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This commit is contained in:
Ali Abdalla 2019-04-18 22:29:23 -07:00
parent aae0d2bf92
commit 07356d682b
2 changed files with 50 additions and 23 deletions
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63
Test Keras.ipynb
View File

@ -9,18 +9,9 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The autoreload extension is already loaded. To reload it, use:\n",
" %reload_ext autoreload\n"
]
}
],
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
@ -32,9 +23,19 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 2,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From C:\\Users\\ALI\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Colocations handled automatically by placer.\n"
]
}
],
"source": [
"model = tf.keras.applications.inception_v3.InceptionV3()"
]
@ -48,7 +49,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@ -64,9 +65,7 @@
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"scrolled": false
},
"metadata": {},
"outputs": [
{
"name": "stdout",
@ -74,7 +73,7 @@
"text": [
"Closing existing server...\n",
"NOTE: Gradio is in beta stage, please report all bugs to: contact.gradio@gmail.com\n",
"Model is running locally at: http://localhost:7861/\n",
"Model is running locally at: http://localhost:7860/\n",
"Unable to create public link for interface, please check internet connection or try restarting python interpreter.\n"
]
},
@ -85,14 +84,14 @@
" <iframe\n",
" width=\"1000\"\n",
" height=\"500\"\n",
" src=\"http://localhost:7861/\"\n",
" src=\"http://localhost:7860/\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x26c65349978>"
"<IPython.lib.display.IFrame at 0x25256b5d7b8>"
]
},
"metadata": {},
@ -101,17 +100,35 @@
{
"data": {
"text/plain": [
"''"
"(<gradio.networking.serve_files_in_background.<locals>.HTTPServer at 0x25256cb1080>,\n",
" 'http://localhost:7860/',\n",
" None)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Error in connection handler\n",
"Traceback (most recent call last):\n",
" File \"C:\\Users\\ALI\\Anaconda3\\lib\\site-packages\\websockets\\server.py\", line 169, in handler\n",
" yield from self.ws_handler(self, path)\n",
" File \"C:\\Users\\ALI\\Desktop\\gradiome\\gradio\\interface.py\", line 139, in communicate\n",
" self.input_interface.rebuild_flagged(msg)\n",
" File \"C:\\Users\\ALI\\Desktop\\gradiome\\gradio\\inputs.py\", line 223, in rebuild_flagged\n",
" sniffer = csv.Sniffer().sniff(f.readline())\n",
" File \"C:\\Users\\ALI\\Anaconda3\\lib\\csv.py\", line 188, in sniff\n",
" raise Error(\"Could not determine delimiter\")\n",
"_csv.Error: Could not determine delimiter\n"
]
}
],
"source": [
"io.launch(inline=True, inbrowser=False, share=True, validate=False)\n",
";"
"io.launch(inline=True, inbrowser=False, share=True, validate=False)"
]
},
{

10
gradio/inputs.py
View File

@ -10,6 +10,7 @@ import numpy as np
from PIL import Image, ImageOps
import datetime
import csv
import pandas as pd
# Where to find the static resources associated with each template.
BASE_INPUT_INTERFACE_TEMPLATE_PATH = 'templates/input/{}.html'
@ -211,9 +212,18 @@ class ImageUpload(AbstractInput):
im = preprocessing_utils.encoding_to_image(inp)
timestamp = datetime.datetime.now()
im.save(f'gradio-flagged/{timestamp.strftime("%Y-%m-%d %H-%M-%S")}.png', 'PNG')
f = open('gradio-flagged/gradio-flagged.csv','a+')
header = ['Gradio flag output: Records filename (timestamp) and associated output, as well confidences and second top two outputs (if they exist)']
columns = ['Filename (timestamp)','Label','Confidence','Label','Confidence','Label','Confidence']
fields = [timestamp.strftime("%Y-%m-%d %H-%M-%S"),msg['data']['output']]
writer = csv.writer(f)
# df = pd.read_csv('gradio-flagged/gradio-flagged.csv')
# if df.empty:
# sniffer = csv.Sniffer().sniff(f.readline())
# if sniffer.has_header(f.read(32)):
# writer.writerow(header)
# writer.writerow(columns)
writer.writerow(fields)
f.close()