wut-web-dev revert to main
ml server
parent
591a02952e
commit
f87a677b19
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@ -2,90 +2,34 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# wut-web-dev --- What U Think? Web App: SatNOGS Observation AI, makes predictions, development version.\n",
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"# wut-web --- What U Think? Web App: SatNOGS Observation AI, makes predictions.\n",
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"#\n",
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"# https://spacecruft.org/spacecruft/satnogs-wut\n",
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"#\n",
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"# GPLv3+\n",
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"\n",
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"from __future__ import print_function\n",
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"import os\n",
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"import random\n",
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"import tempfile\n",
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"import shutil\n",
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"import tensorflow as tf\n",
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"import numpy as np\n",
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"from IPython.display import display, Image\n",
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"from IPython.utils import text\n",
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"from tensorflow.python.keras.models import load_model\n",
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"from tensorflow.python.keras.preprocessing.image import ImageDataGenerator\n",
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"from ipywidgets import interact, interactive, fixed, interact_manual\n",
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"import ipywidgets as widgets"
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"\n",
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"display(Image(filename='/srv/satnogs/satnogs-wut/pics/spacecruft-bk.png'))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import ipywidgets as w\n",
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"from IPython.display import display\n",
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"import time\n",
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"from jupyter_ui_poll import (\n",
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" ui_events, \n",
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" with_ui_events,\n",
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" run_ui_poll_loop\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "1868d94f31f14ea481c93436c8e78de8",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Output(layout=Layout(border='5px solid lightblue'))"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"blueline = widgets.Output(layout={'border': '5px solid lightblue'})\n",
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"blueline"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<H1><B>wut?<B></H1>\n"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"%%HTML\n",
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"<H1><B>wut?<B></H1>"
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@ -93,29 +37,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"wut? --- What U Think? SatNOGS Observation AI.\n",
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"\n",
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"wut is an AI that rates SatNOGS Observations good or bad.\n",
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"The training model was built from DUV transmissions recorded by the\n",
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"SatNOGS network in December, 2019.\n",
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"When the page loads, the AI processes a random image and rates it good or bad.\n",
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"The test pool has 500+ DUV waterfalls the AI hasn't seen before.\n",
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"The plan is to have models of all SatNOGS modes (65 at present),\n",
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"and you can enter an arbitrary Observation ID and the AI will return a rating.\n",
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"\n",
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"Source Code:\n",
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"https://spacecruft.org/spacecruft/satnogs-wut\n",
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"Alpha stage.\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"print(\"wut? --- What U Think? SatNOGS Observation AI.\")\n",
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"print(\"\")\n",
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@ -144,89 +68,30 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "2e6e4c84c4d9409fa9ac6b339902cf7a",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Output(layout=Layout(border='5px solid lightblue'))"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"blueline = widgets.Output(layout={'border': '5px solid lightblue'})\n",
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"blueline"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'rfile' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
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"\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",
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"\u001b[0;31mNameError\u001b[0m: name 'rfile' is not defined"
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]
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}
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],
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"outputs": [],
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"source": [
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"%%capture\n",
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"numwater=0\n",
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"n=0\n",
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"random.seed();\n",
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"for root, dirs, files in os.walk(sample_dir):\n",
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" for name in files:\n",
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" numwater=numwater+1\n",
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" if random.uniform(0, numwater) < 1: rfile=os.path.join(root, name)\n",
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" n=n+1\n",
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" if random.uniform(0, n) < 1: rfile=os.path.join(root, name)\n",
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"\n",
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"shutil.copy(rfile, test_dir + '/unvetted/')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"ename": "OSError",
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"evalue": "SavedModel file does not exist at: /srv/wut/data/models/wut-DUV-201912.tf/{saved_model.pbtxt|saved_model.pb}",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mOSError\u001b[0m Traceback (most recent call last)",
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"\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",
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"\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",
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"\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",
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"\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}"
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]
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}
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],
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"source": [
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"%%capture\n",
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"model = load_model(model_file)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%%capture\n",
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"model = load_model(model_file)\n",
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"\n",
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"test_image_generator = ImageDataGenerator(\n",
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" rescale=1./255\n",
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")\n",
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@ -239,21 +104,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'model' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
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"\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",
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"\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
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]
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}
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],
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"outputs": [],
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"source": [
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"%%capture\n",
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"prediction = model.predict(\n",
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"shutil.rmtree(test_dir)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'rfile' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
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"\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))"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
|
|
|||
Loading…
Reference in New Issue