Add model checkpoint with Git LFS

.gitattributes

@@ -1 +1,2 @@
 code/clip/bpe_simple_vocab_16e6.txt.gz filter=lfs diff=lfs merge=lfs -text
+model/tsbir_model_final.pt filter=lfs diff=lfs merge=lfs -text

pipeline.py → handler.py

@@ -13,7 +13,7 @@ from clip.clip import _transform, tokenize
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-class PreTrainedPipeline:
+class EndpointHandler:
     def __init__(self, path: str = ""):
         """
         Initialize the pipeline by loading the model.

model/tsbir_model_final.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7bc305f6a75e448861ab8b02ebbed143d2ba09723c158646bd53f13a86934b3f
+size 2713984713

model/tsbir_model_final.pt:Zone.Identifier

@@ -0,0 +1,4 @@
+[ZoneTransfer]
+ZoneId=3
+ReferrerUrl=https://github.com/janesjanes/tsbir?tab=readme-ov-file
+HostUrl=https://patsorn.me/projects/tsbir/data/tsbir_model_final.pt

test.py

@@ -0,0 +1,27 @@
+from handler import EndpointHandler
+import base64
+
+# Helper function to encode an image to Base64
+def encode_image_to_base64(image_path):
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode("utf-8")
+
+# Initialize the handler
+handler = EndpointHandler(path=".")
+
+# Prepare sample inputs
+image_path = "path_to_your_sketch_image.jpg"  # Replace with your image path
+base64_image = encode_image_to_base64(image_path)
+text_query = "A pink flower"
+
+# Create payload
+payload = {
+    "image": base64_image,
+    "text": text_query
+}
+
+# Run the handler
+response = handler(payload)
+
+# Show results
+print("Fused Embedding:", response)