mirror of
https://github.com/gradio-app/gradio.git
synced 2025-03-25 12:10:31 +08:00
added screenshots
This commit is contained in:
Abubakar Abid
parent
44e92f6cb1
commit
6df69880e8
72
.idea/workspace.xml
generated
72
.idea/workspace.xml
generated
@ -2,13 +2,17 @@
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</list>
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@ -102,7 +106,7 @@
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@ -111,18 +115,18 @@
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@ -187,16 +191,16 @@
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@ -210,7 +214,7 @@
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@ -306,11 +310,11 @@
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<option value="$PROJECT_DIR$/gradio/inputs.py" />
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<option value="$PROJECT_DIR$/../testing/test_gradio.py" />
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<option value="$PROJECT_DIR$/gradio/js/all-io.js" />
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<option value="$PROJECT_DIR$/setup.py" />
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<option value="$PROJECT_DIR$/gradio/networking.py" />
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<option value="$PROJECT_DIR$/gradio/__init__.py" />
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<option value="$PROJECT_DIR$/gradio/preprocessing_utils.py" />
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<option value="$PROJECT_DIR$/gradio/templates/sketchpad_input.html" />
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<option value="$PROJECT_DIR$/setup.py" />
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@ -321,10 +325,10 @@
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@ -402,10 +406,10 @@
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<servers />
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<layout>
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<window_info active="true" content_ui="combo" id="Project" order="0" visible="true" weight="0.25773194" />
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<window_info id="Structure" order="1" side_tool="true" weight="0.25" />
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<window_info id="Favorites" order="2" side_tool="true" />
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<window_info anchor="bottom" id="Message" order="0" />
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@ -547,13 +551,6 @@
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</entry>
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<entry file="file://$PROJECT_DIR$/gradio/js/all-io.js">
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@ -613,7 +610,7 @@
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@ -623,7 +620,7 @@
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<entry file="file://$PROJECT_DIR$/gradio/templates/class_output.html">
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<folding>
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<element signature="n#style#0;n#div#0;n#div#0;n#!!top" expanded="true" />
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@ -635,8 +632,15 @@
|
||||
</entry>
|
||||
<entry file="file://$PROJECT_DIR$/gradio/templates/sketchpad_input.html">
|
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<provider selected="true" editor-type-id="text-editor">
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<entry file="file://$PROJECT_DIR$/setup.py">
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<provider selected="true" editor-type-id="text-editor">
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|
||||
</state>
|
||||
</provider>
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||||
</entry>
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||||
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||||
@ -9,6 +9,7 @@ from gradio import outputs
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||||
from gradio import networking
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||||
import os
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import shutil
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import tempfile
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nest_asyncio.apply()
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||||
@ -16,17 +17,18 @@ LOCALHOST_IP = '127.0.0.1'
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INITIAL_WEBSOCKET_PORT = 9200
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TRY_NUM_PORTS = 100
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||||
JS_FILE = pkg_resources.resource_filename('gradio', 'js/all-io.js')
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BASE_TEMPLATE = pkg_resources.resource_filename('gradio', 'templates/all_io.html')
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JS_PATH_LIB = pkg_resources.resource_filename('gradio', 'js/')
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JS_PATH_LOCAL = os.path.realpath('js/')
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CSS_PATH_LIB = pkg_resources.resource_filename('gradio', 'css/')
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CSS_PATH_LOCAL = os.path.realpath('css/')
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JS_PATH_TEMP = 'js/'
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||||
CSS_PATH_TEMP = 'css/'
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||||
TEMPLATE_TEMP = 'interface.html'
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||||
BASE_JS_FILE = 'js/all-io.js'
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||||
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||||
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||||
class Interface():
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||||
"""
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||||
"""
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||||
build_template_path = 'interface.html'
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||||
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||||
def __init__(self, input, output, model, model_type, preprocessing_fn=None, postprocessing_fn=None):
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||||
"""
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||||
@ -39,7 +41,7 @@ class Interface():
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self.model_type = model_type
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self.model_obj = model
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def _build_template(self):
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def _build_template(self, temp_dir):
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input_template_path = pkg_resources.resource_filename(
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||||
'gradio', self.input_interface._get_template_path())
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output_template_path = pkg_resources.resource_filename(
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||||
@ -49,8 +51,7 @@ class Interface():
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input_soup = BeautifulSoup(input_page.read(), features="html.parser")
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output_soup = BeautifulSoup(output_page.read(), features="html.parser")
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all_io_url = pkg_resources.resource_filename('gradio', 'templates/all_io.html')
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all_io_page = open(all_io_url)
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all_io_page = open(BASE_TEMPLATE)
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all_io_soup = BeautifulSoup(all_io_page.read(), features="html.parser")
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input_tag = all_io_soup.find("div", {"id": "input"})
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output_tag = all_io_soup.find("div", {"id": "output"})
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@ -58,12 +59,12 @@ class Interface():
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input_tag.replace_with(input_soup)
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output_tag.replace_with(output_soup)
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f = open(self.build_template_path, "w")
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f = open(os.path.join(temp_dir, TEMPLATE_TEMP), "w")
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f.write(str(all_io_soup.prettify))
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self._copy_files(JS_PATH_LIB, JS_PATH_LOCAL)
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self._copy_files(CSS_PATH_LIB, CSS_PATH_LOCAL)
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return self.build_template_path
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self._copy_files(JS_PATH_LIB, os.path.join(temp_dir, JS_PATH_TEMP))
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self._copy_files(CSS_PATH_LIB, os.path.join(temp_dir, CSS_PATH_TEMP))
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return
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||||
def _copy_files(self, src_dir, dest_dir):
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if not os.path.exists(dest_dir):
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@ -74,22 +75,21 @@ class Interface():
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if os.path.isfile(full_file_name):
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shutil.copy(full_file_name, dest_dir)
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def _set_socket_url_in_js(self, socket_url):
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with open(JS_FILE) as fin:
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def _set_socket_url_in_js(self, temp_dir, socket_url):
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with open(os.path.join(temp_dir, BASE_JS_FILE)) as fin:
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lines = fin.readlines()
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lines[0] = 'var NGROK_URL = "{}"\n'.format(socket_url.replace('http', 'ws'))
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||||
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||||
with open(JS_FILE, 'w') as fout:
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with open(os.path.join(temp_dir, BASE_JS_FILE), 'w') as fout:
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for line in lines:
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fout.write(line)
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def _set_socket_port_in_js(self, socket_port):
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with open(JS_FILE) as fin:
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def _set_socket_port_in_js(self, temp_dir, socket_port):
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||||
with open(os.path.join(temp_dir, BASE_JS_FILE)) as fin:
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lines = fin.readlines()
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lines[1] = 'var SOCKET_PORT = {}\n'.format(socket_port)
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||||
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with open(JS_FILE, 'w') as fout:
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with open(os.path.join(temp_dir, BASE_JS_FILE), 'w') as fout:
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||||
for line in lines:
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||||
fout.write(line)
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||||
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||||
@ -124,9 +124,11 @@ class Interface():
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Standard method shared by interfaces that launches a websocket at a specified IP address.
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||||
"""
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||||
networking.kill_processes([4040, 4041])
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server_port = networking.start_simple_server()
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output_directory = tempfile.mkdtemp()
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||||
server_port = networking.start_simple_server(output_directory)
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||||
path_to_server = 'http://localhost:{}/'.format(server_port)
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||||
path_to_template = self._build_template()
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self._build_template(output_directory)
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||||
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||||
ports_in_use = networking.get_ports_in_use()
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||||
for i in range(TRY_NUM_PORTS):
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@ -137,19 +139,19 @@ class Interface():
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||||
INITIAL_WEBSOCKET_PORT, INITIAL_WEBSOCKET_PORT + TRY_NUM_PORTS))
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||||
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||||
start_server = websockets.serve(self.communicate, LOCALHOST_IP, INITIAL_WEBSOCKET_PORT + i)
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||||
self._set_socket_port_in_js(INITIAL_WEBSOCKET_PORT + i)
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||||
self._set_socket_port_in_js(output_directory, INITIAL_WEBSOCKET_PORT + i)
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||||
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||||
if share_link:
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||||
site_ngrok_url = networking.setup_ngrok(server_port)
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socket_ngrok_url = networking.setup_ngrok(INITIAL_WEBSOCKET_PORT, api_url=networking.NGROK_TUNNELS_API_URL2)
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||||
self._set_socket_url_in_js(socket_ngrok_url)
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||||
self._set_socket_url_in_js(output_directory, socket_ngrok_url)
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||||
print("NOTE: Gradio is in beta stage, please report all bugs to: a12d@stanford.edu")
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||||
print("Model available locally at: {}".format(path_to_server + path_to_template))
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||||
print("Model available publicly for 8 hours at: {}".format(site_ngrok_url + '/' + path_to_template))
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||||
print("Model available locally at: {}".format(path_to_server + TEMPLATE_TEMP))
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||||
print("Model available publicly for 8 hours at: {}".format(site_ngrok_url + '/' + TEMPLATE_TEMP))
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asyncio.get_event_loop().run_until_complete(start_server)
|
||||
try:
|
||||
asyncio.get_event_loop().run_forever()
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||||
except RuntimeError: # Runtime errors are thrown in jupyter notebooks because of async.
