README.md

---
title: ClauseGuard
emoji: 🛡️
colorFrom: gray
colorTo: gray
sdk: gradio
sdk_version: "5.23.0"
python_version: "3.12"
app_file: app.py
pinned: false
---

# 🛡️ ClauseGuard v4.0 — World's Best Open-Source Legal Contract Analysis

**ClauseGuard** is the most comprehensive open-source AI-powered legal contract analysis tool. It analyzes contracts using state-of-the-art legal NLP models and provides actionable risk assessments, Q&A chatbot, clause redlining, and OCR for scanned PDFs.

## 🆕 What's New in v4.0

| Feature | Description |
|---------|-------------|
| **🔍 OCR for Scanned PDFs** | Smart PDF router: auto-detects native vs scanned PDFs. Scanned PDFs are processed via docTR OCR engine (CPU-friendly, ~150MB models) |
| **💬 Contract Q&A Chatbot** | RAG-powered chatbot that answers questions about your analyzed contract. Uses sentence-transformers for retrieval + Qwen2.5-7B via HF Inference API for generation |
| **✏️ Clause Redlining** | 3-tier system: (1) Template lookup from 18+ legal templates based on FTC/EU standards, (2) Keyword-based matching, (3) LLM refinement for CRITICAL/HIGH risk clauses |

## ✨ Core Features

### Analysis Engine
| Feature | Description |
|---------|-------------|
| **41 CUAD Clause Categories** | Full taxonomy: Document Name, Parties, Governing Law, Indemnification, Termination, Non-Compete, IP Ownership, Audit Rights, Force Majeure, and more |
| **4-Tier Risk Scoring** | Critical 🔴 / High 🟠 / Medium 🟡 / Low 🟢 with visual risk matrix |
| **Legal NER** | Extracts parties, dates, monetary values ($), jurisdictions, defined terms, and party roles |
| **NLI Contradiction Detection** | Identifies conflicting clauses (e.g., uncapped + capped liability) and missing critical provisions |
| **Obligation Tracker** | Categorizes action items: monetary 💰, compliance ⚖️, reporting 📊, delivery 📦, termination 🛑 |
| **Compliance Checker** | Validates against GDPR, CCPA, SOX, HIPAA, and FINRA requirements |
| **Contract Comparison** | Side-by-side diff between two contracts with alignment scoring |
| **Clause Redlining** | Suggests safer alternatives for risky clauses with legal citations |
| **Q&A Chatbot** | Ask questions about your contract using RAG (Retrieval-Augmented Generation) |
| **OCR Support** | Process scanned PDFs with docTR OCR engine |

### Document Support
- **PDF** parsing via `pdfplumber` (native) + `docTR` OCR (scanned)
- **DOCX/DOC** parsing via `python-docx`
- **TXT / Markdown** direct text input

### UI/UX
- **3-Panel Professional Layout** — Upload sidebar + Main analysis + Summary dashboard
- **Document Viewer** — Inline entity highlights (colored annotations)
- **Clause Cards** — Expandable risk-badged cards with confidence scores
- **Redlining Tab** — Side-by-side original vs suggested safer alternatives
- **Q&A Chat Tab** — Conversational interface to ask questions about the contract
- **Export Reports** — JSON (structured) and CSV (tabular) downloads
- **Color-Coded Risk Badges** — Instant visual triage

## 🧠 Models & Architecture

| Component | Technology |
|-----------|------------|
| Clause Classification | `Mokshith31/legalbert-contract-clause-classification` — LoRA adapter on `nlpaueb/legal-bert-base-uncased`, fine-tuned on CUAD 41-class taxonomy |
| Legal NER | `matterstack/legal-bert-ner` (ML) with regex fallback for 7 entity types |
| NLI | `cross-encoder/nli-deberta-v3-base` (semantic contradiction detection) |
| Embeddings | `sentence-transformers/all-MiniLM-L6-v2` (384-dim, RAG retrieval) |
| LLM | `Qwen/Qwen2.5-7B-Instruct` via HF Inference API (chatbot + redlining) |
| OCR | `docTR` (fast_base + crnn_vgg16_bn) for scanned PDF text extraction |
| Compliance | Regulatory keyword matching across GDPR, CCPA, SOX, HIPAA, FINRA |
| Comparison | Semantic similarity with sentence embeddings + string matching fallback |
| Obligations | Regex pattern matching across 5 obligation categories |

