
  • Use both Google and OpenAI integrations
  • Increase chunk overlap for better coverage

Performance Metrics

Single Agent Architecture Benefits

  • State Consistency: Single agent ensures reliable state management across all processing steps
  • Error Recovery: Centralized error handling with multiple recovery paths shown in DAG
  • Memory Efficiency: Single agent instance handles entire workflow without inter-agent communication overhead

Processing Performance

  • Processing Speed: ~1-2 seconds per 1000 words (local algorithms)
  • Web Search: +2-3 seconds per chunk (depends on network)
  • AI Suggestions: +3-5 seconds (depends on OpenAI response time)
  • Memory Usage: ~200MB base + ~50MB per document
  • Accuracy: 85-95% detection rate for academic text similarities
  • Agent Overhead: Minimal due to single-agent design (~10-20ms state transitions)

🤝 Contributing

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

📄 License

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

🙋 Support

For issues, questions, or contributions:

  • Create an issue on GitHub
  • Check the troubleshooting section above
  • Review the setup guide in /docs/setup_guide.md

🎯 Future Enhancements

  • Support for more file formats (RTF, ODT)
  • Integration with academic databases (ArXiv, PubMed)
  • Advanced citation detection
  • Multi-language support
  • REST API for integration
  • Docker containerization
  • Cloud deployment options

Built with ❤️ using Python, OpenAI, LangGraph, and Streamlit

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Plagiarism Detection Application using Google Custom Search Engine API

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