Forecast Dashboard

A Streamlit-based dashboard for visualizing route forecast data, including overall route counts, weekly forecasts, and detailed tables by region.

Features

• Interactive Filters: Select regions and time ranges to filter data.
• Visualizations:
  • Bar chart for overall route counts (Actual, Lower, Forecast, Upper).
  • Combined bar and line chart for weekly forecasts with upper and lower bounds.
• Tables:
  • Route counts over time by region.
  • Total route counts per region.
• Export: Download weekly forecast data as a CSV file.

Directory Structure

  Root/
  ├── Dockerfile
  ├── docker-compose.yml
  ├── requirements.txt
  ├── README.md
  └── src/
  ├── init.py
  ├── app.py
  ├── config.py
  ├── data/
  │   ├── init.py
  │   ├── loader.py
  │   └── preprocessor.py
  ├── utils/
  │   ├── init.py
  │   └── export.py
  └── visualization/
  ├── init.py
  ├── charts.py
  └── tables.py

Installation

Prerequisites

• Docker (optional, for containerized deployment)
• Python 3.9+

Setup

1. Clone the repository:

   git clone <repository-url>
   cd root

2. Install dependencies:

   pip install -r requirements.txt

3. Ensure the report directory exists with your data file (results_fi_fu.csv by default):

   root/src/report/results_fi_fu.csv

Running the Dashboard

Locally

Run the Streamlit app directly:

cd src
streamlit run app.py

Access the dashboard at http://localhost:8501.

Using Docker Compose

1. Build and run the container:

docker-compose up --build

Access the dashboard at http://localhost:8501.

2. Environment Variables

• FORECAST_FILE_NAME: Specifies the CSV file name (default: results_fi_fu).
  • Set in docker-compose.yml or your shell:

export FORECAST_FILE_NAME=your_file_name

Development

• Adding New Features

  • Add new visualization functions in src/visualization/.
  • Add utility functions in src/utils/.
  • Update src/app.py to include new components in the dashboard.

• Live Changes

  • When using Docker Compose, the src/ directory is mounted, so local changes are reflected automatically in the container.

Dependencies

See requirements.txt for a full list of dependencies.

License This project is licensed under the MIT License.

Steps to Run

1. Create the directory structure as shown above.

2. Save each file with its respective content.

3. Ensure the report directory exists inside src/ with your data file (results_fi_fu.csv).

4. Install dependencies:

   pip install -r requirements.txt

5. Run the app:

• Locally:

  cd src
  streamlit run app.py

• With Docker Compose:

  docker-compose up --build

6. Access the dashboard at http://localhost:8501.