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lastFile:tests/unit/test_project.py

src/skore/project.py

@@ -93,7 +93,7 @@ def serialize(o: Any) -> Item:
                 return Item(
                     raw=o,
                     item_type=ItemType.MEDIA,
-                    serialized=output.getvalue(),
+                    serialized=base64.b64encode(output.getvalue()).decode("ascii"),
                     media_type="image/jpeg",
                 )
 

tests/unit/test_project.py

@@ -1,4 +1,6 @@
+import base64
 import json
+from io import BytesIO
 
 import altair
 import numpy
@@ -181,10 +183,25 @@ def savefig(*args, **kwargs):
     pil_image = Image.new("RGB", (100, 100), color="red")
     project.put("pil_image", pil_image)  # MediaItem (PNG)
 
+    with BytesIO() as output:
+        pil_image.save(output, format="jpeg")
+        pil_image_str = base64.b64encode(output.getvalue()).decode("ascii")
+
     # Add raw bytes with media type
     # raw_bytes = b"Some raw data"
     # project.put_item("raw_data", MediaItem(raw_bytes, "application/octet-stream"))
 
+    # mybytes = b""
+    # project.put_item(
+    #     "mybytes",
+    #     Item(
+    #         raw=mybytes,
+    #         item_type=ItemType.MEDIA,
+    #         media_type="application/octet-stream",
+    #         serialized=mybytes.decode()
+    #     )
+    # )
+
     # Add a scikit-learn model
     model = RandomForestClassifier()
     model.fit(numpy.array([[1, 2], [3, 4]]), [0, 1])
@@ -248,7 +265,7 @@ def savefig(*args, **kwargs):
         },
         "pil_image": {
             "item_type": str(ItemType.MEDIA),
-            "serialized": "",
+            "serialized": pil_image_str,
             "media_type": "image/jpeg",
         },
     }