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* feat: Enhance annotation module with template management and validation - Added DatasetMappingCreateRequest and DatasetMappingUpdateRequest schemas to handle dataset mapping requests with camelCase and snake_case support. - Introduced Annotation Template schemas including CreateAnnotationTemplateRequest, UpdateAnnotationTemplateRequest, and AnnotationTemplateResponse for managing annotation templates. - Implemented AnnotationTemplateService for creating, updating, retrieving, and deleting annotation templates, including validation of configurations and XML generation. - Added utility class LabelStudioConfigValidator for validating Label Studio configurations and XML formats. - Updated database schema for annotation templates and labeling projects to include new fields and constraints. - Seeded initial annotation templates for various use cases including image classification, object detection, and text classification. * feat: Enhance TemplateForm with improved validation and dynamic field rendering; update LabelStudio config validation for camelCase support * feat: Update docker-compose.yml to mark datamate dataset volume and network as external * feat: Add tag configuration management and related components - Introduced new components for tag selection and browsing in the frontend. - Added API endpoint to fetch tag configuration from the backend. - Implemented tag configuration management in the backend, including loading from YAML. - Enhanced template service to support dynamic tag rendering based on configuration. - Updated validation utilities to incorporate tag configuration checks. - Refactored existing code to utilize the new tag configuration structure. * feat: Refactor LabelStudioTagConfig for improved configuration loading and validation * feat: Update Makefile to include backend-python-docker-build in the build process * feat: Migrate to poetry for better deps management * Add pyyaml dependency and update Dockerfile to use Poetry for dependency management - Added pyyaml (>=6.0.3,<7.0.0) to pyproject.toml dependencies. - Updated Dockerfile to install Poetry and manage dependencies using it. - Improved layer caching by copying only dependency files before the application code. - Removed unnecessary installation of build dependencies to keep the final image size small. * feat: Remove duplicated backend-python-docker-build target from Makefile * fix: airflow is not ready for adding yet * feat: update Python version to 3.12 and remove project installation step in Dockerfile
31 lines
860 B
TOML
31 lines
860 B
TOML
[project]
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name = "datamate-python"
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version = "0.1.0"
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description = "This is the Python backend of DataMate."
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authors = [
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{name = "Jason Wang",email = "jasonwong2019@outlook.com"}
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]
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license = {text = "MIT"}
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readme = "README.md"
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requires-python = ">=3.12"
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dependencies = [
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"fastapi (>=0.121.1,<0.122.0)",
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"uvicorn[standard] (>=0.38.0,<0.39.0)",
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"sqlalchemy (>=2.0.44,<3.0.0)",
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"aiomysql (>=0.3.2,<0.4.0)",
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"pymysql (>=1.1.2,<2.0.0)",
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"aiosqlite (>=0.21.0,<0.22.0)",
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"httpx (>=0.28.1,<0.29.0)",
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"pydantic (>=2.12.4,<3.0.0)",
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"pydantic-settings (>=2.12.0,<3.0.0)",
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"python-multipart (>=0.0.20,<0.0.21)",
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"python-dotenv (>=1.2.1,<2.0.0)",
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"python-dateutil (>=2.9.0.post0,<3.0.0)",
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"pyyaml (>=6.0.3,<7.0.0)"
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]
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[build-system]
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requires = ["poetry-core>=2.0.0,<3.0.0"]
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build-backend = "poetry.core.masonry.api"
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