Add support for image dataset (#571)

Co-authored-by: Rupesh Bansal <rupeshbansal@Shankars-MacBook-Air.local>

tests/models/test_clip_processor.py

@@ -0,0 +1,55 @@
+import tempfile
+import unittest
+import os
+import urllib
+from PIL import Image
+from embedchain.models.clip_processor import ClipProcessor
+
+
+class ClipProcessorTest(unittest.TestCase):
+
+    def test_load_model(self):
+        # Test that the `load_model()` method loads the CLIP model and image preprocessing correctly.
+        model, preprocess = ClipProcessor.load_model()
+
+        # Assert that the model is not None.
+        self.assertIsNotNone(model)
+
+        # Assert that the preprocess is not None.
+        self.assertIsNotNone(preprocess)
+
+    def test_get_image_features(self):
+        # Clone the image to a temporary folder.
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            urllib.request.urlretrieve(
+                'https://upload.wikimedia.org/wikipedia/en/a/a9/Example.jpg',
+                "image.jpg")
+
+            image = Image.open("image.jpg")
+            image.save(os.path.join(tmp_dir, "image.jpg"))
+
+            # Get the image features.
+            model, preprocess = ClipProcessor.load_model()
+            ClipProcessor.get_image_features(os.path.join(tmp_dir, "image.jpg"), model, preprocess)
+
+            # Delete the temporary file.
+            os.remove(os.path.join(tmp_dir, "image.jpg"))
+
+            # Assert that the test passes.
+            self.assertTrue(True)
+
+    def test_get_text_features(self):
+        # Test that the `get_text_features()` method returns a list containing the text embedding.
+        query = "This is a text query."
+        model, preprocess = ClipProcessor.load_model()
+
+        text_features = ClipProcessor.get_text_features(query)
+
+        # Assert that the text embedding is not None.
+        self.assertIsNotNone(text_features)
+
+        # Assert that the text embedding is a list of floats.
+        self.assertIsInstance(text_features, list)
+
+        # Assert that the text embedding has the correct length.
+        self.assertEqual(len(text_features), 512)