Update app.py

app.py

@@ -1,51 +1,40 @@
 import numpy as np
-import gradio as gr
 from skimage import io, color
-from numpy.linalg import norm
-from PIL import Image
+from skimage.transform import resize
+from sklearn.decomposition import TruncatedSVD
+import streamlit as st
 
-def svd_compress_color(image, k):
-    """Compress the color image using SVD by keeping only the top k singular values for each channel."""
-    # Initialize an empty array for the compressed image
-    compressed_image = np.zeros_like(image, dtype=np.float64)
-    
-    # Process each color channel separately
-    for channel in range(3):  # Assuming RGB
-        U, S, Vt = np.linalg.svd(image[:, :, channel], full_matrices=False)
-        compressed_channel = np.dot(U[:, :k], np.dot(np.diag(S[:k]), Vt[:k, :]))
-        compressed_image[:, :, channel] = compressed_channel
-    
-    # Clip values to valid range [0, 255] and convert to uint8
-    compressed_image = np.clip(compressed_image, 0, 255)
-    return compressed_image.astype(np.uint8)
+def resize_image(image, target_shape=(500, 500)):
+    """Resize image to reduce processing time."""
+    return resize(image, target_shape, anti_aliasing=True)
 
-def process_image_color(image, k):
-    """Process the uploaded image, compress it using SVD for each color channel, and return the result."""
-    # Convert PIL Image to NumPy array
-    image_np = np.array(image)
-    
-    # Ensure the image has three color channels
-    if image_np.ndim != 3 or image_np.shape[2] != 3:
-        raise ValueError("Input image must be a color image with 3 channels (RGB).")
-    
-    # Compress the image
-    compressed_image = svd_compress_color(image_np, k)
+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)
+    V = svd.components_
+    S = svd.singular_values_
+    compressed_image = np.dot(U, np.dot(np.diag(S), V))
+    return compressed_image
+
+# Streamlit app
+st.title("Fast Image Compression using Truncated SVD")
+
+# Upload an image
+uploaded_file = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg"])
+if uploaded_file is not None:
+    # Load the image
+    image = io.imread(uploaded_file)
+    gray_image = color.rgb2gray(image)
     
-    # Convert compressed image back to PIL Image for Gradio output
-    compressed_image_pil = Image.fromarray(compressed_image)
+    # Resize the image for faster processing
+    resized_image = resize_image(gray_image)
     
-    return compressed_image_pil
+    # Select compression rank
+    k = st.slider("Select the rank for compression", min_value=1, max_value=min(resized_image.shape), value=50)
 
-# Gradio interface
-gr_interface = gr.Interface(
-    fn=process_image_color,
-    inputs=[
-        gr.Image(type="pil", label="Upload Image"),
-        gr.Slider(1, 100, step=1, value=50, label="Compression Rank")
-    ],
-    outputs=gr.Image(type="pil", label="Compressed Image"),
-    title="Interactive Image Compression using SVD",
-    description="Upload a color image and adjust the compression rank to see the compressed version while retaining color."
-)
+    # Compress the image using Truncated SVD
+    compressed_image = truncated_svd_compress(resized_image, k)
 
-gr_interface.launch()
+    # Display original and compressed images
+    st.image([gray_image, compressed_image], caption=["Original", "Compressed"])