Create app.py

app.py

@@ -0,0 +1,94 @@
+import os
+import cv2
+import tempfile
+import numpy as np
+import gradio as gr
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+from pathlib import Path
+
+# Initialize model with optimized settings
+@gr.on(app_started=True)
+def load_model():
+    global img_colorization
+    img_colorization = pipeline(
+        Tasks.image_colorization, 
+        model='iic/cv_ddcolor_image-colorization',
+        model_revision='v1.0.0'
+    )
+
+def inference(img):
+    if img is None:
+        raise gr.Error("Please upload an image first")
+    
+    with tempfile.TemporaryDirectory() as temp_dir:
+        # Convert PIL image to numpy array if needed
+        if isinstance(img, np.ndarray):
+            image = img
+        else:
+            image = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
+        
+        # Process image
+        output = img_colorization(image[..., ::-1])
+        result = output['output_img'].astype(np.uint8)
+        
+        # Save result
+        out_path = os.path.join(temp_dir, 'colorized.png')
+        cv2.imwrite(out_path, result)
+        return Path(out_path), "✅ Colorization completed successfully!"
+
+# Create modern UI with Blocks
+with gr.Blocks(theme="soft", title="🎨 AI Color Restoration Studio") as demo:
+    gr.Markdown("""
+    # 🎨 AI Color Restoration Studio
+    Transform your black & white photos into vibrant colorized versions using state-of-the-art AI!
+    
+    Upload an image and watch as our deep learning model automatically adds natural colors.
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            input_img = gr.Image(
+                label="Upload Monochrome Image",
+                type="pil",
+                height=400,
+                sources=["upload"],
+                interactive=True
+            )
+            submit_btn = gr.Button("✨ Colorize Image", variant="primary")
+            clear_btn = gr.ClearButton()
+            
+        with gr.Column(scale=1):
+            output_img = gr.Image(
+                label="Colorized Result",
+                type="pil",
+                height=400,
+                interactive=False
+            )
+            download_btn = gr.File(label="Download Result")
+            status = gr.Textbox(label="Status", interactive=False)
+    
+    # Examples section
+    gr.Examples(
+        examples=[
+            ["examples/1.jpg"],
+            ["examples/2.jpg"],
+            ["examples/3.jpg"]
+        ],
+        inputs=[input_img],
+        outputs=[output_img, status],
+        fn=inference,
+        cache_examples=True
+    )
+    
+    # Event handlers
+    submit_btn.click(
+        fn=inference,
+        inputs=[input_img],
+        outputs=[output_img, status]
+    )
+    
+    clear_btn.add([input_img, output_img, status])
+
+if __name__ == "__main__":
+    demo.launch(debug=True)