From f87a677b19b4b029d8864a5ce71f963f1abe1c73 Mon Sep 17 00:00:00 2001
From: ml server <ml@spacecruft.org>
Date: Thu, 23 Jan 2020 19:03:17 -0700
Subject: [PATCH] wut-web-dev revert to main

---
 notebooks/wut-web-dev.ipynb | 365 +++---------------------------------
 1 file changed, 21 insertions(+), 344 deletions(-)

diff --git a/notebooks/wut-web-dev.ipynb b/notebooks/wut-web-dev.ipynb
index 34a526a..4e2c43a 100644
--- a/notebooks/wut-web-dev.ipynb
+++ b/notebooks/wut-web-dev.ipynb
@@ -2,90 +2,34 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# wut-web-dev --- What U Think? Web App: SatNOGS Observation AI, makes predictions, development version.\n",
+    "# wut-web --- What U Think? Web App: SatNOGS Observation AI, makes predictions.\n",
     "#\n",
     "# https://spacecruft.org/spacecruft/satnogs-wut\n",
     "#\n",
     "# GPLv3+\n",
     "\n",
-    "from __future__ import print_function\n",
     "import os\n",
     "import random\n",
     "import tempfile\n",
     "import shutil\n",
     "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",
-    "import ipywidgets as widgets"
+    "\n",
+    "display(Image(filename='/srv/satnogs/satnogs-wut/pics/spacecruft-bk.png'))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "import ipywidgets as w\n",
-    "from IPython.display import display\n",
-    "import time\n",
-    "from jupyter_ui_poll import (\n",
-    "    ui_events, \n",
-    "    with_ui_events,\n",
-    "    run_ui_poll_loop\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1868d94f31f14ea481c93436c8e78de8",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Output(layout=Layout(border='5px solid lightblue'))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "blueline = widgets.Output(layout={'border': '5px solid lightblue'})\n",
-    "blueline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<H1><B>wut?<B></H1>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "%%HTML\n",
     "<H1><B>wut?<B></H1>"
@@ -93,29 +37,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "wut? --- What U Think? SatNOGS Observation AI.\n",
-      "\n",
-      "wut is an AI that rates SatNOGS Observations good or bad.\n",
-      "The training model was built from DUV transmissions recorded by the\n",
-      "SatNOGS network in December, 2019.\n",
-      "When the page loads, the AI processes a random image and rates it good or bad.\n",
-      "The test pool has 500+ DUV waterfalls the AI hasn't seen before.\n",
-      "The plan is to have models of all SatNOGS modes (65 at present),\n",
-      "and you can enter an arbitrary Observation ID and the AI will return a rating.\n",
-      "\n",
-      "Source Code:\n",
-      "https://spacecruft.org/spacecruft/satnogs-wut\n",
-      "Alpha stage.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(\"wut? --- What U Think? SatNOGS Observation AI.\")\n",
     "print(\"\")\n",
@@ -144,89 +68,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2e6e4c84c4d9409fa9ac6b339902cf7a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Output(layout=Layout(border='5px solid lightblue'))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "blueline = widgets.Output(layout={'border': '5px solid lightblue'})\n",
-    "blueline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'rfile' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-7-69bb47b72cd0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muniform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumwater\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mrfile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mroot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mshutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_dir\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'/unvetted/'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m: name 'rfile' is not defined"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%capture\n",
-    "numwater=0\n",
+    "n=0\n",
     "random.seed();\n",
     "for root, dirs, files in os.walk(sample_dir):\n",
     "  for name in files:\n",
-    "    numwater=numwater+1\n",
-    "    if random.uniform(0, numwater) < 1: rfile=os.path.join(root, name)\n",
+    "    n=n+1\n",
+    "    if random.uniform(0, n) < 1: rfile=os.path.join(root, name)\n",
     "\n",
     "shutil.copy(rfile, test_dir + '/unvetted/')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "OSError",
-     "evalue": "SavedModel file does not exist at: /srv/wut/data/models/wut-DUV-201912.tf/{saved_model.pbtxt|saved_model.pb}",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-8-e4a7d0db24c4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/.local/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/save.py\u001b[0m in \u001b[0;36mload_model\u001b[0;34m(filepath, custom_objects, compile)\u001b[0m\n\u001b[1;32m    147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 149\u001b[0;31m     \u001b[0mloader_impl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse_saved_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    150\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0msaved_model_load\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.local/lib/python3.7/site-packages/tensorflow_core/python/saved_model/loader_impl.py\u001b[0m in \u001b[0;36mparse_saved_model\u001b[0;34m(export_dir)\u001b[0m\n\u001b[1;32m     81\u001b[0m                   (export_dir,\n\u001b[1;32m     82\u001b[0m                    \u001b[0mconstants\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSAVED_MODEL_FILENAME_PBTXT\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 83\u001b[0;31m                    constants.SAVED_MODEL_FILENAME_PB))\n\u001b[0m\u001b[1;32m     84\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mOSError\u001b[0m: SavedModel file does not exist at: /srv/wut/data/models/wut-DUV-201912.tf/{saved_model.pbtxt|saved_model.pb}"
