Create app.py

app.py

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+import gradio as gr
+from color_matcher import ColorMatcher
+from color_matcher.normalizer import Normalizer
+import numpy as np
+import cv2
+from PIL import Image
+
+# Function to apply color correction
+def color_match(source_img, reference_img):
+    # Convert PIL images to OpenCV format (numpy arrays)
+    img_src = np.array(source_img)
+    img_ref = np.array(reference_img)
+    
+    # Ensure images are in RGB format (3 channels)
+    if img_src.shape[2] == 4:
+        img_src = cv2.cvtColor(img_src, cv2.COLOR_RGBA2RGB)
+    if img_ref.shape[2] == 4:
+        img_ref = cv2.cvtColor(img_ref, cv2.COLOR_RGBA2RGB)
+
+    # Apply color matching
+    cm = ColorMatcher()
+    img_res = cm.transfer(src=img_src, ref=img_ref, method='mkl')
+    
+    # Normalize the result
+    img_res = Normalizer(img_res).uint8_norm()
+
+    # Convert back to PIL for displaying in Gradio
+    img_res_pil = Image.fromarray(img_res)
+    
+    return img_res_pil
+
+# Gradio Interface
+def gradio_interface():
+    # Define input and output components
+    inputs = [
+        gr.Image(type="pil", label="Source Image"),
+        gr.Image(type="pil", label="Reference Image")
+    ]
+    outputs = gr.Image(type="pil", label="Resulting Image")
+
+    # Launch Gradio app
+    gr.Interface(fn=color_match, inputs=inputs, outputs=outputs, title="Color Matching Tool").launch()
+
+# Run the Gradio Interface
+if __name__ == "__main__":
+    gradio_interface()