---
license: mit
library_name: transformers
pipeline_tag: image-to-text
---

# Load model
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

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)