app.py

import gradio as gr
from gradio_client import Client

def get_caption(image_in):
    client = Client("https://vikhyatk-moondream1.hf.space/")
    result = client.predict(
		image_in,	# filepath  in 'image' Image component
		"Describe precisely the image in one sentence.",	# str  in 'Question' Textbox component
		api_name="/answer_question"
    )
    print(result)
    return result

def get_clm(prompt):
    client = Client("https://fffiloni-lcm-lora-for-sdxl.hf.space/")
    result = client.predict(
    		prompt,	# str  in 'parameter_5' Textbox component
    		0.3,	# float (numeric value between 0.0 and 5) in 'Guidance' Slider component
    		8,	# float (numeric value between 2 and 10) in 'Steps' Slider component
    		0,	# float (numeric value between 0 and 12013012031030) in 'Seed' Slider component
    		True,	# bool  in 'Randomize' Checkbox component
    							api_name="/predict"
    )
    print(result)
    return result

def infer(image_in):
    caption = get_caption(image_in)
    img_var = get_lcm(caption)
    return img_var

gr.Interface(
    fn = infer,
    inputs = [
        gr.Image(type="filepath")
    ],
    outputs = [
        gr.Image()
    ]
).launch()