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Some Language Models support generating images, or looking at images, or both. The following are examples for how to use the capabilities of these types of models in your application. For a list of models capable of image input or output, please refer to the multimodal models on the Models page. On that page, you will can also lookup parameters which are model specific.

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Generating images

Generating an image can be fun, but can also be used in your product. The following is a basic way of generating an image based on a prompt, and saving the image to a file. 
import os
import base64
from tempfile import NamedTemporaryFile
from opperai import Opper

opper = Opper(
    http_bearer=os.getenv("OPPER_API_KEY"),
)

def save_file(bytes: bytes, path: str = None) -> str:
    if path is None:
        with NamedTemporaryFile(delete=False, suffix=".png") as temp_file:
            path = temp_file.name

    with open(path, "wb") as f:
        f.write(bytes)
        f.close()

    return path

def generate_image(description: str):
    response = opper.call(
        name="generate_image",
        input=description,
        model="openai/gpt-image-1",
    )
    return response.images[0]

def main():
    description = "wide-angle photo of a person holding a presentation about AI in a room full of people"
    generated_image_str = generate_image(description)
    path = save_file(base64.b64decode(generated_image_str))
    print(path)

if __name__ == "__main__":
    main()
# /var/folders/dx/lwwkf62n61j9z928d87z5txm0000gn/T/tmpave568r8.png

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Using images as input

Processing images by using them as input to your models can unlock a variety of applications, such as image captioning, classification, or generating detailed descriptions based on the image content. 
import os
import base64
from pydantic import BaseModel, Field
from opperai import Opper

opper = Opper(
    http_bearer=os.getenv("OPPER_API_KEY"),
)

class ImageDescription(BaseModel):
    description: str = Field(description="Description of the image content")

def describe_image(image_path: str) -> str:
    with open(image_path, "rb") as f:
        image_data = base64.b64encode(f.read()).decode('utf-8')
    
    media_input = f"data:image/png;base64,{image_data}"
    
    response = opper.call(
        name="describe_image",
        instructions="Describe the content of the image",
        output_schema=ImageDescription,
        input={
            "_opper_media_input": media_input,  # this field name is required by opper
        },
        model="openai/gpt-4o",
    )
    return response.description

def main():
    image_path = "/var/folders/dx/lwwkf62n61j9z928d87z5txm0000gn/T/tmpave568r8.png"
    description = describe_image(image_path)
    print(f"Image description: {description}")
    # Image description: The image shows a person giving a presentation in a dark room. The presenter is pointing at a screen displaying a visual related to artificial intelligence (AI). The screen has the text 'AI' and 'AI RORENGE' along with circuit-like graphics. Several people are seated, watching the presentation attentively.

if __name__ == "__main__":
    main()

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Common denominator

In this example, we are passing in multiple images and asking the LLM to find the most common denominator between the images. 
import os
import base64
from typing import List
from pydantic import BaseModel, Field
from opperai import Opper

opper = Opper(
    http_bearer=os.getenv("OPPER_API_KEY"),
)

class CommonDenominator(BaseModel):
    description: str = Field(description="Description of the common denominator among the images")

def common_denominator(image_paths: List[str]) -> str:
    image_inputs = []
    for path in image_paths:
        with open(path, "rb") as f:
            image_data = base64.b64encode(f.read()).decode('utf-8')
            image_inputs.append(f"data:image/png;base64,{image_data}")
    
    response = opper.call(
        name="common_denominator",
        instructions="given a list of images, return the most common denominator of the images",
        output_schema=CommonDenominator,
        input={
            "_opper_media_input": image_inputs,  # this field name is required by opper
        },
        model="openai/gpt-4o",
    )
    return response.description

def main():
    image_paths = [
        "/var/folders/dx/lwwkf62n61j9z928d87z5txm0000gn/T/tmpid366821.png",
        "/var/folders/dx/lwwkf62n61j9z928d87z5txm0000gn/T/tmpuhl0wyg4.png",
        "/var/folders/dx/lwwkf62n61j9z928d87z5txm0000gn/T/tmpijk_v13m.png",
        "/var/folders/dx/lwwkf62n61j9z928d87z5txm0000gn/T/tmpqglezg1g.png",
    ]
    description = common_denominator(image_paths)
    print(f"Image description: {description}")
    # Image description: People using VR headsets in a forest

if __name__ == "__main__":
    main()
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