-     ]
-    }
-   ],
-   "source": [
-    "%%capture\n",
-    "model = load_model(model_file)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "%%capture\n",
+    "model = load_model(model_file)\n",
+    "\n",
     "test_image_generator = ImageDataGenerator(\n",
     "    rescale=1./255\n",
     ")\n",
@@ -239,21 +104,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'model' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-10-3a92d4c6bb36>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m prediction = model.predict(\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest_data_gen\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m )\n\u001b[1;32m      5\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%capture\n",
     "prediction = model.predict(\n",
@@ -267,30 +120,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "shutil.rmtree(test_dir)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'rfile' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-12-2dce8d4088c5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mwaterfallpng\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Random waterfall:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwaterfallpng\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEvalFormatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mobsid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"{waterfall[slice(10,17)]}\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwaterfall\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwaterfallpng\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Observation URL: https://network.satnogs.org/observations/{}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobsid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'rfile' is not defined"
-     ]
-    }
-   ],
    "source": [
     "waterfallpng=os.path.basename(rfile)\n",
     "print('Random waterfall:', waterfallpng)\n",
@@ -310,7 +142,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#display(Image(filename=rfile, width=300))"
+    "%%capture\n",
+    "shutil.rmtree(test_dir)"
    ]
   },
   {
@@ -319,163 +152,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "blueline = widgets.Output(layout={'border': '5px solid lightblue'})\n",
-    "blueline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import IPython.display\n",
-    "from IPython.display import Audio\n",
-    "framerate = 44100\n",
-    "t = np.linspace(0,5,framerate*5)\n",
-    "data = np.sin(2*np.pi*220*t) + np.sin(2*np.pi*224*t)\n",
-    "IPython.display.Audio(data,rate=framerate)\n",
-    "\n",
-    "audiofile=('/srv/satnogs/download/1456893/satnogs_1456893_2019-12-30T10-35-46.ogg')\n",
-    "#Audio(filename=audiofile)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#obs_file='/srv/satnogs/download/1456893/1456893.json'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from IPython.display import JSON\n",
-    "obs_file='/srv/satnogs/download/1456893/1456893.json'\n",
-    "obs_json=JSON(filename=obs_file, expanded=False)\n",
-    "foo=JSON(filename=obs_file)\n",
-    "#display_pretty(foo, raw=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from IPython.display import HTML\n",
-    "obs_html = HTML(obs_file)\n",
-    "display(obs_html)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from IPython.utils import text\n",
-    "waterfallpng=os.path.basename(rfile)\n",
-    "print('Waterfall:', waterfallpng)\n",
-    "text.LSString(waterfallpng)\n",
-    "#text.SList(waterfallpng)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print('Waterfall:', waterfallpng)\n",
-    "f=text.EvalFormatter()\n",
-    "obsid=(f.format(\"{waterfall[slice(10,17)]}\", waterfall=waterfallpng))\n",
-    "print('Observation ID:', obsid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(text.marquee('wut?',40,'*'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "blueline = widgets.Output(layout={'border': '5px solid lightblue'})\n",
-    "blueline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Cute SSTV: 1456893\n",
-    "# December, 2019 range: 1292461 \n",
-    "obsmin=1292461\n",
-    "obsmax=1470525\n",
-    "print(\"Minimum Observation ID: \", obsmin)\n",
-    "print(\"Maximum Observation ID: \", obsmax)\n",
-    "\n",
-    "print(\"Enter a value between the minimum and maximum Observation ID:\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "obsoutputText = widgets.Text()\n",
-    "obsoutputText"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "obsinputText = widgets.Text()\n",
-    "\n",
-    "def makeUpperCase(sender):\n",
-    "    obsoutputText.value = obsinputText.value.upper()\n",
-    "    print('dat:', obsinputText.value.upper)\n",
-    "\n",
-    "obsinputText.on_submit(makeUpperCase)\n",
-    "obsinputText"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "blueline = widgets.Output(layout={'border': '5px solid lightblue'})\n",
-    "blueline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "blueline.clear_output()"
+    "display(Image(filename=rfile, width=300))"
    ]
   }
  ],