Slack Reports

Application for generating reports of messages and most active users in Slack channels, with AI-powered thread summarization.

Project Structure

.
├── backend/           # FastAPI Server (app)
├── frontend/          # React Application
└── README.md

Cómo Ejecutar la Aplicación

Levantar el Frontend

1. Navegar al directorio del frontend:

   cd frontend

2. Instalar dependencias:

   npm install
   # o
   yarn install

3. Iniciar el servidor de desarrollo:

   npm start
   # o
   yarn start

4. Acceder a la aplicación:

   • La aplicación estará disponible en http://localhost:3000
   • Asegúrate de que el backend esté ejecutándose en http://localhost:5000

Levantar el Backend

1. Configurar variables de entorno (ver sección Backend para detalles)

2. Ejecutar con Docker:

   docker-compose up --build

3. El backend estará disponible en http://localhost:5000

Frontend

Prerequisites

• Node.js (version 14 or higher)
• npm or yarn

Installation

1. Navigate to the frontend directory:

cd frontend

2. Install dependencies:

npm install
# or
yarn install

Main Dependencies

• React 18
• TypeScript
• Tailwind CSS
• Heroicons (for icons)
• React Router (for navigation)

Frontend Structure

frontend/
├── src/
│   ├── components/         # Reusable components
│   │   ├── Sidebar.tsx     # Navigation sidebar
│   │   ├── ReportForm.tsx  # Report generation form
│   │   └── TaskStatus.tsx  # Generated reports status
│   ├── types/             # TypeScript definitions
│   │   └── index.ts       # Interfaces and types
│   └── App.tsx            # Main component
├── package.json           # Dependencies and scripts
└── tsconfig.json          # TypeScript configuration

Configuration

1. Ensure the backend is running at http://localhost:5000
2. If the backend is at a different URL, update the API_URL constant in App.tsx

Running the Application

To start the development server:

npm start
# or
yarn start

The application will be available at http://localhost:3000

Features

1. Report Generation

   • Select report type (messages or top repliers)
   • Enter channel ID
   • Specify date range
   • For top repliers, indicate how many users to show

2. Thread Summarization (NEW)

   • Generate AI-powered summaries of Slack threads
   • Support for OpenAI and Ollama LLM providers
   • Input thread timestamp and channel ID

3. Report Tracking

   • View status of generated reports
   • Manually update status
   • Download results in CSV or JSON format

Main Components

1. Sidebar

   • Navigation between "Generate Reports" and "Report Status"
   • Responsive and user-friendly design

2. ReportForm

   • Intuitive form for report generation
   • Field validation
   • Report type selection with icons

3. TaskStatus

   • Display of each report's status
   • Update and download options
   • Detailed report information

Styling

• Uses Tailwind CSS for styling
• Responsive design
• Light and dark themes
• Heroicons for icons

Development

For local development:

1. Clone the repository
2. Install dependencies
3. Start the development server

Backend

Prerequisites

Make sure you have the following components installed:

• Python 3.8+
• Docker
• Docker Compose
• Redis (for Celery)
• Slack API Token
• OpenAI API Key (optional, for OpenAI summarization)
• Ollama (optional, for local LLM summarization)

Installation and Configuration

1. Clone the repository:

   git clone https://github.com/pamelars86/slack-reports.git
   cd slack-reports

2. Create a .env file in the root of the project with the following content:

   # Slack Configuration
   SLACK_TOKEN=your_slack_api_token
   SLACK_HOME="https://your-organization.slack.com"
   
   # Redis Configuration
   CELERY_BROKER_URL="redis://redis:6379/0"
   result_backend="redis://redis:6379/0"
   
   # OpenAI Configuration (optional)
   OPENAI_API_KEY=your_openai_api_key
   OPENAI_MODEL=gpt-3.5-turbo
   
   # Ollama Configuration (optional)
   OLLAMA_HOST=http://localhost:11434
   OLLAMA_MODEL=llama3

3. Build and start the Docker containers:

   docker-compose up --build

4. Start the server:

   docker-compose up

New AI Summarization Features

Thread Summarization

The application now supports AI-powered summarization of Slack threads using either OpenAI or Ollama:

• OpenAI Integration: Uses GPT models for high-quality summaries
• Ollama Integration: Uses local LLM models for privacy-focused summarization
• Configurable Prompts: Prompts are stored in app/prompts.yml for easy customization
• Flexible Architecture: Abstract interface allows easy addition of new LLM providers

LLM Architecture

• LLMInterface: Abstract base class for all LLM implementations
• OpenAILLM: OpenAI API implementation
• OllamaLLM: Ollama local model implementation
• LLMFactory: Factory pattern for creating appropriate LLM instances
• Prompts stored in YAML for easy modification without code changes

Usage of Endpoints

To use the endpoints for generating reports, follow these steps:

1. Fetch Messages: Use the /fetch-messages endpoint to initiate the process of fetching messages from Slack. This operation is asynchronous and will return a task-id.

2. Top Repliers: Use the /top-repliers endpoint to generate a report of the top repliers in your Slack workspace. This operation is also asynchronous and will return a task-id.

3. Thread Summarization (NEW): Use the /summarize-thread endpoint to generate AI summaries of Slack threads:

   {
     "channel_id": "C1234567890",
     "thread_ts": "1748458889.115369",
     "llm_provider": "openai"
   }

4. Check Task Status: To check the status of your task, use the /task-status/{task-id} endpoint. Replace {task-id} with the actual task ID you received from the previous endpoints.

For detailed information on the input and output of these endpoints, refer to the Swagger documentation available at http://localhost:5000/apidocs/.

API Documentation

You can find the API documentation in Swagger by accessing the following URL once the server is running:

http://localhost:5000/apidocs/

Usage

To generate reports and summaries, make sure all services are running and use the endpoints documented in Swagger.

Contributions

Contributions are welcome. Please open an issue or a pull request to discuss any changes you would like to make.

License

This project is licensed under the MIT License. See the LICENSE file for more details.