smartptf

This project now uses uv for dependency and environment management.

Prerequisites

• Python 3.13
• uv installed on your machine

Install Dependencies

From the project root, use one of these commands:

uv sync                              # Simple app execution
uv sync --dev                        # Development tools (tests, deptry, etc.)
uv sync --extra ai                  # DRL training dependencies
uv sync --all-extras --all-groups   # Everything

These commands create or update the local virtual environment in .venv.

Run the Streamlit App

From the project root, run:

uv run streamlit run src/app.py

Streamlit should open automatically in your browser (typically at http://localhost:8501).

AI Training and TensorBoard

To train the DRL model, make sure you installed AI dependencies first:

uv sync --extra ai

Training logs are written under models/DPT/logs by the DPT training script.

Run training with uv:

uv run python src/models/DPT/AI_CLILearning.py

Then launch TensorBoard:

uv run tensorboard --logdir=models/DPT/logs

Open the URL shown by TensorBoard (typically http://localhost:6006).

Useful Commands

# Sync dependencies after pyproject.toml changes
uv sync

# Run tests
uv run pytest