app.py

import torch 
import re 
import gradio as gr
from transformers import pipeline

device='cpu'

image_captioner = pipeline("image-to-text",
 model="eduardofarina/MultimodalXray",
  device = device)
generate_kwargs = {
    "max_length":160
}


def predict(image):
    image = image.convert('RGB')
    with torch.no_grad():  
        caption = image_captioner(image, generate_kwargs=generate_kwargs)
    return caption[0]['generated_text']



input = gr.inputs.Image(label="Upload any Chest Xray", type = 'pil', optional=True)
output = gr.outputs.Textbox(type="text",label="Preliminary Radiology Report")

title = "X-Ray Report Generation "
description = "Not for clinical use. The examples are cases from Radiopaedia"

examples = ["example_1.jpeg", "example_2.jpeg"]

interface = gr.Interface(
            
        fn=predict,
        description=description,
        inputs = input,
        examples = examples,
        theme="grass",
        outputs=output,
        title=title,
    )
interface.launch(debug=True)