JSON'dan Python'a

from dataclasses import dataclass
from typing import List, Optional
import json

@dataclass
class Address:
    street: str
    city: str
    zip: str

@dataclass
class User:
    id: int
    name: str
    email: str
    active: bool
    score: float
    address: Address
    tags: List[str]
    metadata: Optional[str] = None

raw = '{"id":1,"name":"Alice","email":"alice@example.com","active":true,"score":98.5,"address":{"street":"123 Main St","city":"Springfield","zip":"12345"},"tags":["admin","user"],"metadata":null}'
data = json.loads(raw)

# Reconstruct nested objects manually
addr = Address(**data["address"])
user = User(**{**data, "address": addr})
print(user.name)     # -> Alice
print(user.address)  # -> Address(street='123 Main St', city='Springfield', zip='12345')

// Minimal JSON-to-Python-dataclass generator in JS
function jsonToPython(obj, name = "Root") {
  const classes = [];
  function infer(val, fieldName) {
    if (val === null) return "Optional[str]";
    if (typeof val === "string") return "str";
    if (typeof val === "number") return Number.isInteger(val) ? "int" : "float";
    if (typeof val === "boolean") return "bool";
    if (Array.isArray(val)) {
      const first = val.find(v => v !== null);
      return first ? `List[${infer(first, fieldName + "Item")}]` : "List[Any]";
    }
    if (typeof val === "object") {
      const clsName = fieldName.charAt(0).toUpperCase() + fieldName.slice(1);
      build(val, clsName);
      return clsName;
    }
    return "Any";
  }
  function build(obj, cls) {
    const fields = Object.entries(obj).map(([k, v]) => `    ${k}: ${infer(v, k)}`);
    classes.push(`@dataclass\nclass ${cls}:\n${fields.join("\n")}`);
  }
  build(obj, name);
  return classes.join("\n\n");
}

const data = { id: 1, name: "Alice", scores: [98, 85] };
console.log(jsonToPython(data, "User"));
// @dataclass
// class User:
//     id: int
//     name: str
//     scores: List[int]

from pydantic import BaseModel
from typing import List, Optional

class Address(BaseModel):
    street: str
    city: str
    zip: str

class User(BaseModel):
    id: int
    name: str
    email: str
    active: bool
    score: float
    address: Address
    tags: List[str]
    metadata: Optional[str] = None

# Pydantic parses and validates JSON in one step
raw = '{"id":1,"name":"Alice","email":"alice@example.com","active":true,"score":98.5,"address":{"street":"123 Main St","city":"Springfield","zip":"12345"},"tags":["admin","user"],"metadata":null}'
user = User.model_validate_json(raw)
print(user.name)              # -> Alice
print(user.model_dump_json()) # -> re-serializes to JSON

# Install the generator
pip install datamodel-code-generator

# Generate dataclasses from a JSON file
datamodel-codegen --input data.json --output models.py --output-model-type dataclasses.dataclass

# Generate Pydantic models instead
datamodel-codegen --input data.json --output models.py

# From a JSON string via stdin
echo '{"id": 1, "name": "Alice", "tags": ["admin"]}' | \
  datamodel-codegen --output-model-type dataclasses.dataclass
# Output:
# @dataclass
# class Model:
#     id: int
#     name: str
#     tags: List[str]