Update app.py

app.py

@@ -4,67 +4,38 @@ import tempfile
 from modelscope.outputs import OutputKeys
 from modelscope.pipelines import pipeline
 from modelscope.utils.constant import Tasks
-import numpy as np
+from pathlib import Path
 import gradio as gr
+import numpy as np
 
-# Load model once at startup
+# Load the model into memory to make running multiple predictions efficient
 img_colorization = pipeline(Tasks.image_colorization, model='iic/cv_ddcolor_image-colorization')
 
 def inference(img):
-    """Process input image and return colorized output"""
     image = cv2.imread(str(img))
     output = img_colorization(image[..., ::-1])
     result = output[OutputKeys.OUTPUT_IMG].astype(np.uint8)
-    
-    # Save result to temporary directory
+
     temp_dir = tempfile.mkdtemp()
-    out_path = os.path.join(temp_dir, 'colorized.png')
+    out_path = os.path.join(temp_dir, 'old-to-color.png')
     cv2.imwrite(out_path, result)
     return Path(out_path)
 
-# Modern UI design
-with gr.Blocks(theme="default", css=".container {max-width: 1000px; margin: auto;}") as demo:
-    # Header section
-    gr.Markdown("## 🎨 Image Colorization Studio\n*Transform your black-and-white images into vibrant color masterpieces*")
-    
-    # Input/Output layout
+# Modernized UI using Gradio 3.9 components
+title = "🌈 Color Restorization Model"
+description = "Upload a black & white photo to restore it in color using a deep learning model."
+
+with gr.Blocks(title=title) as demo:
+    gr.Markdown(f"## {title}")
+    gr.Markdown(description)
+
     with gr.Row():
         with gr.Column():
-            input_img = gr.Image(
-                label="Grayscale Image",
-                type="filepath",
-                elem_id="input-image"
-            )
-            submit_btn = gr.Button("🎨 Colorize Image", variant="primary")
-        
+            input_image = gr.Image(type="filepath", label="Upload B&W Image")
+            submit_btn = gr.Button("Colorize")
         with gr.Column():
-            output_img = gr.Image(label="Colorized Result", elem_id="output-image")
-            download_btn = gr.File(label="Download Result")
-    
-    # Examples section
-    gr.Examples(
-        examples=[
-            ["examples/vintage.jpg"],
-            ["examples/portrait.png"],
-            ["examples/architecture.jpeg"]
-        ],
-        inputs=input_img,
-        outputs=output_img,
-        fn=inference,
-        cache_examples=True
-    )
-    
-    # Event handlers
-    submit_btn.click(
-        fn=inference,
-        inputs=[input_img],
-        outputs=[output_img]
-    )
-    output_img.change(
-        fn=lambda img: gr.File.update(value=img) if img else None,
-        inputs=[output_img],
-        outputs=[download_btn]
-    )
+            output_image = gr.Image(type="pil", label="Colorized Output")
+
+    submit_btn.click(fn=inference, inputs=input_image, outputs=output_image)
 
-# Launch configuration
-demo.launch(enable_queue=True)
+demo.launch(enable_queue=True)