ruslanmv commited on
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c3db921
1 Parent(s): dc7b78c

Update main.py

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  1. main.py +3 -5
main.py CHANGED
@@ -3,9 +3,7 @@ import gradio as gr
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  from transformers import AutoProcessor, AutoTokenizer, AutoImageProcessor, AutoModelForCausalLM, BlipForConditionalGeneration, VisionEncoderDecoderModel
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  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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- torch.hub.download_url_to_file('https://cdn.openai.com/dall-e-2/demos/text2im/astronaut/horse/photo/0.jpg', 'astronaut.jpg')
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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")
@@ -61,14 +59,14 @@ def generate_captions(image):
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  examples = [["cat.jpg"], ["dog.jpg"], ["horse.jpg"]]
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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+GPT2, 3 state-of-the-art vision+language 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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  article = "<p style='text-align: center'><a href='https://huggingface.co/docs/transformers/main/model_doc/blip' target='_blank'>BLIP docs</a> | <a href='https://huggingface.co/docs/transformers/main/model_doc/git' target='_blank'>GIT docs</a></p>"
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  iface = 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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  article=article,
 
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  from transformers import AutoProcessor, AutoTokenizer, AutoImageProcessor, AutoModelForCausalLM, BlipForConditionalGeneration, VisionEncoderDecoderModel
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  import torch
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+
 
 
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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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  examples = [["cat.jpg"], ["dog.jpg"], ["horse.jpg"]]
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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 = "Image to Text : Multiple Models"
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  description = "Gradio Demo to compare GIT, BLIP and ViT+GPT2, 3 state-of-the-art vision+language 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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  article = "<p style='text-align: center'><a href='https://huggingface.co/docs/transformers/main/model_doc/blip' target='_blank'>BLIP docs</a> | <a href='https://huggingface.co/docs/transformers/main/model_doc/git' target='_blank'>GIT docs</a></p>"
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  iface = 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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  article=article,