app_caption.py

#!/usr/bin/env python

from __future__ import annotations

import os
import pathlib
import gradio as gr

from prismer_model import Model


def create_demo() -> gr.Blocks:
    model = Model()
    model.mode = 'caption'
    with gr.Blocks() as demo:
        with gr.Row():
            with gr.Column():
                image = gr.Image(label='Input', type='filepath')
                model_name = gr.Dropdown(label='Model', choices=['Prismer-Base', 'Prismer-Large'], value='Prismer-Base')
                run_button = gr.Button('Run')
            with gr.Column(scale=1.5):
                caption = gr.Text(label='Model Prediction')
                with gr.Row():
                    depth = gr.Image(label='Depth')
                    edge = gr.Image(label='Edge')
                    normals = gr.Image(label='Normals')
                with gr.Row():
                    segmentation = gr.Image(label='Segmentation')
                    object_detection = gr.Image(label='Object Detection')
                    ocr = gr.Image(label='OCR Detection')

        inputs = [image, model_name]
        outputs = [caption, depth, edge, normals, segmentation, object_detection, ocr]

        paths = sorted(pathlib.Path('prismer/images').glob('*'))
        examples = [[path.as_posix(), 'Prismer-Base'] for path in paths]
        gr.Examples(examples=examples,
                    inputs=inputs,
                    outputs=outputs,
                    fn=model.run_caption,
                    cache_examples=os.getenv('SYSTEM') == 'spaces')

        run_button.click(fn=model.run_caption, inputs=inputs, outputs=outputs)
    return demo


if __name__ == '__main__':
    demo = create_demo()
    demo.queue().launch()