Create app.py

app.py

@@ -0,0 +1,45 @@
+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()