User Story Generator Application

A Flask-based web application that simplifies the life of product owners by automatically generating user stories from requirements using Large Language Models (LLMs).

Features

• LLM Integration: Connect to any OpenAI-compatible LLM endpoint
• User Story Generation: Automatically create user stories following INVEST criteria
• Modern UI: Clean, responsive interface with a two-column layout
• Copy to Clipboard: One-click copy functionality for generated stories
• Loading Indicators: Visual feedback with spinning wheel during story generation
• Error Handling: Comprehensive error handling and user feedback

Requirements

• Python 3.8 or higher
• pip (Python package installer)

Installation

1. Clone the repository (or navigate to the project directory):

   cd flask

2. Create a virtual environment:

   python3 -m venv venv

3. Activate the virtual environment:

   # On Linux/Mac:
   source venv/bin/activate
   
   # On Windows:
   venv\Scripts\activate

4. Install dependencies:

   pip install .

Running the Application

1. Activate the virtual environment (if not already activated):

   source venv/bin/activate

2. Start the Flask server:

   python app.py

3. Open your browser and navigate to:

   http://localhost:5000
   

The application will run in debug mode, so any code changes will automatically reload.

Usage

1. Configure LLM Settings:

   • Enter your LLM endpoint URL (e.g., https://openrouter.ai/api/v1/chat/completions)
   • Enter the model name (e.g., qwen/qwen3-30b-a3b-thinking-2507)
   • Enter your API key

2. Enter Requirements:

   • Type or paste your requirements in the "Requirement" field

3. Generate Story:

   • Click the "Create Story" button
   • Wait for the story to be generated (a spinner will indicate progress)

4. Copy Story:

   • Once generated, click the "Copy" button above the Story field to copy it to your clipboard

Project Structure

flask/
├── app.py                 # Main Flask application
├── prompts.py            # User story prompt template
├── pyproject.toml       # Project metadata and dependencies
├── README.md             # This file
├── .gitignore           # Git ignore rules
├── templates/
│   └── index.html       # Main HTML template
├── static/
│   ├── css/
│   │   └── style.css    # Application styles
│   └── js/
│       └── main.js      # Frontend JavaScript
└── venv/                # Virtual environment (not in git)

API Endpoints

POST /api/create-story

Generates a user story from a requirement using an LLM.

Request Body:

{
  "llmEndpoint": "https://api.openai.com/v1/chat/completions",
  "model": "gpt-4",
  "apiKey": "your-api-key",
  "requirement": "User requirement text here"
}

Success Response (200):

{
  "story": "Generated user story text..."
}

Error Response (400/500):

{
  "error": "Error message description"
}

Configuration

LLM Endpoint Compatibility

The application is designed to work with OpenAI-compatible APIs. The expected request format is:

{
  "model": "model-name",
  "messages": [
    {
      "role": "user",
      "content": "prompt text"
    }
  ],
  "temperature": 0.7
}

The expected response format should include:

• choices[0].message.content (OpenAI format), or
• content (alternative format)

Prompt Template

The user story prompt template is defined in prompts.py and follows INVEST criteria:

• Independent
• Negotiable
• Valuable
• Estimable
• Small
• Testable

Dependencies

• Flask (3.0.0): Web framework
• Werkzeug (3.0.1): WSGI utilities
• requests (2.31.0): HTTP library for LLM API calls

Development

The application runs in debug mode by default. To disable debug mode, modify app.py:

app.run(debug=False, host='0.0.0.0', port=5000)

Troubleshooting

Port Already in Use

If port 5000 is already in use, you can change it in app.py:

app.run(debug=True, host='0.0.0.0', port=8080)

LLM API Errors

• Verify your API endpoint URL is correct
• Check that your API key is valid
• Ensure the model name matches your LLM provider's available models
• Check network connectivity

Import Errors

Make sure all dependencies are installed:

pip install .

License

This project is open source and available for use.

Contributing

Contributions are welcome! Please feel free to submit a pull request.