feat: add labeling template. refactor: switch to Poetry, build and deploy of backend Python (#79)

* 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

runtime/datamate-python/app/module/annotation/service/mapping.py

@@ -83,7 +83,7 @@ class DatasetMappingService:
         labeling_project: LabelingProject
     ) -> DatasetMappingResponse:
         """创建数据集映射"""
-        logger.info(f"Create dataset mapping: {labeling_project.dataset_id} -> {labeling_project.labeling_project_id}")
+        logger.debug(f"Create dataset mapping: {labeling_project.dataset_id} -> {labeling_project.labeling_project_id}")
         
         # Use the passed object directly
         self.db.add(labeling_project)
@@ -201,7 +201,7 @@ class DatasetMappingService:
         )
         await self.db.commit()
         
-        if result.rowcount > 0:
+        if result.rowcount and result.rowcount > 0:  # type: ignore
             return await self.get_mapping_by_uuid(mapping_id)
         return None
     
@@ -219,7 +219,7 @@ class DatasetMappingService:
         )
         await self.db.commit()
         
-        success = result.rowcount > 0
+        success = result.rowcount and result.rowcount > 0  # type: ignore
         if success:
             logger.info(f"Mapping soft-deleted: {mapping_id}")
         else: