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--- |
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library_name: transformers |
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license: apache-2.0 |
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pipeline_tag: image-to-text |
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--- |
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# BLIP-Image-to-recip |
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# Inference code |
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--- |
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import requests |
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from PIL import Image |
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from transformers import BlipForConditionalGeneration, AutoProcessor |
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img_url = 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSQuFg4LTHUattLGPU0kLzYpBGHRtuqgJY8Gho3uZe_cg&s' |
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image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') |
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model = BlipForConditionalGeneration.from_pretrained("Fatehmujtaba/BLIP-Image-to-recipe").to(device) |
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processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base") |
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inputs = processor(images=image, return_tensors="pt").to(device) |
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pixel_values = inputs.pixel_values |
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generated_ids = model.generate(pixel_values=pixel_values, max_length=50) |
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generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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--- |
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