# pydantic-to-typescript
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A simple CLI tool for converting pydantic models into typescript interfaces. Useful for any scenario in which python and javascript applications are interacting, since it allows you to have a single source of truth for type definitions.
This tool requires that you have the lovely json2ts CLI utility installed. Instructions can be found here: https://www.npmjs.com/package/json-schema-to-typescript
### Installation
```shell
pip install pydantic-to-typescript2
```
---
### CLI
| Prop | Description |
| :------------------------------ | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| ‑‑module | name or filepath of the python module you would like to convert. All the pydantic models within it will be converted to typescript interfaces. Discoverable submodules will also be checked. |
| ‑‑output | name of the file the typescript definitions should be written to. Ex: './frontend/apiTypes.ts' |
| ‑‑exclude | name of a pydantic model which should be omitted from the resulting typescript definitions. This option can be defined multiple times, ex: `--exclude Foo --exclude Bar` to exclude both the Foo and Bar models from the output. |
| ‑‑json2ts‑cmd | optional, the command used to invoke json2ts. The default is 'json2ts'. Specify this if you have it installed locally (ex: 'yarn json2ts') or if the exact path to the executable is required (ex: /myproject/node_modules/bin/json2ts) |
---
### Usage
Define your pydantic models (ex: /backend/api.py):
```python
from fastapi import FastAPI
from pydantic import BaseModel
from typing import List, Optional
api = FastAPI()
class LoginCredentials(BaseModel):
username: str
password: str
class Profile(BaseModel):
username: str
age: Optional[int]
hobbies: List[str]
class LoginResponseData(BaseModel):
token: str
profile: Profile
@api.post('/login/', response_model=LoginResponseData)
def login(body: LoginCredentials):
profile = Profile(**body.dict(), age=72, hobbies=['cats'])
return LoginResponseData(token='very-secure', profile=profile)
```
Execute the command for converting these models into typescript definitions, via:
```bash
$ pydantic2ts --module backend.api --output ./frontend/apiTypes.ts
```
or:
```bash
$ pydantic2ts --module ./backend/api.py --output ./frontend/apiTypes.ts
```
or:
```python
from pydantic2ts import generate_typescript_defs
generate_typescript_defs("backend.api", "./frontend/apiTypes.ts")
```
The models are now defined in typescript...
```ts
/* tslint:disable */
/**
/* This file was automatically generated from pydantic models by running pydantic2ts.
/* Do not modify it by hand - just update the pydantic models and then re-run the script
*/
export interface LoginCredentials {
username: string;
password: string;
}
export interface LoginResponseData {
token: string;
profile: Profile;
}
export interface Profile {
username: string;
age?: number;
hobbies: string[];
}
```
...and can be used in your typescript code with complete confidence.
```ts
import { LoginCredentials, LoginResponseData } from "./apiTypes.ts";
async function login(
credentials: LoginCredentials,
resolve: (data: LoginResponseData) => void,
reject: (error: string) => void
) {
try {
const response: Response = await fetch("/login/", {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify(credentials),
});
const data: LoginResponseData = await response.json();
resolve(data);
} catch (error) {
reject(error.message);
}
}
```
### Testing
```shell
# Install nvm
curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.3/install.sh | bash
# Load nvm into the current shell session
export NVM_DIR="$([ -z "${XDG_CONFIG_HOME-}" ] && printf %s "${HOME}/.nvm" || printf %s "${XDG_CONFIG_HOME}/nvm")"
[ -s "$NVM_DIR/nvm.sh" ] && \. "$NVM_DIR/nvm.sh" # This loads nvm
# Install Node.js version 18
nvm install 18
# Use Node.js version 18
nvm use 18
# Verify Node.js installation
node -v
npm -v
# Install Python dependencies
pip install -U pip wheel pytest pytest-cov coverage
pip install -U .
