diff --git a/notebooks/wut-web.ipynb b/notebooks/wut-web.ipynb
index 9222d93..773b629 100644
--- a/notebooks/wut-web.ipynb
+++ b/notebooks/wut-web.ipynb
@@ -18,6 +18,7 @@
     "import shutil\n",
     "import tensorflow as tf\n",
     "from IPython.display import display, Image\n",
+    "from IPython.utils import text\n",
     "from tensorflow.python.keras.models import load_model\n",
     "from tensorflow.python.keras.preprocessing.image import ImageDataGenerator\n",
     "\n",
@@ -53,6 +54,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "%%capture\n",
     "n=0\n",
     "random.seed();\n",
     "for root, dirs, files in os.walk(sample_dir):\n",
@@ -69,6 +71,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "%%capture\n",
     "model = load_model(model_file)\n",
     "\n",
     "test_image_generator = ImageDataGenerator(\n",
@@ -78,21 +81,50 @@
     "                                                       directory=test_dir,\n",
     "                                                       target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
     "                                                       shuffle=True,\n",
-    "                                                       class_mode='binary')\n",
-    "\n",
+    "                                                       class_mode='binary')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
     "prediction = model.predict(\n",
     "    x=test_data_gen,\n",
     "    verbose=0\n",
     ")\n",
     "predictions=[]\n",
     "prediction_bool = (prediction >0.8)\n",
-    "predictions = prediction_bool.astype(int)\n",
+    "predictions = prediction_bool.astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "waterfallpng=os.path.basename(rfile)\n",
+    "print(waterfallpng)\n",
+    "f=text.EvalFormatter()\n",
+    "obsid=(f.format(\"{waterfall[slice(10,17)]}\", waterfall=waterfallpng))\n",
+    "print('https://network.satnogs.org/observations/{}'.format(obsid))\n",
     "if prediction_bool[0] == False:\n",
     "  rating = 'bad'\n",
     "else:\n",
     "  rating = 'good'\n",
-    "print('Observation: %s' % (rating))\n",
-    "\n",
+    "print('Observation: %s' % (rating))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
     "shutil.rmtree(test_dir)"
    ]
   },