Image Input Output Evaluation

from fi.evals import ImageInputOutput, EvalClient
from fi.testcases import MLLMTestCase, MLLMImage

# Initialize the evaluation client
evaluator = EvalClient(
    fi_api_key="your_api_key",
    fi_secret_key="your_secret_key"         
)

# Create a test case using MLLMImage objects
test_case_with_objects = MLLMTestCase(
    input="Generate a similar image but change the background to a beach setting",
    input_image_url=MLLMImage(url="https://example.com/input-image.jpg"),
    output_image_url=MLLMImage(url="https://example.com/generated-image.jpg")
)

# Or create a test case using URL strings
test_case = MLLMTestCase(
    input="Generate a similar image but change the background to a beach setting",
    input_image_url="https://example.com/input-image.jpg",
    output_image_url="https://example.com/generated-image.jpg"
)

# Initialize the image input-output evaluator
image_eval = ImageInputOutput(
    config = {
        "criteria": """Evaluate the output image based on: 
        - Adherence to input instruction
        - Preservation of key elements from input image
        - Quality of background modification
        - Overall composition
        - Image quality and coherence"""
    }
)

# Run the evaluation
result = evaluator.evaluate(image_eval, test_case)
print(result) # Will return a score between 0 and 1