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#!/usr/bin/env python
import pathlib
import shlex
import subprocess
import gradio as gr
from model import Model
from settings import CACHE_EXAMPLES, MAX_SEED
from utils import randomize_seed_fn
def create_demo(model: Model) -> gr.Blocks:
if not pathlib.Path('corgi.png').exists():
subprocess.run(
shlex.split(
'wget https://raw.githubusercontent.com/openai/shap-e/d99cedaea18e0989e340163dbaeb4b109fa9e8ec/shap_e/examples/example_data/corgi.png -O corgi.png'
))
examples = ['corgi.png']
def process_example_fn(image_path: str) -> str:
return model.run_image(image_path)
with gr.Blocks() as demo:
with gr.Box():
image = gr.Image(label='Input image',
show_label=False,
type='filepath')
run_button = gr.Button('Run')
result = gr.Model3D(label='Result', show_label=False)
with gr.Accordion('Advanced options', open=False):
seed = gr.Slider(label='Seed',
minimum=0,
maximum=MAX_SEED,
step=1,
value=0)
randomize_seed = gr.Checkbox(label='Randomize seed',
value=True)
guidance_scale = gr.Slider(label='Guidance scale',
minimum=1,
maximum=20,
step=0.1,
value=3.0)
num_inference_steps = gr.Slider(
label='Number of inference steps',
minimum=1,
maximum=100,
step=1,
value=64)
gr.Examples(examples=examples,
inputs=image,
outputs=result,
fn=process_example_fn,
cache_examples=CACHE_EXAMPLES)
inputs = [
image,
seed,
guidance_scale,
num_inference_steps,
]
run_button.click(
fn=randomize_seed_fn,
inputs=[seed, randomize_seed],
outputs=seed,
queue=False,
).then(
fn=model.run_image,
inputs=inputs,
outputs=result,
)
return demo
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