Grammar Correction & Paraphrasing System

Project Structure

grammar-paraphrase-system/
├── README.md
├── requirements.txt
├── .env.template
├── .gitignore
├── docker-compose.yml
├── Dockerfile
│
├── src/
│   ├── __init__.py
│   │
│   ├── domain/
│   │   ├── __init__.py
│   │   ├── entities/
│   │   │   ├── __init__.py
│   │   │   ├── text_request.py
│   │   │   └── text_response.py
│   │   ├── services/
│   │   │   ├── __init__.py
│   │   │   ├── grammar_service.py
│   │   │   └── paraphrase_service.py
│   │   └── interfaces/
│   │       ├── __init__.py
│   │       ├── model_interface.py
│   │       └── text_processor_interface.py
│   │
│   ├── infrastructure/
│   │   ├── __init__.py
│   │   ├── models/
│   │   │   ├── __init__.py
│   │   │   ├── model_loader.py
│   │   │   ├── flan_t5_model.py
│   │   │   └── model_config.py
│   │   ├── cache/
│   │   │   ├── __init__.py
│   │   │   └── redis_cache.py
│   │   └── monitoring/
│   │       ├── __init__.py
│   │       └── performance_monitor.py
│   │
│   ├── application/
│   │   ├── __init__.py
│   │   ├── use_cases/
│   │   │   ├── __init__.py
│   │   │   ├── correct_grammar_use_case.py
│   │   │   └── paraphrase_text_use_case.py
│   │   └── dto/
│   │       ├── __init__.py
│   │       ├── grammar_request_dto.py
│   │       └── paraphrase_request_dto.py
│   │
│   ├── presentation/
│   │   ├── __init__.py
│   │   ├── api/
│   │   │   ├── __init__.py
│   │   │   ├── main.py
│   │   │   ├── routers/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── grammar_router.py
│   │   │   │   └── paraphrase_router.py
│   │   │   └── middleware/
│   │   │       ├── __init__.py
│   │   │       └── rate_limiting.py
│   │   └── cli/
│   │       ├── __init__.py
│   │       └── demo_cli.py
│   │
│   └── config/
│       ├── __init__.py
│       ├── settings.py
│       └── prompts.py
│
├── tests/
│   ├── __init__.py
│   ├── unit/
│   ├── integration/
│   └── performance/
│
├── scripts/
│   ├── setup_environment.py
│   ├── download_model.py
│   ├── run_demo.py
│   └── benchmark_model.py
│
├── data/
│   ├── sample_texts/
│   ├── fine_tuning/ (for future fine-tuning data)
│   └── evaluation/
│
├── models/ (downloaded models will be stored here)
│
└── logs/

Step-by-Step Implementation Plan

Phase 1: Foundation Setup (Day 1)

1. Environment Setup

   # Create project
   mkdir grammar-paraphrase-system
   cd grammar-paraphrase-system
   
   # Setup Python environment
   python -m venv venv
   source venv/bin/activate  # or venv\Scripts\activate on Windows

2. Install Dependencies

   pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
   pip install transformers accelerate sentencepiece protobuf
   pip install fastapi uvicorn pydantic
   pip install python-dotenv loguru

3. Create Core Domain Entities

   • Text processing contracts
   • Request/Response models
   • Service interfaces

Phase 2: Model Integration (Day 2)

1. Model Infrastructure

   • Download and configure FLAN-T5
   • GPU memory management
   • Model loading optimization

2. Domain Services

   • Grammar correction service
   • Paraphrasing service
   • Prompt engineering

Phase 3: Application Layer (Day 3)

1. Use Cases Implementation

   • Grammar correction workflow
   • Paraphrasing workflow
   • Error handling and validation

2. CLI Demo

   • Interactive command-line interface
   • Batch processing capabilities

Phase 4: API Layer (Day 4)

1. REST API

   • FastAPI implementation
   • Request validation
   • Response formatting

2. Performance Optimization

   • Caching layer
   • Batch processing
   • Memory management

Phase 5: Production Ready (Day 5)

1. Containerization

   • Docker setup
   • Environment configuration

2. Monitoring & Logging

   • Performance metrics
   • Error tracking

System Prompts for Grammar Correction & Paraphrasing

Grammar Correction Prompt

You are a professional grammar correction assistant. Your task is to correct grammatical errors, spelling mistakes, and punctuation issues while preserving the original meaning and style.

Instructions:
- Fix grammatical errors (subject-verb agreement, tense consistency, etc.)
- Correct spelling mistakes
- Fix punctuation and capitalization
- Maintain the original tone and style
- Keep the meaning exactly the same
- If the text is already correct, return it unchanged

Input: {input_text}
Corrected text:

Paraphrasing Prompt

You are a professional paraphrasing assistant. Your task is to rewrite the given text while maintaining the same meaning but using different words and sentence structures.

Instructions:
- Preserve the exact meaning and key information
- Use different vocabulary and sentence structures
- Maintain appropriate tone and formality level
- Ensure the paraphrased text is clear and natural
- Keep similar length unless specified otherwise

Input: {input_text}
Paraphrased text:

Key Configuration Files

requirements.txt

torch>=2.0.0
transformers>=4.30.0
accelerate>=0.20.0
sentencepiece>=0.1.99
protobuf>=3.20.0
fastapi>=0.100.0
uvicorn>=0.22.0
pydantic>=2.0.0
python-dotenv>=1.0.0
loguru>=0.7.0
redis>=4.5.0
numpy>=1.24.0

.env.template

# Model Configuration
MODEL_NAME=google/flan-t5-large
MODEL_CACHE_DIR=./models
MAX_LENGTH=512
BATCH_SIZE=4

# API Configuration
API_HOST=0.0.0.0
API_PORT=8000
DEBUG=True

# Hardware Configuration
DEVICE=cuda
GPU_MEMORY_FRACTION=0.8

# Cache Configuration
REDIS_URL=redis://localhost:6379
CACHE_TTL=3600

Getting Started Commands

1. Setup Project Structure

   python scripts/setup_environment.py

2. Download Model

   python scripts/download_model.py

3. Run Demo

   python scripts/run_demo.py

4. Start API Server

   uvicorn src.presentation.api.main:app --reload --host 0.0.0.0 --port 8000

Next Steps for Fine-tuning (Future)

1. Prepare training dataset with input-output pairs
2. Use Hugging Face's Trainer for fine-tuning
3. Implement LoRA (Low-Rank Adaptation) for efficient fine-tuning
4. Create evaluation metrics and validation pipeline

This architecture follows SOLID principles with clear separation of concerns, dependency injection, and is easily testable and maintainable.