Spaces:
Runtime error
Runtime error
File size: 1,834 Bytes
7617596 3b61cce 64fb58a 3b61cce 64fb58a 7617596 64fb58a 7617596 6eaf487 64fb58a 3b61cce b734d92 a8208b6 3b61cce 53b7b42 64fb58a 3b61cce 64fb58a 3b61cce ef365f5 0312353 3b61cce 1dd8a60 3b61cce 0312353 3b61cce 7617596 3b61cce 64fb58a 3b61cce |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 |
#!/usr/bin/env python
from __future__ import annotations
import os
import pathlib
import gradio as gr
from prismer_model import Model
def create_demo():
model = Model()
model.mode = 'caption'
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')
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)
run_button.click(fn=model.run_caption, inputs=inputs, outputs=outputs)
if __name__ == '__main__':
demo = create_demo()
demo.queue().launch()
|