add array to hex

myapp/palette.py

@@ -22,9 +22,16 @@ def generate_palette_image(model: KMeans, size=40):
     return image
 
 
-def extract_color_clusters(image_array: np.ndarray):
+def extract_color_clusters(image_array: np.ndarray | Image.Image, n_clusters=2):
+    if not isinstance(image_array, np.ndarray):
+        image_array = np.array(image_array)
+
     w, h, d = image_array.shape
     pixels = image_array.reshape(w * h, d)
-    model = KMeans(n_clusters=4).fit(pixels)
 
-    return model
+    return KMeans(n_clusters=n_clusters).fit(pixels)
+
+
+def array_to_hex(values: np.ndarray):
+    values = np.round(values).astype(int)
+    return "#" + ("{:02X}" * len(values)).format(*values)

myapp/pall_app.py

@@ -1,20 +1,19 @@
 import gradio as gr
 import numpy as np
-from sklearn.cluster import KMeans
+
+from myapp.palette import array_to_hex, extract_color_clusters
 
 with gr.Blocks() as demo:
     image = gr.Image("vulture.webp")
-    n_colors = gr.Slider(1, 16, 4)
+    n_colors = gr.Slider(1, 16, 4, step=1)
     button = gr.Button()
-    plot = gr.Plot()
 
-    @button.click(inputs=image, outputs=gr.JSON())
-    def get_palette(image_array: np.ndarray):
-        w, h, d = image_array.shape
-        pixels = image_array.reshape(w * h, d)
-        model = KMeans(n_clusters=4).fit(pixels)
+    @gr.render(inputs=[image, n_colors])
+    def render_palette(image_array: np.ndarray, n_clusers: int):
+        model = extract_color_clusters(image_array, n_clusers)
 
-        return model.cluster_centers_
+        for cluster in model.cluster_centers_:
+            gr.ColorPicker(array_to_hex(cluster))
 
 
 if __name__ == "__main__":