Create app.py

app.py

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+import gradio as gr
+import cv2
+import numpy as np
+from sklearn.cluster import KMeans
+from PIL import Image
+import matplotlib.pyplot as plt
+import io
+import base64
+
+def extract_colors(image, num_colors=5):
+    image = image.resize((200, 200))  # Downsize for faster processing
+    img_array = np.array(image)
+    img_array = img_array.reshape((-1, 3))
+
+    kmeans = KMeans(n_clusters=num_colors)
+    kmeans.fit(img_array)
+    colors = kmeans.cluster_centers_.astype(int)
+
+    hex_colors = ['#{:02x}{:02x}{:02x}'.format(*color) for color in colors]
+    return hex_colors
+
+def plot_palette(hex_colors):
+    fig, ax = plt.subplots(1, figsize=(5, 1))
+    for i, color in enumerate(hex_colors):
+        ax.add_patch(plt.Rectangle((i, 0), 1, 1, color=color))
+    plt.xlim(0, len(hex_colors))
+    plt.axis('off')
+
+    buf = io.BytesIO()
+    plt.savefig(buf, format='png', bbox_inches='tight', pad_inches=0)
+    plt.close(fig)
+    buf.seek(0)
+    return Image.open(buf)
+
+def process_image(image):
+    image = Image.fromarray(image)
+    colors = extract_colors(image)
+    palette_img = plot_palette(colors)
+    return palette_img, "\n".join(colors)
+
+demo = gr.Interface(
+    fn=process_image,
+    inputs=gr.Image(type="numpy", label="Upload an Image"),
+    outputs=[
+        gr.Image(type="pil", label="Generated Palette"),
+        gr.Textbox(label="Hex Codes")
+    ],
+    title="AI Color Palette Generator",
+    description="Upload an image and extract dominant colors using K-Means. Lightweight & Fast!"
+)
+
+if __name__ == "__main__":
+    demo.launch()