## 🔍 OCR Architecture (Smart PDF Router)

```
PDF uploaded
    ↓
[detect_if_scanned] — pdfplumber extracts >50 chars/page?
    ↓                           ↓
  Native PDF               Scanned PDF
    ↓                           ↓
  pdfplumber              docTR OCR (CPU)
    ↓                           ↓
  Contract text → existing analysis pipeline
```

## 💬 Q&A Chatbot Architecture (RAG)

```
User asks question about their contract
        ↓
[1] Embed question with all-MiniLM-L6-v2
        ↓
[2] Retrieve top-5 most relevant chunks from contract
        ↓
[3] Build prompt:
    - System: ClauseGuard analysis results (clauses, entities, risk scores)
    - Context: Retrieved contract chunks (≤2.5K tokens)
    - User question
        ↓
[4] Stream response from Qwen2.5-7B via HF Inference API
```

**Key design:** Analyzed data (clauses, entities, risk scores) goes in the system prompt — NOT through RAG retrieval. Only the raw contract text goes through RAG. This gives the model both structured analysis AND verbatim evidence.

## ✏️ Clause Redlining Architecture (3-Tier)

| Tier | Method | Speed | Hallucination Risk |
|------|--------|-------|--------------------|
| **1. Template Lookup** | 18+ pre-written safe alternatives based on FTC/EU/CFPB standards | Instant | Zero |
| **2. Keyword Matching** | Match clause text to relevant templates via legal keywords | Instant | Zero |
| **3. LLM Refinement** | Qwen2.5-7B adapts template to specific clause context | ~3-5s | Low (template-anchored) |

Anti-hallucination guardrails:
- **Template anchor:** LLM can only refine, not generate from scratch
- **Legal citation:** Every suggestion includes legal basis and consumer standard
- **Disclaimer:** Clear "Not legal advice" warning

## 📊 Risk Scoring Methodology

Risk scores combine clause detection with weighted severity:
- **CRITICAL**: 40 pts (Uncapped Liability, Arbitration, IP Assignment, etc.)
- **HIGH**: 20 pts (Non-Compete, Exclusivity, Unilateral Change, etc.)
- **MEDIUM**: 10 pts (Governing Law, Jurisdiction, etc.)
- **LOW**: 3 pts (Document Name, Dates, etc.)

Final score normalized to 0-100 with letter grades:
- A (0-14): Low risk
- B (15-29): Moderate risk
- C (30-49): Elevated risk
- D (50-69): High risk
- F (70+): Critical risk

## 🚀 Usage

1. **Upload** a contract (PDF, DOCX, or TXT) or paste text directly
   - 💡 Scanned PDFs are automatically processed with OCR
2. Click **Analyze Contract**
3. View results across tabs:
   - **Document**: Full text with inline entity highlights
   - **Clauses**: Detected clauses with risk badges
   - **Entities**: Extracted parties, dates, money, jurisdictions
   - **Contradictions**: Conflicting clauses and missing provisions
   - **Obligations**: Action items categorized by type
   - **Compliance**: Regulatory framework checks
   - **Redlining**: ✏️ Safer clause alternatives with legal citations
4. **Export** JSON/CSV reports
5. Switch to **💬 Contract Q&A** tab to ask questions about your contract

## 🔀 Compare Contracts

Switch to the **Compare Contracts** tab to:
- Upload or paste two contracts side-by-side
- See clause-level diffs (added, removed, modified)
- Get an alignment score and risk delta

## ⚠️ Disclaimer

*Not legal advice. ClauseGuard is an AI-powered analysis tool for informational purposes only. Always consult a qualified attorney for legal decisions. The tool may miss nuances and should be used as a preliminary screening aid, not a substitute for professional legal review.*

## 🔗 Links

- [ClauseGuard Space](https://huggingface.co/spaces/gaurv007/ClauseGuard)
- [Clause Classifier Model](https://huggingface.co/Mokshith31/legalbert-contract-clause-classification)
- [Legal-BERT Base](https://huggingface.co/nlpaueb/legal-bert-base-uncased)
- [CUAD Dataset](https://huggingface.co/datasets/theatticusproject/cuad-qa)
- [Qwen2.5-7B (Chatbot LLM)](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)
- [docTR OCR](https://github.com/mindee/doctr)
- [CUAD Paper (arXiv:2103.06268)](https://arxiv.org/abs/2103.06268)

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*Built with ❤️ using Gradio, Hugging Face Transformers, and Legal-BERT. Open source and free for all.*