> ## Documentation Index
> Fetch the complete documentation index at: https://docs.getvalenceai.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Get emotions (Asynch)

> After uploading your audio file via upload_file, get the results of your file's emotion classification.

### Parameters

* `request_id` (string): Request ID from `client.asynch.upload`
* `max_tries` (number, optional): Maximum polling attempts (default: 20, range: 1-100)
* `interval` (number, optional): Polling interval in milliseconds (default: 5000, range: 1000-60000)

### Response

**Returns:** `results [string]` - Emotion prediction results

**Throws:** Error if request\_id is invalid or prediction times out

### Usage

<CodeGroup>
  ```python Python theme={null}
  from valenceai import ValenceClient

  client = ValenceClient(show_progress=True)

  # Upload the audio file
  request_id = client.asynch.upload("YOUR_FILE.wav")

  # Get emotions from uploaded audio
  result = client.asynch.emotions(request_id)
  print(result)
  ```

  ```javascript Javascript theme={null}
  import { ValenceClient } from 'valenceai';

  try {
    const client = new ValenceClient();
    
    // Upload the audio file
    const requestId = await client.asynch.upload('YOUR_FILE.wav');
    console.log('Upload complete. Request ID:', requestId);
    
    // Get emotions from uploaded audio
    const emotions = await client.asynch.emotions(requestId);
    console.log('Emotions detected:', emotions);
  } catch (error) {
    console.error('Error:', error.message);
  }
  ```
</CodeGroup>