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||||
pass
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||||
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||||
webbrowser.open(path_to_server + path_to_template)
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||||
webbrowser.open(path_to_server + TEMPLATE_TEMP)
|
||||
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||||
@ -1,4 +1,4 @@
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||||
var NGROK_URL = "ws://0076f141.ngrok.io"
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var NGROK_URL = "ws://0f9bffb5.ngrok.io"
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||||
var SOCKET_PORT = 9200
|
||||
|
||||
try {
|
||||
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||||
@ -28,7 +28,7 @@ def get_ports_in_use():
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return ports_in_use
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||||
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||||
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||||
def start_simple_server():
|
||||
def start_simple_server(directory_to_serve=None):
|
||||
# TODO(abidlabs): increment port number until free port is found
|
||||
ports_in_use = get_ports_in_use()
|
||||
for i in range(TRY_NUM_PORTS):
|
||||
@ -37,7 +37,11 @@ def start_simple_server():
|
||||
else:
|
||||
raise OSError("All ports from {} to {} are in use. Please close a port.".format(
|
||||
INITIAL_PORT_VALUE, INITIAL_PORT_VALUE + TRY_NUM_PORTS))
|
||||
subprocess.Popen(['python', '-m', 'http.server', str(INITIAL_PORT_VALUE + i)])
|
||||
if directory_to_serve is None:
|
||||
subprocess.Popen(['python', '-m', 'http.server', str(INITIAL_PORT_VALUE + i)])
|
||||
else:
|
||||
cmd = ' '.join(['python', '-m', 'http.server', '-d', directory_to_serve, str(INITIAL_PORT_VALUE + i)])
|
||||
subprocess.Popen(cmd, shell=True) # Doesn't seem to work if list is passed for some reason.
|
||||
return INITIAL_PORT_VALUE + i
|
||||
|
||||
|
||||
|
||||
@ -17,17 +17,17 @@ def resize_and_crop(img, size, crop_type='top'):
|
||||
ValueError: if an invalid `crop_type` is provided.
|
||||
"""
|
||||
# Get current and desired ratio for the images
|
||||
img_ratio = img.size[0] / float(img.size[1])
|
||||
ratio = size[0] / float(size[1])
|
||||
# The image is scaled/cropped vertically or horizontally depending on the ratio
|
||||
img_ratio = img.size[0] // float(img.size[1])
|
||||
ratio = size[0] // float(size[1])
|
||||
# The image is scaled//cropped vertically or horizontally depending on the ratio
|
||||
if ratio > img_ratio:
|
||||
img = img.resize((size[0], size[0] * img.size[1] / img.size[0]),
|
||||
img = img.resize((size[0], size[0] * img.size[1] // img.size[0]),
|
||||
Image.ANTIALIAS)
|
||||
# Crop in the top, middle or bottom
|
||||
if crop_type == 'top':
|
||||
box = (0, 0, img.size[0], size[1])
|
||||
elif crop_type == 'middle':
|
||||
box = (0, (img.size[1] - size[1]) / 2, img.size[0], (img.size[1] + size[1]) / 2)
|
||||
box = (0, (img.size[1] - size[1]) // 2, img.size[0], (img.size[1] + size[1]) // 2)
|
||||
elif crop_type == 'bottom':
|
||||
box = (0, img.size[1] - size[1], img.size[0], img.size[1])
|
||||
else:
|
||||
@ -40,7 +40,7 @@ def resize_and_crop(img, size, crop_type='top'):
|
||||
if crop_type == 'top':
|
||||
box = (0, 0, size[0], img.size[1])
|
||||
elif crop_type == 'middle':
|
||||
box = ((img.size[0] - size[0]) / 2, 0, (img.size[0] + size[0]) / 2, img.size[1])
|
||||
box = ((img.size[0] - size[0]) // 2, 0, (img.size[0] + size[0]) // 2, img.size[1])
|
||||
elif crop_type == 'bottom':
|
||||
box = (img.size[0] - size[0], 0, img.size[0], img.size[1])
|
||||
else:
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
<div class="col-6">
|
||||
<h5>Sketch Pad Input: (Use your cursor to draw)</h5>
|
||||
<h5>Sketch Pad Input:</h5>
|
||||
<canvas id="canvas" width="400" height="400"></canvas><br>
|
||||
<div class="btn-group" role="group" aria-label="Basic example">
|
||||
<button type="button" class="btn btn-primary" id="submit-button">Recognize</button>
|
||||
|
||||
Binary file not shown.
