Refactor sandbox module

Author: jenniferjiangkells

healthchain/__init__.py

@@ -4,7 +4,6 @@
 from .utils.logger import add_handlers
 from .config.base import ConfigManager, ValidationLevel
 
-from .sandbox.decorator import sandbox, api, ehr
 
 # Enable deprecation warnings
 warnings.filterwarnings("always", category=DeprecationWarning, module="healthchain")
@@ -30,4 +29,14 @@ def __getattr__(name):
         from healthchain.sandbox import generators
 
         return generators
+    elif name == "use_cases":
+        warnings.warn(
+            "Importing use_cases from healthchain is deprecated. "
+            "Use 'from healthchain.sandbox import use_cases' instead.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
+        from healthchain.sandbox import use_cases
+
+        return use_cases
     raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

healthchain/sandbox/__init__.py

@@ -1,34 +1,34 @@
-# New simplified API
-from .sandbox_client import SandboxClient
+import warnings
+
+from .sandboxclient import SandboxClient
 from .datasets import DatasetRegistry, DatasetLoader, list_available_datasets
 
-# Import loaders to trigger auto-registration
 
-# Legacy decorators and classes (deprecated)
-from .decorator import sandbox, api, ehr
-from .environment import SandboxEnvironment
-from .use_cases import (
-    ClinicalDecisionSupport,
-    ClinicalDocumentation,
-    CdsRequestConstructor,
-    ClinDocRequestConstructor,
-)
-from .clients import EHRClient
+# Import loaders to trigger auto-registration
 
 __all__ = [
-    # New API
     "SandboxClient",
     "DatasetRegistry",
     "DatasetLoader",
     "list_available_datasets",
-    # Legacy API (deprecated)
-    "sandbox",
-    "api",
-    "ehr",
-    "SandboxEnvironment",
-    "ClinicalDecisionSupport",
-    "ClinicalDocumentation",
-    "CdsRequestConstructor",
-    "ClinDocRequestConstructor",
-    "EHRClient",
 ]
+
+
+def __getattr__(name):
+    deprecated_names = [
+        "sandbox",
+        "api",
+        "ehr",
+        "ClinicalDecisionSupport",
+        "ClinicalDocumentation",
+    ]
+
+    if name in deprecated_names:
+        warnings.warn(
+            f"{name} is deprecated and has been removed. "
+            f"Use SandboxClient instead.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
+        raise AttributeError(f"{name} has been removed")
+    raise AttributeError(f"module 'healthchain.sandbox' has no attribute '{name}'")

healthchain/sandbox/base.py

@@ -1,20 +1,22 @@
 from abc import ABC, abstractmethod
-from typing import Dict, List, Optional
+from typing import Dict
+from enum import Enum
 
-from healthchain.sandbox.workflows import UseCaseType, Workflow
+from healthchain.models.hooks.prefetch import Prefetch
+from healthchain.sandbox.workflows import Workflow
 
 
-class BaseClient(ABC):
-    """Base client class
-    A client can be an EHR or CPOE etc.
-    The basic operation is that it sends data in a specified standard.
+class ApiProtocol(Enum):
     """
+    Enum defining the supported API protocols.
 
-    @abstractmethod
-    def send_request(self) -> None:
-        """
-        Sends a request to AI service
-        """
+    Available protocols:
+    - soap: SOAP protocol
+    - rest: REST protocol
+    """
+
+    soap = "SOAP"
+    rest = "REST"
 
 
 class BaseRequestConstructor(ABC):
@@ -30,40 +32,38 @@ def construct_request(self, data, workflow: Workflow) -> Dict:
         pass
 
 
-class BaseUseCase(ABC):
+class DatasetLoader(ABC):
     """
-    Abstract base class for healthcare use cases in the sandbox environment.
+    Abstract base class for dataset loaders.
 
-    This class provides a foundation for implementing different healthcare use cases
-    such as Clinical Decision Support (CDS) or Clinical Documentation (NoteReader).
-    Subclasses must implement the type and strategy properties.
+    Subclasses should implement the load() method to return Prefetch data
+    from their specific dataset source.
     """
 
-    def __init__(
-        self,
-        client: Optional[BaseClient] = None,
-    ) -> None:
-        self._client: BaseClient = client
+    @abstractmethod
+    def load(self, **kwargs) -> Prefetch:
+        """
+        Load dataset and return as Prefetch object.
 
-        self.responses: List[Dict[str, str]] = []
-        self.sandbox_id = None
-        self.url = None
+        Args:
+            **kwargs: Loader-specific parameters
 
-    @property
-    @abstractmethod
-    def type(self) -> UseCaseType:
+        Returns:
+            Prefetch object containing FHIR resources
+
+        Raises:
+            FileNotFoundError: If dataset files are not found
+            ValueError: If dataset parameters are invalid
+        """
         pass
 
     @property
     @abstractmethod
-    def strategy(self) -> BaseRequestConstructor:
+    def name(self) -> str:
+        """Dataset name for registration."""
         pass
 
     @property
-    def path(self) -> str:
-        path = self._path
-        if not path.startswith("/"):
-            path = "/" + path
-        if not path.endswith("/"):
-            path = path + "/"
-        return path
+    def description(self) -> str:
+        """Optional description of the dataset."""
+        return ""

healthchain/sandbox/clients/__init__.py

@@ -5,49 +5,14 @@
 """
 
 import logging
-from abc import ABC, abstractmethod
+
 from typing import Any, Dict, List
 
 from healthchain.models import Prefetch
+from healthchain.sandbox.base import DatasetLoader
 
-log = logging.getLogger(__name__)
-
-
-class DatasetLoader(ABC):
-    """
-    Abstract base class for dataset loaders.
-
-    Subclasses should implement the load() method to return Prefetch data
-    from their specific dataset source.
-    """
-
-    @abstractmethod
-    def load(self, **kwargs) -> Prefetch:
-        """
-        Load dataset and return as Prefetch object.
 
-        Args:
-            **kwargs: Loader-specific parameters
-
-        Returns:
-            Prefetch object containing FHIR resources
-
-        Raises:
-            FileNotFoundError: If dataset files are not found
-            ValueError: If dataset parameters are invalid
-        """
-        pass
-
-    @property
-    @abstractmethod
-    def name(self) -> str:
-        """Dataset name for registration."""
-        pass
-
-    @property
-    def description(self) -> str:
-        """Optional description of the dataset."""
-        return ""
+log = logging.getLogger(__name__)
 
 
 class DatasetRegistry: