json2pyi infers a type schema from a sample JSON file and generates Python type definitions (dataclass, Pydantic BaseModel or PEP-589 TypedDict) accordingly. It runs in browser, requiring no installation.
🌐 Available online 🔗: https://json2pyi.pages.dev
Input:
Output:
@dataclass
class Page:
id = str
name = str
url = str
time_zone = str
updated_at = str
@dataclass
class Components:
id = str
name = str
status = str
created_at = str
updated_at = str
position = int
description = Optional[str]
showcase = bool
start_date = None
group_id = None
page_id = str
group = bool
only_show_if_degraded = bool
@dataclass
class Status:
indicator = str
description = str
@dataclass
class RootObject:
page = Page
components = List[Components]
incidents = List[Any]
scheduled_maintenances = List[Any]
status = StatusOr:
from typing import TypedDict, Optional, List
from datetime import datetime
IncidentUpdate = TypedDict("IncidentUpdate", {"body": str, "created_at": datetime, "display_at": datetime, "id": str, "incident_id": str, "status": str, "updated_at": datetime})
IncidentOrScheduledMaintenance = TypedDict("IncidentOrScheduledMaintenance", {"created_at": datetime, "id": str, "impact": str, "incident_updates": List[IncidentUpdate], "monitoring_at": None, "name": str, "page_id": str, "resolved_at": None, "shortlink": str, "status": str, "updated_at": datetime, "scheduled_for": Optional[datetime], "scheduled_until": Optional[datetime]})
Component = TypedDict("Component", {"created_at": datetime, "description": None, "id": str, "name": str, "page_id": str, "position": int, "status": str, "updated_at": datetime})
Status = TypedDict("Status", {"description": str, "indicator": str})
Page = TypedDict("Page", {"id": str, "name": str, "url": str, "updated_at": datetime})
RootObject = TypedDict("UnnammedType3C2BC8", {"page": Page, "status": Status, "components": List[Component], "incidents": List[IncidentOrScheduledMaintenance], "scheduled_maintenances": List[IncidentOrScheduledMaintenance]})- Detect tuple (array)
- Detect UUID / datetime
- Detect Enum
- Merge data types with similar structure and common name prefix/suffix
- Detect recursive type definition (e.g. tree)
- Include imports of non-primitive types
- Generate type alias for complex Union
- Improve the logic of determining whether a Union ix complex or not
- Generate TypedDict
-
Refactor to unify TypedDict and dataclass generationSeperated intendedly for clear code structure. - Compile to WASM and provide a Web-based app
- Allow to tweak more options on Web app (partially blocked by vhiribarren/raytracer-rust#8)
- Avoid merging data types with totally different structures in a union
- Avoid unnecessary heap allocation by reducing one-time usage of Vec
- Allow specifying the order of generated data types
- Support more input types, such as JSON Schema
- Support more target languages
- Add usage instructions
The project is inspired by:
{ "page": { "id": "kctbh9vrtdwd", "name": "GitHub", "url": "https://www.githubstatus.com", "time_zone": "Etc/UTC", "updated_at": "2020-12-03T08:11:21.385Z" }, "components": [ { "id": "8l4ygp009s5s", "name": "Git Operations", "status": "operational", "created_at": "2017-01-31T20:05:05.370Z", "updated_at": "2020-10-29T22:51:43.831Z", "position": 1, "description": "Performance of git clones, pulls, pushes, and associated operations", "showcase": false, "start_date": null, "group_id": null, "page_id": "kctbh9vrtdwd", "group": false, "only_show_if_degraded": false }, /* ... */ ], "incidents": [], "scheduled_maintenances": [], "status": { "indicator": "none", "description": "All Systems Operational" } }