@ -0,0 +1,95 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Using TensorFlow backend.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import sklearn\n",
|
||||
"import gradio\n",
|
||||
"from keras.models import load_model\n",
|
||||
"\n",
|
||||
"%load_ext autoreload\n",
|
||||
"%autoreload 2"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Load an Facial Emotion Detector Model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model = load_model('../.models/emotion.h5')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def post_p(prediction): \n",
|
||||
" emotional_dict = {0: \"Angry\", 1: \"Disgusted\", 2: \"Fearful\", 3: \"Happy\", 4: \"Neutral\", 5: \"Sad\", 6: \"Surprised\"}\n",
|
||||
" return emotional_dict[prediction.squeeze().argmax()]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"NOTE: Gradio is in beta stage, please report all bugs to: a12d@stanford.edu\n",
|
||||
"Model available locally at: http://localhost:7865/interface.html\n",
|
||||
"Model available publicly for 8 hours at: http://f7ae4c9b.ngrok.io/interface.html\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"iface = gradio.Interface(input='webcam', output='class', model=model, model_type='keras',postprocessing_fn=post_p)\n",
|
||||
"iface.launch()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6 (tensorflow)",
|
||||
"language": "python",
|
||||
"name": "tensorflow"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.7"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@ -0,0 +1,82 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import sklearn\n",
|
||||
"import gradio\n",
|
||||
"from keras.models import load_model\n",
|
||||
"import sys\n",
|
||||
"import warnings\n",
|
||||
"\n",
|
||||
"if not sys.warnoptions:\n",
|
||||
" warnings.simplefilter(\"ignore\")\n",
|
||||
"\n",
|
||||
"%load_ext autoreload\n",
|
||||
"%autoreload 2"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Load MNIST Model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model = load_model('../.models/mnist-cnn.h5')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Run Interface"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"iface = gradio.Interface(input='sketchpad', output='class', model=model, model_type='keras')\n",
|
||||
"iface.launch(share_link=False)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6 (tensorflow)",
|
||||
"language": "python",
|
||||
"name": "tensorflow"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.7"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@ -0,0 +1,253 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Using TensorFlow backend.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import sklearn\n",
|
||||
"import gradio\n",
|
||||
"from keras.models import load_model\n",
|
||||
"from keras.datasets import imdb\n",
|
||||
"from keras.preprocessing import sequence\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"%load_ext autoreload\n",
|
||||
"%autoreload 2"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model = load_model('../.models/sentiment.h5')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from keras.preprocessing.text import one_hot\n",
|
||||
"from keras.preprocessing.text import text_to_word_sequence\n",
|
||||
"from keras.preprocessing import sequence\n",
|
||||
"\n",
|
||||
"def preprocessing(text): \n",
|
||||
" word2id = imdb.get_word_index()\n",
|
||||
" integers = [word2id.get(i,None) for i in text.split(\" \") if word2id.get(i,None)] \n",
|
||||
" padded = np.array(integers + [0]*(500-len(integers))).reshape(1,-1)\n",
|
||||
" return padded"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def postprocessing(prediction):\n",
|
||||
" if prediction[0] > 0.5:\n",
|
||||
" return \"Good\"\n",
|
||||
" else:\n",
|
||||
" return \"Bad\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"NOTE: Gradio is in beta stage, please report all bugs to: a12d@stanford.edu\n",
|
||||
"Model available locally at: http://localhost:7862/interface.html\n",
|
||||
"Model available publicly for 8 hours at: http://8756b8ce.ngrok.io/interface.html\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Error in connection handler\n",
|
||||
"Traceback (most recent call last):\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\websockets\\server.py\", line 169, in handler\n",
|
||||
" yield from self.ws_handler(self, path)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\gradio\\__init__.py\", line 116, in communicate\n",
|
||||
" prediction = self.predict(processed_input)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\gradio\\__init__.py\", line 100, in predict\n",
|
||||
" return self.model_obj.predict(array)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\engine\\training.py\", line 1169, in predict\n",
|
||||
" steps=steps)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\engine\\training_arrays.py\", line 294, in predict_loop\n",
|
||||
" batch_outs = f(ins_batch)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\", line 2715, in __call__\n",
|
||||
