Update app.py

app.py

@@ -2,8 +2,12 @@ import gradio as gr
 import cv2
 import numpy as np
 from PIL import Image, ImageEnhance
+from gradio_imageslider import ImageSlider
 
 def apply_filter(image, filter_type, intensity):
+    # PIL 이미지를 numpy array로 변환
+    image = np.array(image)
+
     # 강도를 0.0에서 1.0 사이로 정규화
     normalized_intensity = intensity / 100.0
 
@@ -62,9 +66,7 @@ def convert_to_grayscale(image):
 
 def convert_and_save(image, filter_type, intensity):
     filtered_image = apply_filter(image, filter_type, intensity)
-    output_path = "output.jpg"
-    cv2.imwrite(output_path, filtered_image)
-    return filtered_image, output_path
+    return Image.fromarray(image), Image.fromarray(filtered_image)  # 원본과 필터 적용된 이미지를 반환
 
 def get_filter_description(filter_type):
     descriptions = {
@@ -92,10 +94,18 @@ with gr.Blocks() as iface:
             description_output = gr.Markdown(get_filter_description("Soft Glow"))
 
         with gr.Column():
-            output_image = gr.Image(type="pil", label="결과 이미지")
+            slider_output = ImageSlider(label="Before and After", type="pil")
+
+    filter_input.change(fn=get_filter_description, inputs=filter_input, outputs=description_output)
 
-    filter_input.change(fn=lambda filter_type: get_filter_description(filter_type), inputs=filter_input, outputs=description_output)
     process_button = gr.Button("필터 적용")
-    process_button.click(fn=convert_and_save, inputs=[image_input, filter_input, intensity_slider], outputs=output_image)
+    process_button.click(
+        fn=convert_and_save,
+        inputs=[image_input, filter_input, intensity_slider],
+        outputs=slider_output
+    )
+
+    iface.title = "인물 사진에 최적화된 필터"
+    iface.description = "인물 사진에 최적화된 다양한 필터를 적용할 수 있습니다."
 
 iface.launch()