Create app.py

app.py

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+import gradio as gr
+from transformers import AutoProcessor, AutoTokenizer, AutoImageProcessor, AutoModelForCausalLM, BlipForConditionalGeneration, VisionEncoderDecoderModel
+import torch
+
+torch.hub.download_url_to_file('http://images.cocodataset.org/val2017/000000039769.jpg', 'cats.jpg')
+torch.hub.download_url_to_file('https://huggingface.co/datasets/nielsr/textcaps-sample/resolve/main/stop_sign.png', 'stop_sign.png')
+torch.hub.download_url_to_file('https://cdn.openai.com/dall-e-2/demos/text2im/astronaut/horse/photo/0.jpg', 'astronaut.jpg')
+
+git_processor_base = AutoProcessor.from_pretrained("microsoft/git-base-coco")
+git_model_base = AutoModelForCausalLM.from_pretrained("microsoft/git-base-coco")
+
+git_processor_large = AutoProcessor.from_pretrained("microsoft/git-large-coco")
+git_model_large = AutoModelForCausalLM.from_pretrained("microsoft/git-large-coco")
+
+blip_processor_base = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+blip_model_base = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
+
+blip_processor_large = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
+blip_model_large = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")
+
+vitgpt_processor = AutoImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+vitgpt_model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+vitgpt_tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+git_model_base.to(device)
+blip_model_base.to(device)
+git_model_large.to(device)
+blip_model_large.to(device)
+vitgpt_model.to(device)
+
+def generate_caption(processor, model, image, tokenizer=None):
+    inputs = processor(images=image, return_tensors="pt").to(device)
+    
+    generated_ids = model.generate(pixel_values=inputs.pixel_values, max_length=50)
+
+    if tokenizer is not None:
+        generated_caption = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    else:
+        generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+   
+    return generated_caption
+
+
+def generate_captions(image):
+    caption_git_base = generate_caption(git_processor_base, git_model_base, image)
+
+    caption_git_large = generate_caption(git_processor_large, git_model_large, image)
+
+    caption_blip_base = generate_caption(blip_processor_base, blip_model_base, image)
+
+    caption_blip_large = generate_caption(blip_processor_large, blip_model_large, image)
+
+    caption_vitgpt = generate_caption(vitgpt_processor, vitgpt_model, image, vitgpt_tokenizer)
+
+    return caption_git_base, caption_git_large, caption_blip_base, caption_blip_large, caption_vitgpt
+
+   
+examples = [["cats.jpg"], ["stop_sign.png"], ["astronaut.jpg"]]
+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")] 
+
+title = "Interactive demo: comparing image captioning models"
+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."
+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>"
+
+interface = gr.Interface(fn=generate_captions, 
+                         inputs=gr.inputs.Image(type="pil"),
+                         outputs=outputs,
+                         examples=examples, 
+                         title=title,
+                         description=description,
+                         article=article, 
+                         enable_queue=True)
+interface.launch(debug=True)