Create app.py

app.py

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+import numpy as np
+from PIL import Image
+import gradio as gr
+import tempfile
+import os
+
+def initialize_centroids(X, k):
+    np.random.seed(42)
+    indices = np.random.choice(X.shape[0], k, replace=False)
+    return X[indices]
+
+def compute_centroids(X, labels, k):
+    centroids = []
+    for i in range(k):
+        cluster_points = X[labels == i]
+        if len(cluster_points) > 0:
+            centroids.append(cluster_points.mean(axis=0))
+        else:
+            centroids.append(X[np.random.choice(len(X))])
+    return np.array(centroids)
+
+def assign_clusters(X, centroids):
+    distances = np.linalg.norm(X[:, np.newaxis] - centroids, axis=2)
+    return np.argmin(distances, axis=1)
+
+def kmeans(X, k, max_iters=100):
+    centroids = initialize_centroids(X, k)
+    for _ in range(max_iters):
+        labels = assign_clusters(X, centroids)
+        new_centroids = compute_centroids(X, labels, k)
+        if np.allclose(centroids, new_centroids):
+            break
+        centroids = new_centroids
+    return centroids, labels
+
+def compress_image(img, clusters=16):
+    if clusters < 2:
+        return None, "Minimum 3 clusters required."
+
+    # Resize for speed & consistency
+    max_dim = 512
+    w, h = img.size
+    scale = max_dim / max(w, h)
+    img = img.resize((int(w * scale), int(h * scale)))
+
+    img_np = np.array(img)
+    shape = img_np.shape
+    img_flat = img_np.reshape(-1, 3).astype(float)
+
+    centroids, labels = kmeans(img_flat, clusters, max_iters=30)
+    compressed_flat = centroids[labels].astype(np.uint8)
+    compressed_img = compressed_flat.reshape(shape)
+
+    # Save original and compressed to compare file sizes (use PNG to avoid JPEG inconsistency)
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as orig:
+        Image.fromarray(img_np).save(orig.name, format='PNG')
+        orig_size = os.path.getsize(orig.name)
+
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as comp:
+        Image.fromarray(compressed_img).save(comp.name, format='PNG')
+        comp_size = os.path.getsize(comp.name)
+
+    os.remove(orig.name)
+    os.remove(comp.name)
+
+    percent = 100 * (orig_size - comp_size) / orig_size
+    if percent < 0:
+        status = f"⚠️ Larger by {abs(percent):.2f}% (due to more color detail)"
+    else:
+        status = f"✅ Compressed by {percent:.2f}%"
+
+    return Image.fromarray(compressed_img), status
+
+iface = gr.Interface(
+    fn=compress_image,
+    inputs=[
+        gr.Image(type="pil", label="Upload Image"),
+        gr.Slider(2, 64, value=16, step=1, label="Number of Clusters")
+    ],
+    outputs=[
+        gr.Image(type="pil", label="Compressed Image"),
+        gr.Label(label="Compression Info")
+    ],
+    title="Fixed K-Means Image Compressor",
+    description="Uses KMeans clustering on color pixels. PNG is used to give accurate compression size comparison."
+)
+
+if __name__ == "__main__":
+    iface.launch()