Update app.py

app.py

@@ -1,58 +1,42 @@
 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"
+  results = client.predict(
+        prompt,  # existing parameters
+        num_images=4,  # New parameter to request 4 images. This parameter may be different for the actual API.
     )
-
-    # 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
-
+    # 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/")
-    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
+    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_vars = get_lcm(caption)
-    return caption, img_vars
+    img_var = get_lcm(caption)
+    return img_var
 
 gr.Interface(
-    title="ArXivGPT Image",
-    description=" Image2Image Variation - LCM SDXL & Moondream1 using for image generation",
-    fn=infer,
-    inputs=[
+    title = "ArXivGPT Image",
+    description = "Image to Image variation, using LCM SDXL & Moondream1",
+    fn = infer,
+    inputs = [
         gr.Image(type="filepath", label="Image input")
     ],
-    outputs=[
-        gr.Textbox(label="Caption"),
-        gr.Gallery(label="LCM Image variations")
+    outputs = [
+        gr.Image(label="Image variation")
     ]
-).queue(max_size=25).launch()
+).queue(max_size=25).launch()