Torn.com-ActivityTracker/README.md

# Torn User Activity Tracker

> [!WARNING]  
> **Development is still in its early stages; do not put it to productive use!**

## Features

Multiple users control a single activity tracker using Torn's API.

- Start and stop scraping user activity data
- View real-time logs
- Download data and log files
- View scraping results
- Plugin based analysis system
- Toggle between light and dark mode

**Note:** Many features are not fully implemented yet, but the activity tracker/grabber works as intended.

## Planned Features

- Additional analyses plugins
- Selector for Torn API data to choose which data shall be tracked
- log viewer

## Requirements

- Python 3.8+
- Flask
- Flask-Bootstrap
- Flask-WTF
- Pandas
- Requests
- Redis
- Celery
- uWSGI

Redis currently has to run locally, but this will be changed in the future. See file tasks.py:

```python
# tasks.py
def get_redis():
    return redis.StrictRedis(
        host='localhost',
        port=6379,
        db=0,
        decode_responses=True
    )
```

## Installation

### Docker

#### Prerequisites
- Docker
- Docker Compose

#### Steps to Deploy

1. Clone the repository:
```bash
git clone <repository-url>
cd TornActivityTracker
```

2. Configure environment variables:
- Copy the example .env file and modify if needed
```bash
cp .env.example .env
```

3. Build and start the containers:
```bash
docker-compose up -d --build
```

This will start:
- The main Flask application
- Redis for task queue management
- Nginx as reverse proxy

The application will be available at `http://localhost:80`

#### Maintenance

To view logs:
```bash
docker-compose logs -f
```

To stop the application:
```bash
docker-compose down
```

To rebuild and restart:
```bash
docker-compose up -d --build
```

### Manual

1. Clone the repository:

```sh
git clone https://github.com/MichaelB7/TornActivityTracker.git
cd TornActivityTracker
```

2. Create a virtual environment and activate it:

```sh
python3 -m venv venv
source venv/bin/activate  # On Windows use: .\venv\Scripts\activate
```

3. Install the required packages:

```sh
pip install -r requirements.txt
```

4. Start Redis server locally:
```sh
redis-server
```

5. Set up your configuration:
Create a `config.ini` file in the root directory by copying `example_config.ini`:

```sh
cp example_config.ini config.ini
```

Then edit `config.ini` with your settings:

```ini
[DEFAULT]
SECRET_KEY = your_secret_key
API_KEY = your_api_key
# ...rest of the config settings...
```

6. Start the Celery worker:
```sh
celery -A app.celery_worker worker --loglevel=info
```

7. Run the Flask application:
```sh
flask run
```

The application will be available at `http://127.0.0.1:5000/`

## Adding an Analysis Module

This guide explains how to add a new analysis module using the provided base classes: `BasePlotlyAnalysis` and `BasePlotAnalysis`. These base classes ensure a structured workflow for data preparation, transformation, and visualization.

### 1. Choosing the Right Base Class
Before implementing an analysis module, decide on the appropriate base class:
- **`BasePlotlyAnalysis`**: Use this for interactive plots with **Plotly** that generate **HTML** outputs.
- **`BasePlotAnalysis`**: Use this for static plots with **Matplotlib/Seaborn** that generate **PNG** image files.
- **`BaseAnalysis`**: Use this for any other type of analysis with **text** or **HTML** output for max flexibility.