# Install json-schema-to-typescript
npm install -g json-schema-to-typescript
```
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"description": "# pydantic-to-typescript\n\n[](https://badge.fury.io/py/pydantic-to-typescript)\n[](https://github.com/dariuslabs/pydantic-to-typescript2/actions/workflows/cicd.yml)\n[](https://coveralls.io/github/dariuslabs/pydantic-to-typescript2?branch=master)\n\nA simple CLI tool for converting pydantic models into typescript interfaces. Useful for any scenario in which python and javascript applications are interacting, since it allows you to have a single source of truth for type definitions.\n\nThis tool requires that you have the lovely json2ts CLI utility installed. Instructions can be found here: https://www.npmjs.com/package/json-schema-to-typescript\n\n### Installation\n\n```shell\npip install pydantic-to-typescript2\n```\n\n---\n\n### CLI\n\n| Prop | Description |\n| :------------------------------ | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| ‑‑module | name or filepath of the python module you would like to convert. All the pydantic models within it will be converted to typescript interfaces. Discoverable submodules will also be checked. |\n| ‑‑output | name of the file the typescript definitions should be written to. Ex: './frontend/apiTypes.ts' |\n| ‑‑exclude | name of a pydantic model which should be omitted from the resulting typescript definitions. This option can be defined multiple times, ex: `--exclude Foo --exclude Bar` to exclude both the Foo and Bar models from the output. |\n| ‑‑json2ts‑cmd | optional, the command used to invoke json2ts. The default is 'json2ts'. Specify this if you have it installed locally (ex: 'yarn json2ts') or if the exact path to the executable is required (ex: /myproject/node_modules/bin/json2ts) |\n\n---\n\n### Usage\n\nDefine your pydantic models (ex: /backend/api.py):\n\n```python\nfrom fastapi import FastAPI\nfrom pydantic import BaseModel\nfrom typing import List, Optional\n\napi = FastAPI()\n\nclass LoginCredentials(BaseModel):\n username: str\n password: str\n\nclass Profile(BaseModel):\n username: str\n age: Optional[int]\n hobbies: List[str]\n\nclass LoginResponseData(BaseModel):\n token: str\n profile: Profile\n\n@api.post('/login/', response_model=LoginResponseData)\ndef login(body: LoginCredentials):\n profile = Profile(**body.dict(), age=72, hobbies=['cats'])\n return LoginResponseData(token='very-secure', profile=profile)\n```\n\nExecute the command for converting these models into typescript definitions, via:\n\n```bash\n$ pydantic2ts --module backend.api --output ./frontend/apiTypes.ts\n```\n\nor:\n\n```bash\n$ pydantic2ts --module ./backend/api.py --output ./frontend/apiTypes.ts\n```\n\nor:\n\n```python\nfrom pydantic2ts import generate_typescript_defs\n\ngenerate_typescript_defs(\"backend.api\", \"./frontend/apiTypes.ts\")\n```\n\nThe models are now defined in typescript...\n\n```ts\n/* tslint:disable */\n/**\n/* This file was automatically generated from pydantic models by running pydantic2ts.\n/* Do not modify it by hand - just update the pydantic models and then re-run the script\n*/\n\nexport interface LoginCredentials {\n username: string;\n password: string;\n}\nexport interface LoginResponseData {\n token: string;\n profile: Profile;\n}\nexport interface Profile {\n username: string;\n age?: number;\n hobbies: string[];\n}\n```\n\n...and can be used in your typescript code with complete confidence.\n\n```ts\nimport { LoginCredentials, LoginResponseData } from \"./apiTypes.ts\";\n\nasync function login(\n credentials: LoginCredentials,\n resolve: (data: LoginResponseData) => void,\n reject: (error: string) => void\n) {\n try {\n const response: Response = await fetch(\"/login/\", {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify(credentials),\n });\n const data: LoginResponseData = await response.json();\n resolve(data);\n } catch (error) {\n reject(error.message);\n }\n}\n```\n\n### Testing\n```shell\n# Install nvm\ncurl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.3/install.sh | bash\n\n# Load nvm into the current shell session\nexport NVM_DIR=\"$([ -z \"${XDG_CONFIG_HOME-}\" ] && printf %s \"${HOME}/.nvm\" || printf %s \"${XDG_CONFIG_HOME}/nvm\")\"\n[ -s \"$NVM_DIR/nvm.sh\" ] && \\. \"$NVM_DIR/nvm.sh\" # This loads nvm\n\n# Install Node.js version 18\nnvm install 18\n\n# Use Node.js version 18\nnvm use 18\n\n# Verify Node.js installation\nnode -v\nnpm -v\n\n# Install Python dependencies\npip install -U pip wheel pytest pytest-cov coverage\npip install -U .\n\n# Install json-schema-to-typescript\nnpm install -g json-schema-to-typescript\n```\n\n",
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