> For the complete documentation index, see [llms.txt](https://infronai.gitbook.io/docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://infronai.gitbook.io/docs/features/multimodal-input/images-inputs.md).

# Images Inputs

Infron AI supports sending images via the API. This guide will show you how to work with images file types using our API.

## Image Inputs

Requests with images, to multimodel models, are available via the`/v1/chat/completions`API with a multi-part `messages` parameter. The `image_url` can either be a URL or a base64-encoded image. 

Note that multiple images can be sent in separate content array entries. The number of images you can send in a single request varies per provider and per model. Due to how the content is parsed, we recommend sending the text prompt first, then the images. If the images must come first, we recommend putting it in the system prompt.

### Using Image URLs

Here's how to send an image using a URL:

{% tabs %}
{% tab title="Python" %}

```python
import requests
import json

url = "https://llm.onerouter.pro/v1/chat/completions"
headers = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json"
}

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "What's in this image?"
            },
            {
                "type": "image_url",
                "image_url": {
                    "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
                }
            }
        ]
    }
]

payload = {
    "model": "{{MODEL}}",
    "messages": messages
}

response = requests.post(url, headers=headers, json=payload)
print(response.json()["choices"][0]["message"]["content"])
```

{% endtab %}

{% tab title="TypeScript" %}

```typescript
const response = await fetch('https://llm.onerouter.pro/v1/chat/completions', {
  method: 'POST',
  headers: {
    Authorization: `Bearer ${API_KEY}`,
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    model: '{{MODEL}}',
    messages: [
      {
        role: 'user',
        content: [
          {
            type: 'text',
            text: "What's in this image?",
          },
          {
            type: 'image_url',
            image_url: {
              url: 'https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg',
            },
          },
        ],
      },
    ],
  }),
});

const data = await response.json();
console.log(data);
```

{% endtab %}
{% endtabs %}

### Using Base64 Encoded Images

For locally stored images, you can send them using base64 encoding. Here's how to do it:

{% tabs %}
{% tab title="Python" %}

```python
import requests
import json
import base64
from pathlib import Path

def encode_image_to_base64(image_path):
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode('utf-8')

url = "https://llm.onerouter.pro/v1/chat/completions"
headers = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json"
}

# Read and encode the image
image_path = "path/to/your/image.jpg"
base64_image = encode_image_to_base64(image_path)
data_url = f"data:image/jpeg;base64,{base64_image}"

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "What's in this image?"
            },
            {
                "type": "image_url",
                "image_url": {
                    "url": data_url
                }
            }
        ]
    }
]

payload = {
    "model": "{{MODEL}}",
    "messages": messages
}

response = requests.post(url, headers=headers, json=payload)
print(response.json()["choices"][0]["message"]["content"])
```

{% endtab %}

{% tab title="TypeScript" %}

```typescript
async function encodeImageToBase64(imagePath: string): Promise<string> {
  const imageBuffer = await fs.promises.readFile(imagePath);
  const base64Image = imageBuffer.toString('base64');
  return `data:image/jpeg;base64,${base64Image}`;
}

// Read and encode the image
const imagePath = 'path/to/your/image.jpg';
const base64Image = await encodeImageToBase64(imagePath);

const response = await fetch('https://llm.onerouter.pro/v1/chat/completions', {
  method: 'POST',
  headers: {
    Authorization: `Bearer ${API_KEY_REF}`,
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    model: '{{MODEL}}',
    messages: [
      {
        role: 'user',
        content: [
          {
            type: 'text',
            text: "What's in this image?",
          },
          {
            type: 'image_url',
            image_url: {
              url: base64Image,
            },
          },
        ],
      },
    ],
  }),
});

const data = await response.json();
console.log(data);
```

{% endtab %}
{% endtabs %}

Supported image content types are:

* `image/png`
* `image/jpeg`
* `image/webp`


---

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```
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```

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