Func Annotator is a simple Python tool that generates natural language annotations for Python functions using a plain-text prompt. The annotation describes the function's input, processing, and output in clear, concise English.

• Generates structured, plain-English documentation for Python functions.
• Outputs annotation in three parts: Input, Processing, Output.
• Simple to run locally or in Docker.
• Designed for clarity—no Markdown or extraneous formatting.

1. Clone the repository and install dependencies:

   git clone <your-repo-url>
   cd <repo>
   pip install -r requirements.txt
   

2. Run the annotator (interactive mode):

   python app/func_annotator.py
   

   Paste your function when prompted, end your input with 'end'(case ignored) in a new line to get result.

3. Batch annotation (process all function samples):

   python app/batch_annotator.py
   

   This will process all functions in feedings/ and save annotated results to outputs/.

4. Run the test:

   pytest tests/test.py
   

You can also use Docker for easy setup and execution:

# 构建 Docker 镜像
docker build -t func-annotator .

# 运行标注工具（需要挂载配置文件）
docker run --rm -v $(pwd)/docker.env:/app/config/docker.env func-annotator

# 运行测试
docker run --rm -v $(pwd)/docker.env:/app/config/docker.env func-annotator pytest tests/test.py -v

The tool uses .env to store model info and credentials:

API_KEY=YOUR_API_KEY
API_URL=YOUR_API_URL
MODEL_NAME=YOUR_MODEL_NAME

The tool uses prompt_template.txt to modify prompt sending to models.

This project uses DVC to version control function samples in the feedings/ directory. This allows you to track changes to your training data and collaborate with others.

1. Install DVC (already included in requirements.txt):

   pip install -r requirements.txt

2. Initialize DVC manually:

   # Initialize DVC repository
   dvc init
   
   # Add function sample files to version control
   dvc add feedings/function_sample1.py
   dvc add feedings/function_sample2.py

   This will create .dvc files that track your function samples and add them to DVC cache.

# View current status
dvc status

# Commit changes
dvc commit -m "Updated function samples"

# Push to remote storage
dvc push

# Pull latest version
dvc pull

# View history
dvc list .

# Show differences
dvc diff

• function_sample1.py - Arithmetic and mathematical functions (20 functions)
  • Arithmetic operations, geometry, statistics, linear algebra
• function_sample2.py - Text processing functions (20 functions)
  • String operations, text analysis, data formatting, encryption

• debugged errors in Dockerfile.
• added error captures in func_annotator.py.
• attached readme.md.
• added cache files in .gitignore and .dockerignore.

• added model performance traced by integrating mlflow & dagshub.
• restructured project with app/ and tests/ directories.
• enhanced error handling and monitoring metrics.

• integrated DVC (Data Version Control) for managing function samples.
• added version control for feedings/ function sample files.
• reorganized project structure with docs/ directory.
• added batch annotation script (app/batch_annotator.py) for processing multiple functions at once.