DevBentoGenerate Python Dataclasses from Config JSON
Generate Python dataclasses with type annotations from a nested configuration object. Paste your config JSON and get typed Python classes for settings modules.
// Generated by DevBento — json-to-typescript
interface RootApp {
name: string;
version: string;
debug: boolean;
}
interface RootDatabaseCredentials {
user: string;
password: string;
}
interface RootDatabase {
host: string;
port: number;
credentials: RootDatabaseCredentials;
}
interface RootFeatures {
darkMode: boolean;
maxConnections: number;
allowedOrigins: string[];
}
interface Root {
app: RootApp;
database: RootDatabase;
features: RootFeatures;
}Generate Python Dataclasses from Config JSON
This example converts a nested application config into Python dataclasses with type annotations. The pre-filled JSON has app settings, a database section with credentials, and feature flags. Python mode is pre-selected, so the generated classes appear as soon as the page loads.
What You Get
The tool produces:
RootAppfor top-level app settings (name, version, debug)RootDatabaseandRootDatabaseCredentialsfor the nested database sectionRootFeaturesfor feature flags, including the allowed origins array- Python type mappings:
str,int,bool,List[str] - snake_case field names converted from camelCase JSON keys
- Imports from
dataclassesandtypingincluded automatically
How to Use
- The example loads with a sample application config and Python mode pre-selected
- Inspect the generated dataclasses and their type annotations
- Copy the code into a settings module in your Python project
- Replace the sample with your actual config JSON to generate classes for your own settings
Real-World Pattern
The standard Python settings pattern is a module that loads config once at startup and exposes typed attributes to the rest of the application. The generated dataclasses drop straight into that pattern. For validation on top of typing, pydantic-settings accepts the same model shape and raises a clear error when the loaded config does not match. Either way, a misconfigured deployment fails at boot with the exact field that is wrong, not later with a KeyError in production.
Environment variable overrides
A common extension is to load the JSON config, then let environment variables override individual keys. With typed settings, the override layer stays simple: read the env var, coerce it to the field type, and assign it to the loaded dataclass instance.
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