Update README.md

README.md

@@ -51,17 +51,17 @@ pip install transformers==4.50
 ```
 Then run the code:
 ```python
-from transformers import AutoProcessor, AutoModel
-from PIL import Image
 from io import BytesIO
+
 import requests
 import torch
+from PIL import Image
+from transformers import AutoProcessor, AutoModel
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 model_name = "ibm-granite/granite-vision-embedding-3.3-2b"
-model = AutoModel.from_pretrained(model_name, trust_remote_code=True,torch_dtype=torch.float16).to(device).eval()
-processor = AutoProcessor.from_pretrained(model_name,trust_remote_code=True)
-
+model = AutoModel.from_pretrained(model_name, trust_remote_code=True, torch_dtype=torch.float16).to(device).eval()
+processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
 
 # ─────────────────────────────────────────────
 # Inputs: Image + Text
@@ -101,7 +101,6 @@ similarity = processor.score(txt_emb, img_emb, batch_size=1, device=device)
 print("\n" + "=" * 50)
 print(f"📊 Similarity between image and text: {similarity.item():.4f}")
 print("=" * 50)
-
 ```
 ### Use granite-vision-embedding-3.3-2b for MM RAG
 For an example of MM-RAG using granite-vision-3.3-2b-embedding refer to [this notebook](......).