#!/usr/bin/env python

import gradio as gr
import PIL.Image
import os
from gradio_client import Client, file

lgm_mini_client = Client("dylanebert/LGM-mini")
triposr_client = Client("stabilityai/TripoSR")
crm_client = Client("Zhengyi/CRM")

def run(image, model_name):
    file_path = "temp.png"
    image.save(file_path)
    
    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")
        
        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]

    elif model=='crm':
        preprocess_result = crm_client.predict(
		file(file_path),	# filepath in 'Image input' Image component
		"Auto Remove background",	# Literal['Alpha as mask', 'Auto Remove background'] in 'backgroud choice' Radio component
		1,	# float (numeric value between 0.5 and 1.0) in 'Foreground Ratio' Slider component
		"#000000",	# str in 'Background Color' Colorpicker component
		api_name="/preprocess_image"
        )

        result = crm_client.predict(
    		file(preprocess_result), # filepath in 'Processed Image' Image component
    		1234,	# float in 'seed' Number component
    		5.5,	# float in 'guidance_scale' Number component
    		30,	# float in 'sample steps' Number component
    		api_name="/gen_image"
        )
        output = result[2]
    return output


demo = gr.Interface(
    fn=run,
    inputs=[gr.Image(type="pil"),gr.Textbox(label="Model Name")],
    outputs=gr.Model3D(label="3D Model"),
    api_name="synthesize"
)

demo.launch()