Update README.md

README.md

@@ -46,9 +46,9 @@ from transformers import CLIPModel, CLIPProcessor
 from PIL import Image
 import torch
 
-# Load model + feature extractor
-model = CLIPModel.from_pretrained("your-username/PHOENIX-patent-retrieval")
-feature_extractor = CLIPProcessor.from_pretrained("your-username/PHOENIX-patent-retrieval")
+# Load fine-tuned model and processor
+model = CLIPModel.from_pretrained("kshitij3188/PHOENIX-patent-retrieval")
+processor = CLIPProcessor.from_pretrained("kshitij3188/PHOENIX-patent-retrieval")
 
 model.eval()
 ```
@@ -58,12 +58,16 @@ model.eval()
 ```python
 from torchvision import transforms
 
-def extract_embedding(image_path):
+def extract_image_embedding(image_path):
     image = Image.open(image_path).convert("RGB")
-    inputs = feature_extractor(images=image, return_tensors="pt")
+    inputs = processor(images=image, return_tensors="pt")
     with torch.no_grad():
-        outputs = model(**inputs)
-    return outputs.last_hidden_state[:, 0, :].squeeze()
+        embedding = model.get_image_features(**inputs).squeeze()
+    return embedding
+
+# Example
+embedding = extract_image_embedding("some_patent_image.png")
+print("🔍 Image embedding shape:", embedding.shape)
 ```
 
 You can now compare cosine similarity between embeddings to retrieve similar patent drawings.