Update app.py

app.py

@@ -1,44 +1,33 @@
 import numpy as np
+import gradio as gr
 from skimage import io, color
-from skimage.transform import resize
-from sklearn.decomposition import TruncatedSVD
-import streamlit as st
+from numpy.linalg import norm
+from PIL import Image
 
-def resize_image(image, target_shape=(500, 500)):
-    """Resize image to reduce processing time."""
-    return resize(image, target_shape, anti_aliasing=True)
-
-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))
+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
 
-# 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)
+def process_image(image, k):
+    """Process the uploaded image, compress it using SVD, and return the result."""
+    # Convert PIL Image to NumPy array
+    image_np = np.array(image)
     
-    # Resize the image for faster processing
-    resized_image = resize_image(gray_image)
+    # Compress the image
+    compressed_image = svd_compress(gray_image, k)
     
-    # Select compression rank
-    k = st.slider("Select the rank for compression", min_value=1, max_value=min(resized_image.shape), value=50)
-
-    # Compress the image using Truncated SVD
-    compressed_image = truncated_svd_compress(resized_image, k)
+    # Convert compressed image back to PIL Image for Gradio output
+    compressed_image_pil = Image.fromarray((compressed_image * 255).astype(np.uint8))
+    
+    return compressed_image_pil
 
-    # Normalize the images for display
-    gray_image_display = (resized_image - resized_image.min()) / (resized_image.max() - resized_image.min())
-    compressed_image_display = (compressed_image - compressed_image.min()) / (compressed_image.max() - compressed_image.min())
+# Gradio interface
+gr_interface = gr.Interface(fn=process_image,
+                            inputs=[gr.Image(type="pil"), 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 and adjust the compression rank to see the compressed version.")
 
-    # Display original and compressed images
-    st.image([gray_image_display, compressed_image_display], caption=["Original", "Compressed"], use_column_width=True)
+gr_interface.launch()