Update app.py

app.py

@@ -1,52 +1,66 @@
+# Importing the Gradio library for creating interactive web interfaces
 import gradio as gr
+
+# Importing necessary libraries for image processing
 import numpy as np
 from sklearn.cluster import KMeans
 from sklearn.neighbors import NearestNeighbors
 
+# Function to perform k-means color quantization on an image
 def knn_color_quantization(image, n_colors):
+    # Reshaping the image into a 2D array of pixels
     pixels = np.reshape(image, (image.shape[0] * image.shape[1], image.shape[2]))
 
+    # Performing k-means clustering to find the dominant colors
     kmeans = KMeans(n_clusters=n_colors, random_state=42)
     kmeans.fit(pixels)
 
+    # Getting the cluster centers as the representative colors
     kmeans_centers = kmeans.cluster_centers_
+
+    # Fitting a nearest neighbors model to find the closest color for each pixel
     knn = NearestNeighbors(n_neighbors=1)
     knn.fit(kmeans_centers)
 
+    # Finding the closest color for each pixel and reconstructing the quantized image
     distances, indices = knn.kneighbors(pixels)
     quantized_pixels = kmeans_centers[indices.flatten()]
-
     quantized_image = np.reshape(quantized_pixels, image.shape)
-    quantized_image = quantized_image / 255.0
 
+    # Scaling the image values to the range [0, 1]
+    quantized_image = quantized_image / 255.0
 
     return quantized_image
 
+# Function to create the Gradio interface for color quantization
 def color_quantization_app(image, num_colors):
+    # Calling the knn_color_quantization function to quantize the image
     quantized_image = knn_color_quantization(image, num_colors)
+    # Converting the pixel values back to the range [0, 255] and casting to uint8
     quantized_image = (quantized_image * 255).astype(np.uint8)
     return quantized_image
 
-
-#themes
-#freddyaboulton/dracula_revamped
-#freddyaboulton/test-blue
-#Insuz/Mocha
-#Taithrah/Minimal
-
-dark_minimalist = gr.Theme.from_hub("Taithrah/Minimal")
-
+# Creating a Gradio interface object
 iface = gr.Interface(
+    # Specifying the function to be executed
     fn=color_quantization_app,
+    # Defining the inputs for the interface (an uploaded image and a slider for number of colors)
     inputs=[gr.Image(label="Uploaded Image", sources='upload', type="numpy"), gr.Slider(minimum=2,maximum=300,value=8, label='Number of Colors',interactive=True)],
+    # Defining the output for the interface (quantized image)
     outputs=gr.Image(label="Quantized Image"),
+    # Disabling live updates
     live=False,
+    # Disabling user flagging of outputs
     allow_flagging="never",
+    # Applying a custom theme retrieved from Gradio Hub
     theme=dark_minimalist,
+    # Adding custom CSS to hide the footer of the interface
     css="""
     footer {
         visibility: hidden;
     }
     """
 )
-iface.launch()
+
+# Launching the Gradio interface
+iface.launch()