> ## Documentation Index
> Fetch the complete documentation index at: https://pype-db52d533.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Text To Image

The TextToImageMetric is a multimodal metric designed to evaluate the quality of images generated from text prompts. It assesses how well the generated image matches the expected output, serving as a proxy for evaluating the performance of text-to-image models.

### **Required Arguments**

* input: A list of text prompts that describe the desired image content.
* actual\_output: A list of MLLMImage instances representing the actual images generated by the model. Each MLLMImage requires:
* url: The file path or URL to the image.

### **Optional Arguments**

* threshold: A float representing the minimum passing threshold, defaulted to 0.5.
* model: A string specifying which of OpenAI's GPT models to use, or any custom LLM model of type DeepEvalBaseLLM. Defaulted to 'gpt-4o'.
* include\_reason: A boolean which, when set to True, includes a reason for its evaluation score. Defaulted to True.
* strict\_mode: A boolean which, when set to True, enforces a binary metric score: 1 for perfection, 0 otherwise. It also overrides the current threshold and sets it to 1. Defaulted to False.
* async\_mode: A boolean which, when set to True, enables concurrent execution within the measure() method. Defaulted to True.
* verbose\_mode: A boolean which, when set to True, prints the intermediate steps used to calculate the metric to the console. Defaulted to False.

### **Usage Example**

```python theme={null}
import sys
import os

from agensight.eval.metrics import TextToImageMetric
from agensight.eval.test_case import MLLMTestCase
from agensight.eval.test_case import MLLMImage

# Dummy image and prompt for demonstration
prompt = ["A cat sitting on a windowsill."]
actual_output = [MLLMImage(url="image-path/cat.jpeg")]

metric = TextToImageMetric(model="gpt-4o", threshold=0.5)
test_case = MLLMTestCase(input=prompt, actual_output=actual_output)

metric.measure(test_case)
print(f"Score: {metric.score}")
print(f"Reason: {metric.reason}") 
```

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