| import gradio as gr | |
| from transformers import AutoProcessor, AutoModelForCausalLM, BlipForConditionalGeneration | |
| import torch | |
| torch.hub.download_url_to_file('http://images.cocodataset.org/val2017/000000039769.jpg', 'cats.jpg') | |
| git_processor = AutoProcessor.from_pretrained("microsoft/git-base-coco") | |
| git_model = AutoModelForCausalLM.from_pretrained("microsoft/git-base-coco") | |
| blip_processor = AutoProcessor.from_pretrained("Salesfoce/blip-image-captioning-base") | |
| blip_model = BlipForConditionalGeneration.from_pretrained("Salesfoce/blip-image-captioning-base") | |
| def generate_caption(processor, model, image): | |
| inputs = processor(image=image, return_tensors="pt") | |
| generated_ids = model.generate(pixel_values=pixel_values, max_length=50) | |
| generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] | |
| return generated_caption | |
| def generate_captions(image): | |
| caption_git = generate_caption(git_processor, git_model, image) | |
| caption_blip = generate_caption(blip_processor, blip_model, image) | |
| return caption_git, caption_blip | |
| examples = [["cats.jpg"]] | |
| title = "Interactive demo: ViLT" | |
| description = "Gradio Demo for ViLT (Vision and Language Transformer), fine-tuned on VQAv2, a model that can answer questions from images. To use it, simply upload your image and type a question and click 'submit', or click one of the examples to load them. Read more at the links below." | |
| article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2102.03334' target='_blank'>ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision</a> | <a href='https://github.com/dandelin/ViLT' target='_blank'>Github Repo</a></p>" | |
| interface = gr.Interface(fn=answer_question, | |
| inputs=gr.inputs.Image(type="pil"), | |
| outputs=[gr.outputs.Textbox(label="Generated caption by GIT"), gr.outputs.Textbox(label="Generated caption by BLIP")], | |
| examples=examples, | |
| title=title, | |
| description=description, | |
| article=article, | |
| enable_queue=True) | |
| interface.launch(debug=True) |