NEW CODE

app.py

@@ -3,120 +3,67 @@ import numpy as np
 from sklearn.cluster import KMeans
 from PIL import Image
 import os
-import traceback
+import tempfile
 from io import BytesIO
 
-def compress_kmeans(image: np.ndarray, k: int) -> np.ndarray:
-    """Compress an RGB image to k colours using K‑Means."""
+def compress_image(image, k):
     pixels = image.reshape(-1, 3)
-    model = KMeans(n_clusters=k, n_init="auto", random_state=42)
+    model = KMeans(n_clusters=k, n_init=10, random_state=42)
     model.fit(pixels)
     centres = model.cluster_centers_.astype(np.uint8)
     labels = model.predict(pixels)
-    return centres[labels].reshape(image.shape)
+    compressed = centres[labels].reshape(image.shape)
+    return compressed
 
-def compress_and_report(filepath, k):
-    """Gradio callback: compress, generate report, return outputs."""
+def process(filepath, k):
     if filepath is None:
-        return None, "<div style='padding: 20px; color: #64748b;'>Upload an image to view the report.</div>"
+        return None, "Please upload an image.", None
 
-    try:
-        # Extract base filename
-        base = os.path.basename(str(filepath))
-        orig_filename = os.path.splitext(base)[0]
+    # Load original
+    orig = np.array(Image.open(filepath).convert('RGB'))
 
-        # Load original
-        orig_pil = Image.open(filepath).convert("RGB")
-        orig_np = np.array(orig_pil)
+    # Compress
+    comp = compress_image(orig, k)
 
-        # Compress
-        comp_np = compress_kmeans(orig_np, k)
-        comp_pil = Image.fromarray(comp_np)
+    def get_size(img):
+        with BytesIO() as buf:
+            Image.fromarray(img).save(buf, format='PNG')
+            return len(buf.getvalue())
 
-        # Compute file sizes (PNG)
-        buf_orig = BytesIO()
-        orig_pil.save(buf_orig, format="PNG")
-        orig_size = len(buf_orig.getvalue())
+    orig_size = get_size(orig)
+    comp_size = get_size(comp)
+    ratio = orig_size / comp_size
+    saved = (1 - comp_size/orig_size) * 100
 
-        buf_comp = BytesIO()
-        comp_pil.save(buf_comp, format="PNG")
-        comp_size = len(buf_comp.getvalue())
+    # PSNR
+    mse = np.mean((orig.astype(float) - comp.astype(float)) ** 2)
+    psnr = 20 * np.log10(255.0 / np.sqrt(mse)) if mse > 0 else float('inf')
 
-        # Metrics
-        mse = np.mean((orig_np.astype(np.float64) - comp_np.astype(np.float64)) ** 2)
-        psnr = float("inf") if mse == 0 else 20.0 * np.log10(255.0 / np.sqrt(mse))
-        ratio = orig_size / comp_size
-        saved_pct = (1.0 - (comp_size / orig_size)) * 100.0
-        orig_kb = orig_size / 1024
-        comp_kb = comp_size / 1024
+    stats = (f"**Original:** {orig_size/1024:.1f} KB  \n"
+             f"**Compressed:** {comp_size/1024:.1f} KB  \n"
+             f"**Compression ratio:** {ratio:.1f}x  \n"
+             f"**Space saved:** {saved:.1f}%  \n"
+             f"**PSNR:** {psnr:.1f} dB")
 
-        # Quality badge
-        if psnr > 35:
-            badge = '<span style="color:#16a34a;">Excellent</span>'
-        elif psnr > 28:
-            badge = '<span style="color:#eab308;">Good</span>'
-        else:
-            badge = '<span style="color:#dc2626;">Acceptable</span>'
+    temp = tempfile.mkdtemp()
+    out_path = os.path.join(temp, 'compressed.png')
+    Image.fromarray(comp).save(out_path)
 
