wut-web cleaup

notebooks/wut-web.ipynb

@@ -10,15 +10,8 @@
     "#\n",
     "# https://spacecruft.org/spacecruft/satnogs-wut\n",
     "#\n",
-    "# GPLv3+"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "# GPLv3+\n",
+    "\n",
     "import os\n",
     "import random\n",
     "import tempfile\n",
@@ -26,15 +19,8 @@
     "import tensorflow as tf\n",
     "from IPython.display import display, Image\n",
     "from tensorflow.python.keras.models import load_model\n",
-    "from tensorflow.python.keras.preprocessing.image import ImageDataGenerator"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "from tensorflow.python.keras.preprocessing.image import ImageDataGenerator\n",
+    "\n",
     "print(\"wut --- What U Think? SatNOGS Observation AI.\")\n",
     "print(\"\")\n",
     "print(\"wut is an AI that rates SatNOGS Observations good or bad.\")\n",
@@ -47,15 +33,8 @@
     "print(\"\")\n",
     "print(\"Source Code:\")\n",
     "print(\"https://spacecruft.org/spacecruft/satnogs-wut\")\n",
-    "print(\"Alpha stage.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "print(\"Alpha stage.\")\n",
+    "\n",
     "IMG_HEIGHT = 416\n",
     "IMG_WIDTH= 804\n",
     "batch_size = 32\n",
@@ -78,15 +57,8 @@
     "for root, dirs, files in os.walk(sample_dir):\n",
     "  for name in files:\n",
     "    n=n+1\n",
-    "    if random.uniform(0, n) < 1: rfile=os.path.join(root, name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "    if random.uniform(0, n) < 1: rfile=os.path.join(root, name)\n",
+    "\n",
     "shutil.copy(rfile, test_dir + '/unvetted/')"
    ]
   },
@@ -96,15 +68,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model = load_model(model_file)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "model = load_model(model_file)\n",
+    "\n",
     "test_image_generator = ImageDataGenerator(\n",
     "    rescale=1./255\n",
     ")\n",
@@ -112,23 +77,12 @@
     "                                                       directory=test_dir,\n",
     "                                                       target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
     "                                                       shuffle=True,\n",
-    "                                                       class_mode='binary')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "baserfile=os.path.basename(rfile)\n",
-    "print(\"Rating:\", baserfile)\n",
+    "                                                       class_mode='binary')\n",
     "\n",
     "prediction = model.predict(\n",
     "    x=test_data_gen,\n",
     "    verbose=0\n",
     ")\n",
-    "\n",
     "predictions=[]\n",
     "prediction_bool = (prediction >0.8)\n",
     "predictions = prediction_bool.astype(int)\n",
@@ -136,15 +90,8 @@
     "  rating = 'bad'\n",
     "else:\n",
     "  rating = 'good'\n",
-    "print('Observation: %s' % (rating))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "print('Observation: %s' % (rating))\n",
+    "\n",
     "shutil.rmtree(test_dir)"
    ]
   },