Update app.py

app.py

@@ -1,2 +1,104 @@
-import os
-exec(os.environ.get('APP’)
+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):
+    image = np.array(image)
+    normalized_intensity = intensity / 100.0
+
+    if filter_type == "Grayscale":
+        return convert_to_grayscale(image)
+    elif filter_type == "Soft Glow":
+        base_intensity = 0.1
+        adjusted_intensity = base_intensity + (normalized_intensity * (1 - base_intensity))
+        gaussian = cv2.GaussianBlur(image, (15, 15), 0)
+        soft_glow = cv2.addWeighted(image, 1 - adjusted_intensity, gaussian, adjusted_intensity, 0)
+        return soft_glow
+    elif filter_type == "Portrait Enhancer":
+        base_intensity = 0.5
+        adjusted_intensity = base_intensity + (normalized_intensity * (1 - base_intensity))
+        image_pil = Image.fromarray(image)
+        enhancer = ImageEnhance.Sharpness(image_pil)
+        image_pil = enhancer.enhance(1 + 0.5 * adjusted_intensity)
+        enhancer = ImageEnhance.Color(image_pil)
+        image_pil = enhancer.enhance(1 + 0.5 * adjusted_intensity)
+        enhanced_image = np.array(image_pil)
+        return enhanced_image
+    elif filter_type == "Warm Tone":
+        warm_image = cv2.addWeighted(image, 1.0, np.full(image.shape, (20, 66, 112), dtype=np.uint8), 0.3 * normalized_intensity, 0)
+        return warm_image
+    elif filter_type == "Cold Tone":
+        cold_image = cv2.addWeighted(image, 1.0, np.full(image.shape, (112, 66, 20), dtype=np.uint8), 0.3 * normalized_intensity, 0)
+        return cold_image
+    elif filter_type == "High-Key":
+        high_key = cv2.convertScaleAbs(image, alpha=1.0 + 0.3 * normalized_intensity, beta=20)
+        return high_key
+    elif filter_type == "Low-Key":
+        low_key = cv2.convertScaleAbs(image, alpha=1.0 - 0.1 * normalized_intensity, beta=-10)
+        return low_key
+    elif filter_type == "Haze":
+        haze = cv2.addWeighted(image, 1.0, np.full(image.shape, 255, dtype=np.uint8), 0.3 * normalized_intensity, 0)
+        return haze
+    else:
+        return image
+
+def convert_to_grayscale(image):
+    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    return cv2.cvtColor(gray_image, cv2.COLOR_GRAY2BGR)
+
+def convert_and_save(image, filter_type, intensity):
+    image_cv = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+    filtered_image = apply_filter(image_cv, filter_type, intensity)
+    
+    original_image_pil = Image.fromarray(cv2.cvtColor(image_cv, cv2.COLOR_BGR2RGB))
+    filtered_image_pil = Image.fromarray(cv2.cvtColor(filtered_image, cv2.COLOR_BGR2RGB))
+
+    output_path = "filtered_image.jpg"
+    filtered_image_pil.save(output_path)
+
+    return [original_image_pil, filtered_image_pil], output_path
+
+def get_filter_description(filter_type):
+    descriptions = {
+        "Grayscale": "이미지를 흑백으로 변환합니다.",
+        "Soft Glow": "부드러운 빛을 추가하여 이미지를 은은하게 만듭니다.",
+        "Portrait Enhancer": "피부 톤을 균일하게 하고 선명도를 조절하여 인물을 더욱 돋보이게 만듭니다.",
+        "Warm Tone": "따뜻한 색조를 추가하여 이미지에 온기를 더합니다.",
+        "Cold Tone": "차가운 색조를 추가하여 이미지에 시원함을 더합니다.",
+        "High-Key": "밝고 화사한 이미지를 만들어냅니다.",
+        "Low-Key": "어두운 톤을 강조하여 분위기 있는 이미지를 만듭니다.",
+        "Haze": "부드럽고 흐릿한 효과를 추가하여 몽환적인 이미지를 만듭니다."
+    }
+    return descriptions.get(filter_type, "")
+
+with gr.Blocks() as iface:
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(type="pil", label="이미지 업로드")
+            filter_input = gr.Radio(
+                ["Grayscale", "Soft Glow", "Portrait Enhancer", "Warm Tone", "Cold Tone", "High-Key", "Low-Key", "Haze"], 
+                label="필터 선택",
+                value="Soft Glow"
+            )
+            intensity_slider = gr.Slider(1, 100, value=50, label="필터 강도")
+            description_output = gr.Markdown(get_filter_description("Soft Glow"))
+
+        with gr.Column():
+            slider_output = ImageSlider(label="Before and After", type="pil")
+            download_link = gr.File(label="Download Filtered Image")
+
+    filter_input.change(fn=get_filter_description, 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=[slider_output, download_link]
+    )
+
+    iface.title = "인물 사진에 최적화된 필터"
+    iface.description = "인물 사진에 최적화된 다양한 필터를 적용할 수 있습니다."
+
+iface.launch()