47 lines
1.6 KiB
Python
47 lines
1.6 KiB
Python
import os
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import tempfile
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import urllib
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from PIL import Image
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from embedchain.models.clip_processor import ClipProcessor
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class TestClipProcessor:
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def test_load_model(self):
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# Test that the `load_model()` method loads the CLIP model and image preprocessing correctly.
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model, preprocess = ClipProcessor.load_model()
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assert model is not None
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assert preprocess is not None
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def test_get_image_features(self):
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# Clone the image to a temporary folder.
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with tempfile.TemporaryDirectory() as tmp_dir:
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urllib.request.urlretrieve("https://upload.wikimedia.org/wikipedia/en/a/a9/Example.jpg", "image.jpg")
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image = Image.open("image.jpg")
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image.save(os.path.join(tmp_dir, "image.jpg"))
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# Get the image features.
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model, preprocess = ClipProcessor.load_model()
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ClipProcessor.get_image_features(os.path.join(tmp_dir, "image.jpg"), model, preprocess)
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# Delete the temporary file.
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os.remove(os.path.join(tmp_dir, "image.jpg"))
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def test_get_text_features(self):
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# Test that the `get_text_features()` method returns a list containing the text embedding.
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query = "This is a text query."
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model, preprocess = ClipProcessor.load_model()
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text_features = ClipProcessor.get_text_features(query)
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# Assert that the text embedding is not None.
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assert text_features is not None
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# Assert that the text embedding is a list of floats.
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assert isinstance(text_features, list)
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# Assert that the text embedding has the correct length.
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assert len(text_features) == 512
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