app.py

import gradio as gr
from gradio_client import Client

def get_caption(image_in):
    client = Client("https://vikhyatk-moondream1.hf.space/")
  results = client.predict(
        prompt,  # existing parameters
        num_images=4,  # New parameter to request 4 images. This parameter may be different for the actual API.
    )
    # Print and return the results assuming it contains 4 images
    print(results)
    return results[:4]  # Return first 4 images in case the API returns more

def get_lcm(prompt):
    client = Client("https://latent-consistency-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[0]

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

gr.Interface(
    title = "ArXivGPT Image",
    description = "Image to Image variation, using LCM SDXL & Moondream1",
    fn = infer,
    inputs = [
        gr.Image(type="filepath", label="Image input")
    ],
    outputs = [
        gr.Image(label="Image variation")
    ]
).queue(max_size=25).launch()