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import gradio as gr
from gradio_client import Client
import json
def get_caption(image_in):
client = Client("https://vikhyatk-moondream1.hf.space/")
result = client.predict(
image_in, # filepath in 'image' Image component
"Describe the image", # str in 'Question' Textbox component
api_name="/answer_question"
)
# JSON 형태로 파싱
try:
result_json = json.loads(result)
caption = result_json['choices'][0]['text']
except json.JSONDecodeError:
print("Error: Response is not valid JSON.")
return ""
except (KeyError, IndexError):
print("Error: Invalid format in JSON response.")
return ""
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()
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