app.py

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)
    return caption

def generate_caption_vitgpt(image, max_tokens, temperature):
    caption = vitGPT.generate_caption(image)
    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():
        sample_images = [
        'https://imgur.com/W1pIr9b',
        'https://imgur.com/MLJaWnf',
        'https://imgur.com/6XymFW1',
        'https://imgur.com/zdPjZZ1',
        'https://imgur.com/DKUlZbF'
            ]

        
        image = gr.Image(label="Upload Chest X-ray", type="pil")    
        
        sample_images_gallery = gr.Gallery(value = sample_images,label="Sample Images")

    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):
        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.change(predict, [sample_images_gallery, model_choice, max_tokens, temperature], caption)  


demo.launch()