" return self._call(inputs)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\", line 2675, in _call\n",
|
||||
" fetched = self._callable_fn(*array_vals)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\client\\session.py\", line 1382, in __call__\n",
|
||||
" run_metadata_ptr)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\errors_impl.py\", line 519, in __exit__\n",
|
||||
" c_api.TF_GetCode(self.status.status))\n",
|
||||
"tensorflow.python.framework.errors_impl.InvalidArgumentError: indices[0,3] = 5872 is not in [0, 5000)\n",
|
||||
"\t [[Node: embedding_1/embedding_lookup = GatherV2[Taxis=DT_INT32, Tindices=DT_INT32, Tparams=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"](embedding_1/embeddings/read, embedding_1/Cast, embedding_1/embedding_lookup/axis)]]\n",
|
||||
"Error in connection handler\n",
|
||||
"Traceback (most recent call last):\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\websockets\\server.py\", line 169, in handler\n",
|
||||
" yield from self.ws_handler(self, path)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\gradio\\__init__.py\", line 116, in communicate\n",
|
||||
" prediction = self.predict(processed_input)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\gradio\\__init__.py\", line 100, in predict\n",
|
||||
" return self.model_obj.predict(array)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\engine\\training.py\", line 1169, in predict\n",
|
||||
" steps=steps)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\engine\\training_arrays.py\", line 294, in predict_loop\n",
|
||||
" batch_outs = f(ins_batch)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\", line 2715, in __call__\n",
|
||||
" return self._call(inputs)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\", line 2675, in _call\n",
|
||||
" fetched = self._callable_fn(*array_vals)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\client\\session.py\", line 1382, in __call__\n",
|
||||
" run_metadata_ptr)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\errors_impl.py\", line 519, in __exit__\n",
|
||||
" c_api.TF_GetCode(self.status.status))\n",
|
||||
"tensorflow.python.framework.errors_impl.InvalidArgumentError: indices[0,3] = 5872 is not in [0, 5000)\n",
|
||||
"\t [[Node: embedding_1/embedding_lookup = GatherV2[Taxis=DT_INT32, Tindices=DT_INT32, Tparams=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"](embedding_1/embeddings/read, embedding_1/Cast, embedding_1/embedding_lookup/axis)]]\n",
|
||||
"Error in connection handler\n",
|
||||
"Traceback (most recent call last):\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\websockets\\server.py\", line 169, in handler\n",
|
||||
" yield from self.ws_handler(self, path)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\gradio\\__init__.py\", line 116, in communicate\n",
|
||||
" prediction = self.predict(processed_input)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\gradio\\__init__.py\", line 100, in predict\n",
|
||||
" return self.model_obj.predict(array)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\engine\\training.py\", line 1169, in predict\n",
|
||||
" steps=steps)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\engine\\training_arrays.py\", line 294, in predict_loop\n",
|
||||
" batch_outs = f(ins_batch)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\", line 2715, in __call__\n",
|
||||
" return self._call(inputs)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\", line 2675, in _call\n",
|
||||
" fetched = self._callable_fn(*array_vals)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\client\\session.py\", line 1382, in __call__\n",
|
||||
" run_metadata_ptr)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\errors_impl.py\", line 519, in __exit__\n",
|
||||
" c_api.TF_GetCode(self.status.status))\n",
|
||||
"tensorflow.python.framework.errors_impl.InvalidArgumentError: indices[0,4] = 7768 is not in [0, 5000)\n",
|
||||
"\t [[Node: embedding_1/embedding_lookup = GatherV2[Taxis=DT_INT32, Tindices=DT_INT32, Tparams=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"](embedding_1/embeddings/read, embedding_1/Cast, embedding_1/embedding_lookup/axis)]]\n",
|
||||
"Error in connection handler\n",
|
||||
"Traceback (most recent call last):\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\websockets\\server.py\", line 169, in handler\n",
|
||||
" yield from self.ws_handler(self, path)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\gradio\\__init__.py\", line 116, in communicate\n",
|
||||
" prediction = self.predict(processed_input)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\gradio\\__init__.py\", line 100, in predict\n",
|
||||
" return self.model_obj.predict(array)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\engine\\training.py\", line 1169, in predict\n",
|
||||
" steps=steps)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\engine\\training_arrays.py\", line 294, in predict_loop\n",
|
||||
" batch_outs = f(ins_batch)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\", line 2715, in __call__\n",
|
||||
