Create app.py

app.py

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+import gradio as gr
+from collections import Counter
+from sklearn.cluster import KMeans
+from matplotlib import colors
+import matplotlib.pyplot as plt
+import numpy as np
+import cv2
+
+def rgb_to_hex(rgb_color):
+    hex_color = "#"
+    for i in rgb_color:
+        hex_color += ("{:02x}".format(int(i)))
+    return hex_color
+
+def preprocess(raw):
+    image = cv2.resize(raw, (900, 600), interpolation = cv2.INTER_AREA)
+    image = image.reshape(image.shape[0]*image.shape[1], 3)
+    return image
+
+def analyze(img,n_cluster ):
+    modified_image = preprocess(img)    
+    clf = KMeans(n_clusters = n_cluster)
+    color_labels = clf.fit_predict(modified_image)
+    center_colors = clf.cluster_centers_
+    counts = Counter(color_labels)
+    ordered_colors = [center_colors[i] for i in counts.keys()]
+    hex_colors = [rgb_to_hex(ordered_colors[i]) for i in counts.keys()]
+
+    plot = plt.figure(figsize = (12, 8))
+    plt.pie(counts.values(), labels = hex_colors, autopct='%1.1f%%', colors = hex_colors)
+
+    plt.savefig("color_classifier_pie.png")
+    print(str(n_cluster) + " the most dominant colors:\n")
+    for color in hex_colors:
+      print(color)
+    
+    return plot
+
+color_picker = gr.Interface(fn=analyze, inputs=["image", gr.inputs.Slider(minimum=2, maximum=10, step=1, label="Number of claster")], outputs="plot")
+color_picker.launch()