Files
livegraphs-django/README.md
Kaj Kowalski 6b19cbcb51 Add configuration and scripts for linting, testing, and dependency management
- Introduced .pre-commit-config.yaml for pre-commit hooks using uv-pre-commit.
- Created lint.sh script to run Ruff and Black for linting and formatting.
- Added test.sh script to execute tests with coverage reporting.
- Configured .uv file for uv settings including lockfile management and dependency resolution.
- Updated Makefile with targets for virtual environment setup, dependency installation, linting, testing, formatting, and database migrations.
- Established requirements.txt with main and development dependencies for the project.
2025-05-17 20:18:21 +02:00

5.0 KiB

Chat Analytics Dashboard

A Django application that creates an analytics dashboard for chat session data. The application allows different companies to have their own dashboards and view their own data.

Features

  • Multi-company support with user authentication
  • CSV file upload and processing
  • Interactive dashboard with charts and visualizations
  • Detailed data views for chat sessions
  • Search functionality to find specific chat sessions
  • Admin interface for managing users and companies
  • Responsive design using Bootstrap 5

Requirements

  • Python 3.13+
  • Django 5.2+
  • UV package manager (recommended)
  • Other dependencies listed in pyproject.toml

Setup

Local Development

  1. Clone the repository:

    git clone <repository-url>
    cd LiveGraphsDjango
    
  2. Install uv if you don't have it yet:

    # Install using pip
    pip install uv
    
    # Or with curl (Unix/macOS)
    curl -sSf https://install.pypa.io/get-uv.py | python3 -
    
    # Or on Windows with PowerShell
    irm https://install.pypa.io/get-uv.ps1 | iex
    
  3. Create a virtual environment and activate it:

    uv venv
    source .venv/bin/activate  # On Windows: .venv\Scripts\activate
    
  4. Install dependencies:

    # Install all dependencies including dev dependencies
    uv pip install -e ".[dev]"
    
    # Or just runtime dependencies
    uv pip install -e .
    
  5. Run migrations:

    cd dashboard_project
    python manage.py makemigrations
    python manage.py migrate
    
  6. Create a superuser:

    python manage.py createsuperuser
    
  7. Run the development server:

    python manage.py runserver
    
  8. Access the application at http://127.0.0.1:8000/

Development Workflow with UV

UV offers several advantages over traditional pip, including faster dependency resolution and installation:

  1. Running linting and formatting:

    # Using the convenience script
    ./.scripts/lint.sh
    
    # Or directly
    uv run -m ruff check dashboard_project
    uv run -m ruff format dashboard_project
    uv run -m black dashboard_project
    
  2. Running tests:

    # Using the convenience script
    ./.scripts/test.sh
    
    # Or directly
    uv run -m pytest
    
  3. Adding new dependencies:

    # Add to project
    uv pip install package_name
    
    # Then update pyproject.toml manually
    # And update the lockfile
    uv pip freeze > requirements.lock
    
  4. Updating the lockfile:

    uv pip compile pyproject.toml -o uv.lock
    

Using Docker

  1. Clone the repository:

    git clone <repository-url>
    cd dashboard_project
    
  2. Build and run with Docker Compose:

    docker-compose up -d --build
    
  3. Create a superuser:

    docker-compose exec web python manage.py createsuperuser
    
  4. Access the application at http://localhost/

Usage

  1. Login as the superuser you created.
  2. Go to the admin interface (http://localhost/admin/) and create companies and users.
  3. Assign users to companies.
  4. Upload CSV files for each company.
  5. View the analytics dashboard.

CSV File Format

The CSV file should contain the following columns:

Column Description
session_id Unique identifier for the chat session
start_time When the session started (datetime)
end_time When the session ended (datetime)
ip_address IP address of the user
country Country of the user
language Language used in the conversation
messages_sent Number of messages in the conversation (integer)
sentiment Sentiment analysis of the conversation (string)
escalated Whether the conversation was escalated (boolean)
forwarded_hr Whether the conversation was forwarded to HR (boolean)
full_transcript Full transcript of the conversation (text)
avg_response_time Average response time in seconds (float)
tokens Total number of tokens used (integer)
tokens_eur Cost of tokens in EUR (float)
category Category of the conversation (string)
initial_msg First message from the user (text)
user_rating User rating of the conversation (string)

Future Enhancements

  • API integration for real-time data
  • More advanced visualizations
  • Custom reports
  • Export functionality
  • Theme customization
  • User access control with more granular permissions

License

This project is unlicensed. Usage is restricted to personal and educational purposes only. For commercial use, please contact the author.