52 lines
2.1 KiB
Python
52 lines
2.1 KiB
Python
# import os
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# import tempfile
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# import urllib
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# import pytest
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# from PIL import Image
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# TODO: Uncomment after fixing clip dependency issue
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# from embedchain.models.clip_processor import ClipProcessor
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# class TestClipProcessor:
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# @pytest.mark.xfail(reason="This test is failing because of the missing CLIP dependency.")
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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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# @pytest.mark.xfail(reason="This test is failing because of the missing CLIP dependency.")
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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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# @pytest.mark.xfail(reason="This test is failing because of the missing CLIP dependency.")
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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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