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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() |