## Execute AI model

`ai.run(strmodel_name, AIRunParams**kwargs)  -> AIRunResponse`

**post** `/accounts/{account_id}/ai/run/{model_name}`

This endpoint provides users with the capability to run specific AI models on-demand.

By submitting the required input data, users can receive real-time predictions or results generated by the chosen AI
model. The endpoint supports various AI model types, ensuring flexibility and adaptability for diverse use cases.

Model specific inputs available in [Cloudflare Docs](https://developers.cloudflare.com/workers-ai/models/).

### Parameters

- `account_id: str`

- `model_name: str`

- `text: str`

  The text that you want to classify

### Returns

- `AIRunResponse`

  An array of classification results for the input text

  - `List[TextClassification]`

    An array of classification results for the input text

    - `label: Optional[str]`

      The classification label assigned to the text (e.g., 'POSITIVE' or 'NEGATIVE')

    - `score: Optional[float]`

      Confidence score indicating the likelihood that the text belongs to the specified label

  - `object`

    The generated image in PNG format

  - `class Audio: …`

    - `audio: Optional[str]`

      The generated audio in MP3 format, base64-encoded

  - `object`

    The generated audio in MP3 format

  - `class TextEmbeddings: …`

    - `data: Optional[List[List[float]]]`

      Embeddings of the requested text values

    - `shape: Optional[List[float]]`

  - `class AutomaticSpeechRecognition: …`

    - `text: str`

      The transcription

    - `vtt: Optional[str]`

    - `word_count: Optional[float]`

    - `words: Optional[List[AutomaticSpeechRecognitionWord]]`

      - `end: Optional[float]`

        The ending second when the word completes

      - `start: Optional[float]`

        The second this word begins in the recording

      - `word: Optional[str]`

  - `List[ImageClassification]`

    - `label: Optional[str]`

      The predicted category or class for the input image based on analysis

    - `score: Optional[float]`

      A confidence value, between 0 and 1, indicating how certain the model is about the predicted label

  - `List[ObjectDetection]`

    An array of detected objects within the input image

    - `box: Optional[ObjectDetectionBox]`

      Coordinates defining the bounding box around the detected object

      - `xmax: Optional[float]`

        The x-coordinate of the bottom-right corner of the bounding box

      - `xmin: Optional[float]`

        The x-coordinate of the top-left corner of the bounding box

      - `ymax: Optional[float]`

        The y-coordinate of the bottom-right corner of the bounding box

      - `ymin: Optional[float]`

        The y-coordinate of the top-left corner of the bounding box

    - `label: Optional[str]`

      The class label or name of the detected object

    - `score: Optional[float]`

      Confidence score indicating the likelihood that the detection is correct

  - `class UnionMember8: …`

    - `response: str`

      The generated text response from the model

    - `tool_calls: Optional[List[UnionMember8ToolCall]]`

      An array of tool calls requests made during the response generation

      - `arguments: Optional[object]`

        The arguments passed to be passed to the tool call request

      - `name: Optional[str]`

        The name of the tool to be called

    - `usage: Optional[UnionMember8Usage]`

      Usage statistics for the inference request

      - `completion_tokens: Optional[float]`

        Total number of tokens in output

      - `prompt_tokens: Optional[float]`

        Total number of tokens in input

      - `total_tokens: Optional[float]`

        Total number of input and output tokens

  - `object`

  - `class Translation: …`

    - `translated_text: Optional[str]`

      The translated text in the target language

  - `class Summarization: …`

    - `summary: Optional[str]`

      The summarized version of the input text

  - `class ImageToText: …`

    - `description: Optional[str]`

  - `class ImageTextToText: …`

    - `description: Optional[str]`

  - `class MultimodalEmbeddings: …`

    - `data: Optional[List[List[float]]]`

    - `shape: Optional[List[float]]`

### Example

```python
import os
from cloudflare import Cloudflare

client = Cloudflare(
    api_token=os.environ.get("CLOUDFLARE_API_TOKEN"),  # This is the default and can be omitted
)
response = client.ai.run(
    model_name="model_name",
    account_id="023e105f4ecef8ad9ca31a8372d0c353",
    text="x",
)
print(response)
```

#### Response

```json
{
  "result": [
    {
      "label": "label",
      "score": 0
    }
  ]
}
```