Update README.md

README.md

@@ -66,23 +66,26 @@ The model achieved high accuracy on a held-out validation set, indicating strong
 To use the model for inference, you can load it using the `transformers` library from Hugging Face:
 
 ```python
-from transformers import AutoModelForImageClassification, ViTImageProcessor
+import torch
 from PIL import Image
-import requests
+from transformers import AutoModelForImageClassification, ViTImageProcessor
 
-# Load an image
-url = "https://example.com/path-to-logo.jpg"
-image = Image.open(requests.get(url, stream=True).raw)
+image = Image.open('<path_to_image>')
+image = image.convert("RGB")  # Ensure image is in RGB format
 
+# Load model and processor
 model = AutoModelForImageClassification.from_pretrained("Falconsai/brand_identification")
 processor = ViTImageProcessor.from_pretrained("Falconsai/brand_identification")
+
+# Preprocess image and make predictions
 with torch.no_grad():
     inputs = processor(images=image, return_tensors="pt")
     outputs = model(**inputs)
     logits = outputs.logits
 
 predicted_label = logits.argmax(-1).item()
-model.config.id2label[predicted_label]
+print(model.config.id2label[predicted_label])
+
 ```
 ### Companies Identified: