pyproject.toml

[build-system]
requires = ["hatchling>=1.27"]
build-backend = "hatchling.build"

[project]
name = "bridgelink-asl"
version = "0.2.0"
description = "Real-time ASL word recognition with MediaPipe landmarks and a Transformer classifier."
readme = "README.md"
requires-python = ">=3.10"
license = { text = "MIT" }
authors = [
  { name = "Dalen Gordon", email = "dgordo34@charlotte.edu" },
  { name = "Ervin Gordon III", email = "egordo17@charlotte.edu" },
  { name = "Frank Garcia", email = "fgarci11@charlotte.edu" },
  { name = "Omar Fraij", email = "ofraij@charlotte.edu" },
]
dependencies = [
  "gradio>=4.44",
  "numpy>=1.26,<2",
  "opencv-contrib-python==4.11.0.86",
  "mediapipe==0.10.14",
  "protobuf>=3.20,<5",
  "torch>=2.2",
  "huggingface_hub>=0.24",
]

[project.optional-dependencies]
speech = ["pyttsx3>=2.90"]
cloud = ["elevenlabs>=1.7.0"]
training = ["tensorflow>=2.16,<2.20", "matplotlib>=3.8"]
vlm = [
  "transformers>=4.51,<5",
  "accelerate>=0.30",
  "safetensors>=0.4",
]
dev = ["pytest>=8.3"]

[project.scripts]
bridgelink-demo = "bridgelink_asl.cli.run_demo:main"
run_demo = "bridgelink_asl.cli.run_demo:main"
train_model = "bridgelink_asl.cli.train_model:main"
evaluate_model = "bridgelink_asl.cli.evaluate_model:main"
train_cnn_model = "bridgelink_asl.cli.train_cnn_model:main"
run_wrapper = "bridgelink_asl.cli.run_wrapper:main"

[tool.hatch.build.targets.wheel]
packages = ["src/bridgelink_asl"]