-        # HTML Report
-        report_html = f"""
-        <div style="background: #f8f9fa; padding: 16px; border-radius: 8px; border-left: 4px solid #2563eb; font-family: sans-serif;">
-            <h4 style="margin: 0 0 12px 0; color: #1e293b;">Compression Report</h4>
-            <table style="width: 100%; border-collapse: collapse; font-size: 14px;">
-                <tr style="border-bottom: 1px solid #e2e8f0;">
-                    <td style="padding: 6px 0; font-weight: 600; color: #475569;">PSNR</td>
-                    <td style="padding: 6px 0; color: #0f172a;">{psnr:.2f} dB</td>
-                    <td style="padding: 6px 0;">{badge}</td>
-                </tr>
-                <tr style="border-bottom: 1px solid #e2e8f0;">
-                    <td style="padding: 6px 0; font-weight: 600; color: #475569;">MSE</td>
-                    <td style="padding: 6px 0; color: #0f172a;" colspan="2">{mse:.2f}</td>
-                </tr>
-                <tr style="border-bottom: 1px solid #e2e8f0;">
-                    <td style="padding: 6px 0; font-weight: 600; color: #475569;">Compression Ratio</td>
-                    <td style="padding: 6px 0; color: #0f172a;" colspan="2">{ratio:.2f}x</td>
-                </tr>
-                <tr style="border-bottom: 1px solid #e2e8f0;">
-                    <td style="padding: 6px 0; font-weight: 600; color: #475569;">Original Size</td>
-                    <td style="padding: 6px 0; color: #0f172a;" colspan="2">{orig_kb:.2f} KB</td>
-                </tr>
-                <tr>
-                    <td style="padding: 6px 0; font-weight: 600; color: #475569;">Compressed Size</td>
-                    <td style="padding: 6px 0; color: #0f172a;">{comp_kb:.2f} KB</td>
-                    <td style="padding: 6px 0; color: #2563eb; font-weight: 600;">↓ {saved_pct:.1f}%</td>
-                </tr>
-            </table>
-        </div>
-        """
+    return comp, stats, out_path
 
-        download_name = f"{orig_filename}_compressed_{int(comp_kb)}KB.png"
-        return gr.update(value=comp_np, download_name=download_name), report_html
-
-    except Exception as e:
-        error_trace = traceback.format_exc()
-        error_html = f"""
-        <div style="background: #fee2e2; padding: 16px; border-radius: 8px; border-left: 4px solid #dc2626; font-family: monospace; font-size: 13px;">
-            <strong style="color: #991b1b;">Runtime Error</strong>
-            <pre style="white-space: pre-wrap; word-wrap: break-word; margin-top: 8px;">{error_trace}</pre>
-        </div>
-        """
-        return None, error_html
-
-# Gradio Interface
 iface = gr.Interface(
-    fn=compress_and_report,
+    fn=process,
     inputs=[
-        gr.Image(type="filepath", label="Upload Image"),
-        gr.Slider(minimum=2, maximum=64, step=2, value=16, label="Number of Colors (k)"),
+        gr.Image(type='filepath', label='Upload an Image'),
+        gr.Slider(minimum=2, maximum=64, step=2, value=16, label='Number of colours (k)')
     ],
     outputs=[
-        gr.Image(type="numpy", label="Compressed Image"),
-        gr.Markdown(label="Metrics Report"),
+        gr.Image(label='Compressed Image'),
+        gr.Markdown(label='Compression Statistics'),
+        gr.File(label='Download Compressed Image')
     ],
-    title="Image Compression using K-Means",
-    description="Upload an image and adjust number of colors (k) to see lossy compression in action.",
+    title='Image Compression with K‑Means',
+    description='Reduce the number of colours in an image using K-Means clustering.'
 )
 
-if __name__ == "__main__":
-    # Set share=True to get a public link (valid for 72 hours)
-    iface.launch(share=True)
+iface.launch()