-
Notifications
You must be signed in to change notification settings - Fork 7
Expand file tree
/
Copy pathmodels.py
More file actions
76 lines (59 loc) · 2 KB
/
models.py
File metadata and controls
76 lines (59 loc) · 2 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
from __future__ import annotations
from datetime import datetime # noqa: TC003 - Required at runtime for Pydantic field validation
from enum import Enum
from pydantic import BaseModel, Field
class TaskState(str, Enum):
SCHEDULED = "SCHEDULED"
PENDING = "PENDING"
RUNNING = "RUNNING"
COMPLETED = "COMPLETED"
FAILED = "FAILED"
CANCELLED = "CANCELLED"
CRASHED = "CRASHED"
PAUSED = "PAUSED"
CANCELLING = "CANCELLING"
class TaskLog(BaseModel):
message: str
severity: str
timestamp: datetime
class TaskRelatedNode(BaseModel):
id: str
kind: str
class Task(BaseModel):
id: str
title: str
state: TaskState
progress: float | None = None
workflow: str | None = None
branch: str | None = None
# start_time: datetime # Is it still required
created_at: datetime
updated_at: datetime
parameters: dict | None = None
tags: list[str] | None = None
related_nodes: list[TaskRelatedNode] = Field(default_factory=list)
logs: list[TaskLog] = Field(default_factory=list)
@classmethod
def from_graphql(cls, data: dict) -> Task:
related_nodes: list[TaskRelatedNode] = []
logs: list[TaskLog] = []
if "related_nodes" in data:
if data.get("related_nodes"):
related_nodes = [TaskRelatedNode(**item) for item in data["related_nodes"]]
del data["related_nodes"]
if "logs" in data:
if data.get("logs"):
logs = [TaskLog(**item["node"]) for item in data["logs"]["edges"]]
del data["logs"]
return cls(**data, related_nodes=related_nodes, logs=logs)
class TaskFilter(BaseModel):
ids: list[str] | None = None
q: str | None = None
branch: str | None = None
state: list[TaskState] | None = None
workflow: list[str] | None = None
limit: int | None = None
offset: int | None = None
related_node__ids: list[str] | None = None
def to_dict(self) -> dict:
return self.model_dump(exclude_none=True)