Spaces:
Sleeping
Sleeping
File size: 1,869 Bytes
19327c9 53b7b42 19327c9 53b7b42 19327c9 ef365f5 53b7b42 19327c9 806eb00 19327c9 806eb00 19327c9 a8208b6 19327c9 |
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 |
#!/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()
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')
question = gr.Text(label='Question')
run_button = gr.Button('Run')
with gr.Column(scale=1.5):
answer = 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, question]
outputs = [answer, depth, edge, normals, segmentation, object_detection, ocr]
paths = sorted(pathlib.Path('prismer/images').glob('*'))
ex_questions = ['What is the man on the left doing?',
'What is this person doing?',
'How many cows in this image?',
'What is the type of animal in this image?',
'What toy is it?']
examples = [[path.as_posix(), 'Prismer-Base', ex_questions[i]] for i, path in enumerate(paths)]
gr.Examples(examples=examples,
inputs=inputs,
outputs=outputs,
fn=model.run_vqa,
cache_examples=os.getenv('SYSTEM') == 'spaces')
run_button.click(fn=model.run_vqa, inputs=inputs, outputs=outputs)
if __name__ == '__main__':
demo = create_demo()
demo.queue().launch()
|