Update README.md

# testing image
inputs = processor(images=img1, return_tensors="pt")

pixel_values = inputs.pixel_values

generated_ids = model.generate(pixel_values=pixel_values, max_length=50)

generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]

print(generated_caption)

README.md

@@ -8,21 +8,19 @@ pipeline_tag: image-to-text
 from transformers import AutoProcessor, BlipForConditionalGeneration
 
 processor = AutoProcessor.from_pretrained("trunks/blip-image-captioning-base")
+
 model = BlipForConditionalGeneration.from_pretrained("trunks/blip-image-captioning-base")
 
+
 # prepare image for model
 from PIL import Image
 from IPython.display import display
 
 img1 = Image.open("imagepath/img.jpeg")
+
 width, height = img1.size
+
 img1_resized = img1.resize((int(0.3 * width), int(0.3 * height))
-display(img1_resized)
 
-# testing image
-inputs = processor(images=img1, return_tensors="pt")
-pixel_values = inputs.pixel_values
+display(img1_resized)
 
-generated_ids = model.generate(pixel_values=pixel_values, max_length=50)
-generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
-print(generated_caption)