JIRA CSV Generator

A modular application for extracting actionable tasks and Q&A items from meeting transcripts and generating JIRA-compatible CSV files using local AI processing.

Features

• Meeting Transcript Analysis: Extract actionable tasks and questions from meeting transcripts
• Local AI Processing: Uses Ollama for private, local AI processing (no data sent to cloud services)
• JIRA CSV Export: Generate JIRA-compatible CSV files for easy import
• Web Interface: User-friendly web interface with progress tracking
• Modular Architecture: Clean, testable code following best practices

Quick Start

Prerequisites

• Python 3.8+
• Ollama installed and running locally
• Llama 3.1 model downloaded in Ollama

Installation

1. Clone the repository:

git clone <repository-url>
cd jira_project

2. Install dependencies:

pip install -r requirements.txt

3. Start Ollama and ensure the model is available:

ollama pull llama3.1:latest
ollama serve

4. Run the application:

python app.py

5. Open your browser and navigate to http://localhost:5000

Architecture

Project Structure

jira_project/
├── src/
│   ├── api/                 # API routes and handlers
│   ├── config/              # Configuration management
│   ├── models/              # Data models (JiraTask, QAItem)
│   ├── services/            # Business logic services
│   ├── utils/               # Utility functions and classes
│   └── exceptions.py        # Custom exception classes
├── tests/
│   ├── unit/                # Unit tests
│   ├── integration/         # Integration tests
│   └── conftest.py          # Pytest configuration
├── templates/               # Web interface templates
├── static/                  # Static assets (CSS, JS)
└── docs/                    # Documentation

Core Components

Services

• TranscriptAnalysisService: Orchestrates transcript processing
• OllamaService: Handles AI communication with Ollama
• CSVGenerationService: Generates JIRA-compatible CSV files

Models

• JiraTask: Represents a JIRA task with validation
• QAItem: Represents a question-answer pair with context

Configuration

• Environment-based configuration management
• Configurable AI service parameters
• Processing limits and validation rules

API Endpoints

Health Check

• GET /api/health - Service health status

Transcript Processing

• POST /api/parse-transcript - Extract tasks only
• POST /api/extract-qa - Extract Q&A only
• POST /api/process-enhanced - Extract both tasks and Q&A

CSV Generation

• POST /api/generate-csv - Generate CSV file from tasks

Service Status

• GET /api/status - Get service status and configuration

Configuration

Configuration can be set via environment variables:

# Application settings
DEBUG=false
HOST=127.0.0.1
PORT=5000
SECRET_KEY=your-secret-key

# Ollama settings
OLLAMA_MODEL=llama3.1:latest
OLLAMA_URL=http://localhost:11434
OLLAMA_TIMEOUT=60

# Processing limits
MAX_TASKS=10
MAX_QUESTIONS=8
MAX_TRANSCRIPT_LENGTH=50000

# JIRA settings
DEFAULT_REPORTER=meeting@example.com

Testing

Run the test suite:

# Run all tests
pytest

# Run unit tests only
pytest tests/unit/

# Run with coverage
pytest --cov=src

# Run specific service tests
pytest tests/unit/test_mcp_jira_service.py -v
pytest tests/unit/test_context_aware_ai_service.py -v
pytest tests/unit/test_smart_duplicate_service.py -v

Test Coverage

• 157+ total tests covering all services
• Model Validation: 22 tests
• CSV Generation: 12 tests
• Context Service: 11 tests
• Export Service: 14 tests
• Cache Service: 12 tests
• Document Service: 19 tests
• MCP JIRA Service: 21 tests ⭐ NEW
• Context-Aware AI: 22 tests ⭐ NEW
• Smart Duplicate Detection: 31 tests ⭐ NEW

See TEST_COVERAGE_SUMMARY.md for detailed test documentation.

Development

Setting up development environment

1. Install development dependencies:

pip install -r requirements-dev.txt

2. Run tests before committing:

pytest

3. Format code:

black src/ tests/

Adding new features

1. Create tests first (TDD approach)
2. Implement the feature following the existing patterns
3. Update documentation as needed
4. Ensure all tests pass

Security Considerations

• All AI processing is done locally using Ollama
• No transcript data is sent to external services
• Input validation prevents malicious data processing
• Configurable processing limits prevent resource exhaustion

Troubleshooting

Common Issues

1. Ollama connection failed

   • Ensure Ollama is running: ollama serve
   • Check if the model is available: ollama list
   • Verify the OLLAMA_URL configuration

2. No tasks extracted

   • Check transcript content (minimum 10 words)
   • Ensure transcript contains actionable items
   • Try the iterative extraction mode

3. CSV generation fails

   • Verify task data has required fields (summary)
   • Check for validation errors in logs
   • Ensure proper email format for reporter field

Logs

Application logs provide detailed information about:

• AI service communication
• Task extraction process
• Validation errors
• Performance metrics

Contributing

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Ensure all tests pass
5. Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.