Project import generated by Copybara.

GitOrigin-RevId: aca1015675b80a848e68c639374255f032b4728d

requirements/base.txt

@@ -2,6 +2,7 @@ backports.cached-property==1.0.0.post2
 certifi>=2021.10.8
 click>=8.1.3,<9
 docker==7.1.0
+inflection==0.5.1
 kubernetes>=28.1.0
 opentelemetry-distro>=0.44b0
 opentelemetry-exporter-prometheus>=0.44b0
@@ -10,6 +11,7 @@ pydantic>=2
 python_dateutil>=2.8.0
 pyyaml==6.0.1
 requests>=2.25,<3
+rich==13.7.1
 smart_open>=2.1.0,<6.0
 tabulate==0.8.9
 tenacity==8.2.2

src/gretel_client/inference_api/base.py

@@ -8,7 +8,6 @@
 
 from gretel_client.config import ClientConfig, configure_session, get_session_config
 from gretel_client.rest.api_client import ApiClient
-from gretel_client.rest.configuration import Configuration
 
 MODELS_API_PATH = "/v1/inference/models"
 
@@ -161,7 +160,7 @@ def __init__(
         elif len(session_kwargs) > 0:
             raise ValueError("cannot specify session arguments when passing a session")
 
-        if session.default_runner != "cloud" and not ".serverless." in session.endpoint:
+        if session.default_runner != "cloud" and ".serverless." not in session.endpoint:
             raise GretelInferenceAPIError(
                 "Gretel's Inference API is currently only "
                 "available within Gretel Cloud. Your current runner "

src/gretel_client/navigator/__init__.py

@@ -0,0 +1,2 @@
+from gretel_client.navigator.data_designer.interface import DataDesigner
+from gretel_client.navigator.workflow import NavigatorWorkflow

src/gretel_client/navigator/client/interface.py

@@ -0,0 +1,130 @@
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Generic, Iterator, Optional, Type, TypeVar, Union
+
+import pandas as pd
+
+from gretel_client.projects import Project
+
+
+def get_client(adapter: Union[Type[ClientAdapter], ClientAdapter]) -> Client:
+    if not isinstance(adapter, ClientAdapter):
+        adapter = adapter()
+    return Client(adapter)
+
+
+@dataclass
+class SubmitBatchWorkflowResponse:
+    project: Project
+    workflow_id: str
+    workflow_run_id: str
+
+
+class Client:
+
+    _adapter: ClientAdapter
+
+    def __init__(self, adapter: ClientAdapter):
+        self._adapter = adapter
+
+    def run_task(
+        self,
+        name: str,
+        config: dict,
+        inputs: Optional[list[TaskInput]] = None,
+        globals: Optional[dict] = None,
+        verbose: bool = False,
+    ) -> TaskOutput:
+        if inputs is None:
+            inputs = []
+        if globals is None:
+            globals = {}
+        return self._adapter.run_task(name, config, inputs, globals, verbose)
+
+    def get_workflow_preview(self, workflow_config: dict) -> Iterator:
+        return self._adapter.stream_workflow_outputs(workflow_config)
+
+    def submit_batch_workflow(
+        self,
+        workflow_config: dict,
+        num_records: int,
+        project_name: Optional[str] = None,
+    ) -> SubmitBatchWorkflowResponse:
+        return self._adapter.submit_batch_workflow(
+            workflow_config, num_records, project_name
+        )
+
+    def get_step_output(
+        self,
+        workflow_run_id: str,
+        step_name: str,
+        format: Optional[str] = None,
+    ) -> TaskOutput:
+        return self._adapter.get_step_output(workflow_run_id, step_name, format)
+
+    def download_step_output(
+        self,
+        workflow_run_id: str,
+        step_name: str,
+        output_dir: Path,
+        format: Optional[str] = None,
+    ) -> Path:
+        return self._adapter.download_step_output(
+            workflow_run_id, step_name, output_dir, format
+        )
+
+    def registry(self) -> list[dict]:
+        return self._adapter.registry()
+
+
+TaskInput = TypeVar("TaskInput")
+TaskOutput = Union[pd.DataFrame, dict]
+
+
+class ClientAdapter(ABC, Generic[TaskInput]):
+
+    @abstractmethod
+    def run_task(
+        self,
+        name: str,
+        config: dict,
+        inputs: list[TaskInput],
+        globals: dict,
+        verbose: bool = False,
+    ) -> TaskOutput: ...
+
+    @abstractmethod
+    def stream_workflow_outputs(
+        self, workflow: dict, verbose: bool = False
+    ) -> Iterator[dict]: ...
+
+    @abstractmethod
+    def registry(self) -> list[dict]: ...
+
+    def submit_batch_workflow(
+        self,
+        workflow_config: dict,
+        num_records: int,
+        project_name: Optional[str] = None,
+    ) -> SubmitBatchWorkflowResponse:
+        raise NotImplementedError("Cannot submit batch Workflows")
+
+    def get_step_output(
+        self,
+        workflow_run_id: str,
+        step_name: str,
+        format: Optional[str] = None,
+    ) -> TaskOutput:
+        raise NotImplementedError("Cannot get batch step outputs")
+
+    def download_step_output(
+        self,
+        workflow_run_id: str,
+        step_name: str,
+        output_dir: Path,
+        format: Optional[str] = None,
+    ) -> Path:
+        raise NotImplementedError("Cannot download batch artifacts")