# Classify

## Classify

`classify.run(ClassifyRunParams**kwargs)  -> ClassifyRunResponse`

**post** `/v1/classify`

Classify

### Parameters

- `file: File`

  File to classify, either as raw data or a URL

  - `class FileData: …`

    - `file_data: str`

      Raw file data encoded as bytes

  - `class FileURL: …`

    - `file_url: str`

      URL pointing to the file to be processed

- `types: Iterable[ClassifyTypeParam]`

  List of classification types to classify the document into

  - `description: str`

    Description of the classified type

  - `name: str`

    Name of the classified type

### Returns

- `class ClassifyRunResponse: …`

  - `credit_used: int`

    Cost in EUR (€) for this classification (per page for PDFs, flat for images)

  - `result: Result`

    Classification result for the provided document

    - `confidence: float`

      Confidence score between 0 and 1

    - `type: str`

      Type of the classified result

### Example

```python
import os
from parseocr import Parseocr

client = Parseocr(
    api_key=os.environ.get("PARSEOCR_API_KEY"),  # This is the default and can be omitted
)
response = client.classify.run(
    file={
        "file_data": "JVBERi0xLjcKJcfs...KqldOIQo="
    },
    types=[{
        "description": "Invoices request payment for goods or services provided.",
        "name": "invoice",
    }, {
        "description": "Quotes provide an estimate of costs for goods or services before they are provided.",
        "name": "quote",
    }],
)
print(response.credit_used)
```

#### Response

```json
{
  "credit_used": 0,
  "result": {
    "confidence": 0.98,
    "type": "invoice"
  }
}
```

## Domain Types

### Classify Type

- `class ClassifyType: …`

  - `description: str`

    Description of the classified type

  - `name: str`

    Name of the classified type

### File Data

- `class FileData: …`

  - `file_data: str`

    Raw file data encoded as bytes

### File URL

- `class FileURL: …`

  - `file_url: str`

    URL pointing to the file to be processed