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Update app.py
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app.py
CHANGED
@@ -5,11 +5,17 @@ import torch
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torch.hub.download_url_to_file('http://images.cocodataset.org/val2017/000000039769.jpg', 'cats.jpg')
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torch.hub.download_url_to_file('https://huggingface.co/datasets/nielsr/textcaps-sample/resolve/main/stop_sign.png', 'stop_sign.png')
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vitgpt_processor = AutoImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
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vitgpt_model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
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@@ -17,8 +23,10 @@ vitgpt_tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-capt
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device = "cuda" if torch.cuda.is_available() else "cpu"
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vitgpt_model.to(device)
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def generate_caption(processor, model, image, tokenizer=None):
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@@ -35,16 +43,21 @@ def generate_caption(processor, model, image, tokenizer=None):
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def generate_captions(image):
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caption_vitgpt = generate_caption(vitgpt_processor, vitgpt_model, image, vitgpt_tokenizer)
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return
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examples = [["cats.jpg"], ["stop_sign.png"]]
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title = "Interactive demo: comparing image captioning models"
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description = "Gradio Demo to compare GIT, BLIP and ViT-2-GPT2, 3 state-of-the-art captioning models. To use it, simply upload your image and click 'submit', or click one of the examples to load them. Read more at the links below."
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@@ -52,7 +65,7 @@ article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2102.033
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interface = gr.Interface(fn=generate_captions,
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inputs=gr.inputs.Image(type="pil"),
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outputs=
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examples=examples,
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title=title,
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description=description,
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torch.hub.download_url_to_file('http://images.cocodataset.org/val2017/000000039769.jpg', 'cats.jpg')
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torch.hub.download_url_to_file('https://huggingface.co/datasets/nielsr/textcaps-sample/resolve/main/stop_sign.png', 'stop_sign.png')
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git_processor_base = AutoProcessor.from_pretrained("microsoft/git-base-coco")
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git_model_base = AutoModelForCausalLM.from_pretrained("microsoft/git-base-coco")
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git_processor_large = AutoProcessor.from_pretrained("microsoft/git-large-coco")
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git_model_large = AutoModelForCausalLM.from_pretrained("microsoft/git-large-coco")
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blip_processor_base = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
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blip_model_base = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
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blip_processor_large = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
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blip_model_large = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")
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vitgpt_processor = AutoImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
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vitgpt_model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
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device = "cuda" if torch.cuda.is_available() else "cpu"
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git_model_base.to(device)
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blip_model_base.to(device)
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git_model_large.to(device)
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blip_model_large.to(device)
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vitgpt_model.to(device)
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def generate_caption(processor, model, image, tokenizer=None):
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def generate_captions(image):
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caption_git_base = generate_caption(git_processor_base, git_model_base, image)
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caption_git_large = generate_caption(git_processor_large, git_model_large, image)
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caption_blip_base = generate_caption(blip_processor_base, blip_model_base, image)
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caption_blip_large = generate_caption(blip_processor_large, blip_model_large, image)
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caption_vitgpt = generate_caption(vitgpt_processor, vitgpt_model, image, vitgpt_tokenizer)
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return caption_git_base, caption_git_large, caption_blip_base, caption_blip_large, caption_vitgpt
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examples = [["cats.jpg"], ["stop_sign.png"]]
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outputs = [gr.outputs.Textbox(label="Caption generated by GIT-base"), gr.outputs.Textbox(label="Caption generated by GIT-large"), gr.outputs.Textbox(label="Caption generated by BLIP-base"), gr.outputs.Textbox(label="Caption generated by BLIP-large"), gr.outputs.Textbox(label="Caption generated by ViT+GPT-2")],
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title = "Interactive demo: comparing image captioning models"
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description = "Gradio Demo to compare GIT, BLIP and ViT-2-GPT2, 3 state-of-the-art captioning models. To use it, simply upload your image and click 'submit', or click one of the examples to load them. Read more at the links below."
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interface = gr.Interface(fn=generate_captions,
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inputs=gr.inputs.Image(type="pil"),
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outputs=outputs,
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examples=examples,
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title=title,
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description=description,
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