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,  
        "Describe the image", 
        api_name="/answer_question"
    )
    caption = result['choices'][0]['text']
    return caption

def get_lcm(prompt):
    client = Client("https://latent-consistency-lcm-lora-for-sdxl.hf.space/")
    images = []
    for _ in range(4):  # 4개의 이미지 생성
        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"
        )
        images.append(result[0])
    return images

def infer(image_in):
    caption = get_caption(image_in)
    img_vars = get_lcm(caption)
    return caption, img_vars

gr.Interface(
    title="ArXivGPT Image",
    description=" Image2Image Variation - LCM SDXL & Moondream1 using for image generation",
    fn=infer,
    inputs=[
        gr.Image(type="filepath", label="Image input")
    ],
    outputs=[
        gr.Textbox(label="Caption"),
        gr.Gallery(label="LCM Image variations")
    ]
).queue(max_size=25).launch()