app.py

#!/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()