JSON to Python & Pydantic Converter
Convert raw JSON payloads into production-ready Pydantic v2 models, standard library @dataclass, or TypedDict with datetime & UUID inference.
100% Zero-Egress Privacy
Never upload sensitive user data or AI prompt schemas to ad-heavy remote converters. Nazamos runs 100% locally in your browser memory.
Pydantic v2 & FastAPI Ready
Generates modern Pydantic v2 BaseModel, ConfigDict, and Field(alias=...) attributes for instant use in FastAPI and AI agents.
Modern Python 3.10+ Types
Supports PEP 604 union types (str | None), built-in generics (list[T]), and automatic datetime and UUID detection.
JSON to Python: Type System Mapping
How Nazamos maps dynamic JSON primitives into Python's strict type annotations:
| JSON Data Type | Python Inferred Type | Behavior & Attributes |
|---|---|---|
| String ("hello") | str | Standard Python UTF-8 string |
| Integer (42, -100) | int | Arbitrary-precision integer |
| Float (19.99, 3.14) | float | Standard double-precision floating point |
| Boolean (true / false) | bool | Standard Python bool |
| ISO-8601 Date ("2026-09-20T...") | datetime | Requires `from datetime import datetime` (auto-parsed by Pydantic) |
| UUID ("a0eebc99-...") | UUID | Requires `from uuid import UUID` (auto-validated by Pydantic) |
| Null / Optional Field | T | None / Optional[T] | Defaults to `None` with `Field(default=None)` |
| Homogeneous Array ([1, 2, 3]) | list[int] / list[str] | Built-in generic list |
| Object Array ([{…}, {…}]) | list[ItemModel] | Unified schema model generated for array items |
| Nested Object ({"user": {…}}) | UserModel | Extracted into a standalone PascalCase BaseModel class |
1. FastAPI API Route Validation
Use generated Pydantic models directly as FastAPI request payload schemas:
from fastapi import FastAPI
from pydantic import BaseModel, Field, ConfigDict
from datetime import datetime
# Generated Model from Nazamos
class UserModel(BaseModel):
user_id: int = Field(alias="userId")
user_name: str = Field(alias="userName")
email: str
is_active: bool = Field(alias="isActive")
registered_at: datetime = Field(alias="registeredAt")
model_config = ConfigDict(populate_by_name=True)
app = FastAPI()
@app.post("/users", response_model=UserModel)
async def create_user(payload: UserModel):
# Payload is automatically validated and converted to typed Python attributes
print(f"Creating user: {payload.user_id} - {payload.user_name}")
return payload2. Parsing JSON with Pydantic v2
Parse raw JSON strings or dictionaries directly in backend scripts or AI tool calls:
raw_json_str = '{"userId": 1042, "userName": "alex", "email": "alex@enterprise.com", "isActive": true, "registeredAt": "2026-09-20T10:30:00Z"}'
# 1. Parse directly from raw JSON string (super fast Rust-backed parser)
user = UserModel.model_validate_json(raw_json_str)
print(user.user_name) # Output: alex
# 2. Export back to JSON with original camelCase field names
json_output = user.model_dump_json(by_alias=True)
print(json_output)