README.md

Java Code Optimizer

A web-based interface for optimizing Java code using a fine-tuned CodeT5-small model.

Overview

This application provides a user-friendly web interface to optimize Java code using a fine-tuned Salesforce/codet5-small model. The model has been trained on Java optimization tasks and can transform verbose or inefficient Java code into more optimal versions.

Features

• 🚀 Real-time Optimization: Get instant optimization suggestions for your Java code
• 🎯 Specialized Model: Fine-tuned specifically for Java code optimization tasks
• 💻 GPU/CPU Support: Automatically utilizes GPU if available, falls back to CPU
• 🎨 Modern Interface: Clean, responsive design with dark/light mode support
• 📚 Example Cases: Pre-loaded examples to demonstrate optimization capabilities
• 🔍 Health Monitoring: Real-time status of model loading and device utilization

Model Information

• Base Model: Salesforce/codet5-small
• Training Data: ~6K training / 680 validation Java optimization pairs
• Framework: HuggingFace Transformers with Seq2SeqTrainer
• Optimization Focus: Java code refactoring and performance improvements

Installation & Setup

Prerequisites

• Python 3.8+
• Git
• Internet connection (for initial model loading)

Step-by-Step Instructions

1. Clone/Navigate to the Project Directory

   cd ~/model/java_optimizer
   

2. Activate the Virtual Environment

   source ~/Python/.venv/bin/activate
   

3. Install Required Dependencies

   pip install -r requirements.txt
   

4. Verify Model Files Exist The application expects the fine-tuned model to be in /home/ssp/model/codet5-fast/. You should see files like:

   • config.json
   • model.safetensors
   • tokenizer_config.json
   • vocab.json
   • merges.txt

5. Run the Application

   python app.py
   

6. Access the Web Interface Open your web browser and navigate to:

   http://localhost:5000
   

Usage

1. Enter Java Code: Type or paste your Java code into the input textarea
2. Click Optimize: Press the "⚡ Optimize Code" button
3. View Results: The optimized code will appear in the output textarea
4. Try Examples: Click on any of the pre-loaded examples to test the optimizer

Example Optimizations

The model has been trained to recognize and optimize common Java patterns:

• Switch Expressions: Converting verbose switch statements to switch expressions
• Collection Operations: Replacing manual iterator removal with removeIf()
• String Handling: Optimizing string concatenation with StringBuilder
• And more...

API Endpoints

GET /

• Returns the main HTML interface

POST /optimize

• Request Body: { "code": "your Java code here" }
• Response:

  {
    "original": "input Java code",
    "optimized": "optimized Java code"
  }
  

• Error Response: { "error": "error message" }

GET /health

• Returns application health status including model loading state and device info

GET /model-info

• Returns information about the model files and size

Troubleshooting

Common Issues

1. Model Not Loading

   • Verify the model directory /home/ssp/model/codet5-fast/ exists
   • Check that all required files are present
   • Ensure you have sufficient disk space and memory

2. CUDA/GPU Issues

   • The application will automatically fall back to CPU if GPU is unavailable
   • To force CPU usage, modify the DEVICE variable in app.py

3. Port Already in Use

   • Change the port in app.py (line with app.run())
   • Or stop the existing process using port 5000

4. Dependencies Missing

   • Run pip install torch transformers flask manually
   • Check Python version compatibility

Performance Notes

• First optimization may be slower due to model loading
• Subsequent requests are faster as model stays in memory
• GPU acceleration significantly improves inference speed
• Model size is approximately 240MB

Development

File Structure

java_optimizerő
├── app.py                 # Main Flask application
├── requirements.txt       # Python dependencies
├── test_model.py         # Model testing script
└── templates/
    └── index.html        # Frontend interface

Making Changes

1. Modify app.py for backend logic changes
2. Update templates/index.html for UI changes
3. Adjust model parameters in app.py if needed
4. Restart the application after changes

License

This project is provided for educational and demonstration purposes.

Acknowledgements

• Model based on Salesforce/codet5-small
• Built with Flask and HuggingFace Transformers
• Inspired by Java optimization best practices# DeepLearning