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---
library_name: transformers
license: apache-2.0
pipeline_tag: image-to-text
---
# BLIP-Image-to-recip
# Inference code
---
import requests
from PIL import Image
from transformers import BlipForConditionalGeneration, AutoProcessor
img_url = 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSQuFg4LTHUattLGPU0kLzYpBGHRtuqgJY8Gho3uZe_cg&s'
image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
model = BlipForConditionalGeneration.from_pretrained("Fatehmujtaba/BLIP-Image-to-recipe").to(device)
processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
inputs = processor(images=image, return_tensors="pt").to(device)
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]
---
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