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#!/usr/bin/env python
import gradio as gr
import PIL.Image
from gradio_client import Client
lgm_mini_client = Client("dylanebert/LGM-mini")
triposr_client = Client("stabilityai/TripoSR")
def run(image, model_name):
file_path = "temp.png"
image.save(file_path)
print(file_path)
model_name = model_name.lower()
if model_name=='lgm-mini':
result = lgm_mini_client.predict(
file_path, # filepath in 'image' Image component
api_name="/run"
)
output = result
elif model_name=='triposr':
process_result = triposr_client.predict(
file_path, # filepath in 'Input Image' Image component
True, # bool in 'Remove Background' Checkbox component
0.85, # float (numeric value between 0.5 and 1.0) in 'Foreground Ratio' Slider component
api_name="/preprocess")
print(type(process_result))
result = triposr_client.predict(
process_result, # filepath in 'Processed Image' Image component
256, # float (numeric value between 32 and 320) in 'Marching Cubes Resolution' Slider component
api_name="/generate")
output = result[0]
return output
with gr.Blocks() as demo:
with gr.Group():
with gr.Row(variant='panel'):
with gr.Column(scale=1):
image = gr.Image(label="Input image", show_label=False, type="pil", height=180)
model_name = gr.Textbox(label="Model name", show_label=False)
run_button = gr.Button("Run")
with gr.Column(scale=1):
result = gr.Model3D(label="Result", show_label=False)
run_button.click(
fn=run,
inputs=[
image,
model_name
],
outputs=result,
api_name="synthesize"
)
demo.launch() |