app.py

from inference import prediction
import gradio as gr
import json

title = "Interactive demo: LayoutLMv3 for receipts"
description = "Demo for Microsoft's LayoutLMv3, a Transformer for state-of-the-art document image understanding tasks. This particular space uses an instance of the model fine-tuned on a dataset that combines CORD and SROIE.\n To use it, simply upload an image or use the example image below. Results will show up in a few seconds."
examples = [['image.jpg'],['image.PNG']]

css = """.output_image, .input_image {height: 600px !important}, .output_json { color: black; }"""

# gradio interface that takes in input an image and return a JSON file that contains its info
# for now it shows also the intermediate steps
iface = gr.Interface(theme=gr.themes.Monochrome(),
                    fn=prediction,
                     inputs=gr.Image(type="pil"),
                     outputs=[
                              #gr.Image(type="pil", label="annotated image"),
                              gr.JSON(label="final output")],
                     title=title,
                     description=description,
                     examples=examples,
                     css=css)

iface.launch()