### 2. Naming Convention
Follow a structured naming convention for consistency:
- **File name:** `plotly_<analysis_name>.py` for Plotly analyses, `plot_<analysis_name>.py` for Matplotlib-based analyses.
- **Class name:** Use PascalCase and a descriptive suffix:
  - Example for Plotly: `PlotlyActivityHeatmap`
  - Example for Matplotlib: `PlotUserSessionDuration`

### 3. Data Structure
The following DataFrame structure is passed to analysis classes:

| user_id  | name       | last_action          | status | timestamp                   | prev_timestamp | was_active | hour |
|----------|-----------|----------------------|--------|-----------------------------|----------------|------------|------|
| XXXXXXX  | UserA     | 2025-02-08 17:58:11  | Okay   | 2025-02-08 18:09:41.867984056 | NaT            | False      | 18   |
| XXXXXXX  | UserB     | 2025-02-08 17:00:10  | Okay   | 2025-02-08 18:09:42.427846909 | NaT            | False      | 18   |
| XXXXXXX  | UserC     | 2025-02-08 16:31:52  | Okay   | 2025-02-08 18:09:42.823201895 | NaT            | False      | 18   |
| XXXXXXX  | UserD     | 2025-02-06 23:57:24  | Okay   | 2025-02-08 18:09:43.179914951 | NaT            | False      | 18   |
| XXXXXXX  | UserE     | 2025-02-06 06:33:40  | Okay   | 2025-02-08 18:09:43.434650898 | NaT            | False      | 18   |

Note that the first X rows, depending on the number of the members, will always contain empty values in prev_timestamp as there has to be a previous timestamp ....

### 4. Implementing an Analysis Module
Each analysis module should define two key methods:
- `transform_data(self, df: pd.DataFrame) -> pd.DataFrame`: Processes the input data for plotting.
- `plot_data(self, df: pd.DataFrame)`: Generates and saves the plot.

#### Example: Adding a Plotly Heatmap
Below is an example of how to create a new analysis module using `BasePlotlyAnalysis`.

```python
import pandas as pd
import plotly.graph_objects as go
from .basePlotlyAnalysis import BasePlotlyAnalysis

class PlotlyActivityHeatmap(BasePlotlyAnalysis):
    """
    Displays user activity trends over multiple days using an interactive heatmap.
    """
    name = "Activity Heatmap (Interactive)"
    description = "Displays user activity trends over multiple days."
    plot_filename = "activity_heatmap.html"

    def transform_data(self, df: pd.DataFrame) -> pd.DataFrame:
        df['hour'] = df['timestamp'].dt.hour
        active_counts = df[df['was_active']].pivot_table(
            index='name', 
            columns='hour', 
            values='was_active', 
            aggfunc='sum', 
            fill_value=0
        ).reset_index()
        return active_counts.melt(id_vars='name', var_name='hour', value_name='activity_count')

    def plot_data(self, df: pd.DataFrame):
        df = df.pivot(index='name', columns='hour', values='activity_count').fillna(0)
        self.fig = go.Figure(data=go.Heatmap(
            z=df.values, x=df.columns, y=df.index, colorscale='Viridis',
            colorbar=dict(title='Activity Count')
        ))
        self.fig.update_layout(title='User Activity Heatmap', xaxis_title='Hour', yaxis_title='User')
```

#### Example: Adding a Static Matplotlib Plot
Below is an example of a Matplotlib-based analysis module using `BasePlotAnalysis`.

```python
import pandas as pd
import matplotlib.pyplot as plt
from .basePlotAnalysis import BasePlotAnalysis

class PlotUserSessionDuration(BasePlotAnalysis):
    """
    Displays a histogram of user session durations.
    """
    name = "User Session Duration Histogram"
    description = "Histogram of session durations."
    plot_filename = "session_duration.png"

    def transform_data(self, df: pd.DataFrame) -> pd.DataFrame:
        df['session_duration'] = (df['last_action'] - df['timestamp']).dt.total_seconds()
        return df

    def plot_data(self, df: pd.DataFrame):
        plt.figure(figsize=(10, 6))
        plt.hist(df['session_duration'].dropna(), bins=30, edgecolor='black')
        plt.xlabel('Session Duration (seconds)')
        plt.ylabel('Frequency')
        plt.title('User Session Duration Histogram')
```

## License

All assets and code are under the  [CC BY-SA 4.0](http://creativecommons.org/licenses/by-sa/4.0/) LICENSE and in the public domain unless specified otherwise.