diff --git a/notebooks/wut-web-dev.ipynb b/notebooks/wut-web-dev.ipynb
index 7e83ff0..6f9f499 100644
--- a/notebooks/wut-web-dev.ipynb
+++ b/notebooks/wut-web-dev.ipynb
@@ -20,6 +20,7 @@
     "import tensorflow as tf\n",
     "import numpy as np\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",
     "from ipywidgets import interact, interactive, fixed, interact_manual\n",
@@ -82,6 +83,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "%%capture\n",
     "n=0\n",
     "random.seed();\n",
     "for root, dirs, files in os.walk(sample_dir):\n",
@@ -98,8 +100,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model = load_model(model_file)\n",
-    "\n",
+    "%%capture\n",
+    "model = load_model(model_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
     "test_image_generator = ImageDataGenerator(\n",
     "    rescale=1./255\n",
     ")\n",
@@ -107,24 +118,52 @@
     "                                                       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",
-    "if prediction_bool[0] == False:\n",
-    "  rating = 'bad'\n",
-    "else:\n",
-    "  rating = 'good'\n",
-    "print('Observation: %s' % (rating))\n",
-    "\n",
+    "predictions = prediction_bool.astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "shutil.rmtree(test_dir)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "waterfallpng=os.path.basename(rfile)\n",
+    "print('Random waterfall:', waterfallpng)\n",
+    "f=text.EvalFormatter()\n",
+    "obsid=(f.format(\"{waterfall[slice(10,17)]}\", waterfall=waterfallpng))\n",
+    "print('Observation URL: https://network.satnogs.org/observations/{}'.format(obsid))\n",
+    "if prediction_bool[0] == False:\n",
+    "  rating = 'BAD'\n",
+    "else:\n",
+    "  rating = 'GOOD'\n",
+    "print('AI Observation rating: %s' % (rating))"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -401,6 +440,25 @@
     "print(text.marquee('wut?',40,'*'))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = input()\n",
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print('mkay')"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,