Update app.py

app.py

@@ -1,57 +1,37 @@
 import numpy as np
 import gradio as gr
-from skimage import color
-from sklearn.decomposition import TruncatedSVD
+from skimage import io, color
+from numpy.linalg import norm
 from PIL import Image
 
-def truncated_svd_compress(image, k):
-    """Compress the image using Truncated SVD by keeping only the top k singular values."""
-    svd = TruncatedSVD(n_components=k)
-    U = svd.fit_transform(image)
-    S = svd.singular_values_
-    Vt = svd.components_
-    compressed_image = np.dot(U, np.dot(np.diag(S), Vt))
+def svd_compress(image, k):
+    """Compress the image using SVD by keeping only the top k singular values."""
+    U, S, Vt = np.linalg.svd(image, full_matrices=False)
+    compressed_image = np.dot(U[:, :k], np.dot(np.diag(S[:k]), Vt[:k, :]))
     return compressed_image
 
 def process_image(image, k):
-    """Process the uploaded image, compress it using Truncated SVD, and return the result."""
+    """Process the uploaded image, compress it using SVD, and return the result."""
     # Convert PIL Image to NumPy array
     image_np = np.array(image)
     
-    # Convert to grayscale for SVD
+    # Convert to grayscale
     gray_image = color.rgb2gray(image_np)
     
-    # Ensure the image is 2D for SVD
-    if len(gray_image.shape) == 3:
-        gray_image = gray_image[:, :, 0]
-    
-    # Compress the image using Truncated SVD
-    compressed_image = truncated_svd_compress(gray_image, k)
-    
-    # Normalize the compressed image to the range [0, 255] and convert to uint8
-    compressed_image = np.clip(compressed_image, 0, 1)  # Ensure values are within [0, 1]
-    compressed_image = (compressed_image * 255).astype(np.uint8)
+    # Compress the image
+    compressed_image = svd_compress(gray_image, k)
     
     # Convert compressed image back to PIL Image for Gradio output
-    compressed_image_pil = Image.fromarray(compressed_image)
-    
-    # Ensure the PIL Image is in RGB mode for consistent display
-    if compressed_image_pil.mode != 'RGB':
-        compressed_image_pil = compressed_image_pil.convert('RGB')
+    compressed_image_pil = Image.fromarray((compressed_image * 255).astype(np.uint8))
     
     return compressed_image_pil
 
 # Gradio interface
-gr_interface = gr.Interface(
-    fn=process_image,  # Image processing function
-    inputs=[
-        gr.Image(type="pil", label="Upload Image"),
-        gr.Slider(1, 100, step=1, value=50, label="Compression Rank")  # Compression rank slider
-    ],
-    outputs=gr.Image(type="pil", label="Compressed Image"),
-    title="Interactive Image Compression using Truncated SVD",
-    description="Upload an image and adjust the compression rank to see the compressed version. The app compresses the image while retaining important features."
-)
+gr.Interface(fn=process_image,
+             inputs=[gr.Image(type="pil", shape=(500, 500)), 
+                     gr.Slider(1, 100, step=1, value=50, label="Compression Rank")],
+             outputs=gr.Image(type="pil"),
+             title="Interactive Image Compression using SVD",
+             description="Upload an image (500x500 max) and adjust the compression rank.")
 
-# Launch the Gradio interface
 gr_interface.launch()