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| import gradio as gr | |
| from PIL import Image | |
| import clipGPT | |
| import vitGPT | |
| import skimage.io as io | |
| import PIL.Image | |
| import difflib | |
| import ViTCoAtt | |
| from build_vocab import Vocabulary | |
| # Caption generation functions | |
| def generate_caption_clipgpt(image, max_tokens, temperature): | |
| caption = clipGPT.generate_caption_clipgpt(image, max_tokens, temperature) | |
| return caption | |
| def generate_caption_vitgpt(image, max_tokens, temperature): | |
| caption = vitGPT.generate_caption(image, max_tokens, temperature) | |
| return caption | |
| def generate_caption_vitCoAtt(image): | |
| caption = ViTCoAtt.CaptionSampler.main(image) | |
| return caption | |
| with gr.Blocks() as demo: | |
| gr.HTML("<h1 style='text-align: center;'>MedViT: A Vision Transformer-Driven Method for Generating Medical Reports 🏥🤖</h1>") | |
| gr.HTML("<p style='text-align: center;'>You can generate captions by uploading an X-Ray and selecting a model of your choice below</p>") | |
| with gr.Row(): | |
| image = gr.Image(label="Upload Chest X-ray", type="pil") | |
| sample_images_gallery = gr.Gallery(value = [ | |
| "https://imgur.com/W1pIr9b", | |
| "https://imgur.com/MLJaWnf", | |
| "https://imgur.com/6XymFW1", | |
| "https://imgur.com/zdPjZZ1", | |
| "https://imgur.com/DKUlZbF"], label="Sample Images", columns = 5) | |
| gr.HTML("<p style='text-align: center;'> Please select the Number of Max Tokens and Temperature setting, if you are testing CLIP GPT2 and VIT GPT2 Models</p>") | |
| with gr.Row(): | |
| with gr.Column(): # Column for dropdowns and model choice | |
| max_tokens = gr.Dropdown(list(range(50, 101)), label="Max Tokens", value=75) | |
| temperature = gr.Slider(0.5, 0.9, step=0.1, label="Temperature", value=0.7) | |
| model_choice = gr.Radio(["CLIP-GPT2", "ViT-GPT2", "ViT-CoAttention"], label="Select Model") | |
| generate_button = gr.Button("Generate Caption") | |
| caption = gr.Textbox(label="Generated Caption") | |
| def predict(img, model_name, max_tokens, temperature): | |
| if model_name == "CLIP-GPT2": | |
| return generate_caption_clipgpt(img, max_tokens, temperature) | |
| elif model_name == "ViT-GPT2": | |
| return generate_caption_vitgpt(img, max_tokens, temperature) | |
| elif model_name == "ViT-CoAttention": | |
| return generate_caption_vitCoAtt(img) | |
| else: | |
| return "Caption generation for this model is not yet implemented." | |
| # Event handlers | |
| generate_button.click(predict, [image, model_choice, max_tokens, temperature], caption) | |
| sample_images_gallery.select(predict, [sample_images_gallery, model_choice, max_tokens, temperature], caption) | |
| demo.launch() |