" return self._call(inputs)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\", line 2675, in _call\n",
|
||||
" fetched = self._callable_fn(*array_vals)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\client\\session.py\", line 1382, in __call__\n",
|
||||
" run_metadata_ptr)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\errors_impl.py\", line 519, in __exit__\n",
|
||||
" c_api.TF_GetCode(self.status.status))\n",
|
||||
"tensorflow.python.framework.errors_impl.InvalidArgumentError: indices[0,1] = 7768 is not in [0, 5000)\n",
|
||||
"\t [[Node: embedding_1/embedding_lookup = GatherV2[Taxis=DT_INT32, Tindices=DT_INT32, Tparams=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"](embedding_1/embeddings/read, embedding_1/Cast, embedding_1/embedding_lookup/axis)]]\n",
|
||||
"Error in connection handler\n",
|
||||
"Traceback (most recent call last):\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\websockets\\server.py\", line 169, in handler\n",
|
||||
" yield from self.ws_handler(self, path)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\gradio\\__init__.py\", line 116, in communicate\n",
|
||||
" prediction = self.predict(processed_input)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\gradio\\__init__.py\", line 100, in predict\n",
|
||||
" return self.model_obj.predict(array)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\engine\\training.py\", line 1169, in predict\n",
|
||||
" steps=steps)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\engine\\training_arrays.py\", line 294, in predict_loop\n",
|
||||
" batch_outs = f(ins_batch)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\", line 2715, in __call__\n",
|
||||
" return self._call(inputs)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\", line 2675, in _call\n",
|
||||
" fetched = self._callable_fn(*array_vals)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\client\\session.py\", line 1382, in __call__\n",
|
||||
" run_metadata_ptr)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\errors_impl.py\", line 519, in __exit__\n",
|
||||
" c_api.TF_GetCode(self.status.status))\n",
|
||||
"tensorflow.python.framework.errors_impl.InvalidArgumentError: indices[0,0] = 5872 is not in [0, 5000)\n",
|
||||
"\t [[Node: embedding_1/embedding_lookup = GatherV2[Taxis=DT_INT32, Tindices=DT_INT32, Tparams=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"](embedding_1/embeddings/read, embedding_1/Cast, embedding_1/embedding_lookup/axis)]]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Error in connection handler\n",
|
||||
"Traceback (most recent call last):\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\websockets\\server.py\", line 169, in handler\n",
|
||||
" yield from self.ws_handler(self, path)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\gradio\\__init__.py\", line 116, in communicate\n",
|
||||
" prediction = self.predict(processed_input)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\gradio\\__init__.py\", line 100, in predict\n",
|
||||
" return self.model_obj.predict(array)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\engine\\training.py\", line 1169, in predict\n",
|
||||
" steps=steps)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\engine\\training_arrays.py\", line 294, in predict_loop\n",
|
||||
" batch_outs = f(ins_batch)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\", line 2715, in __call__\n",
|
||||
" return self._call(inputs)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\", line 2675, in _call\n",
|
||||
" fetched = self._callable_fn(*array_vals)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\client\\session.py\", line 1382, in __call__\n",
|
||||
" run_metadata_ptr)\n",
|
||||
" File \"C:\\Users\\islam\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\errors_impl.py\", line 519, in __exit__\n",
|
||||
" c_api.TF_GetCode(self.status.status))\n",
|
||||
"tensorflow.python.framework.errors_impl.InvalidArgumentError: indices[0,2] = 7768 is not in [0, 5000)\n",
|
||||
"\t [[Node: embedding_1/embedding_lookup = GatherV2[Taxis=DT_INT32, Tindices=DT_INT32, Tparams=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"](embedding_1/embeddings/read, embedding_1/Cast, embedding_1/embedding_lookup/axis)]]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"gradio.Interface(input='textbox', output='class', model=model, model_type='keras', preprocessing_fn=preprocessing, postprocessing_fn=postprocessing).launch()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6 (tensorflow)",
|
||||
"language": "python",
|
||||
"name": "tensorflow"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.7"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@ -36,7 +36,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model = load_model('.models/emotion.h5')"
|
||||
"model = load_model('../.models/emotion.h5')"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -60,8 +60,8 @@
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"NOTE: Gradio is in beta stage, please report all bugs to: a12d@stanford.edu\n",
|
||||
"Model available locally at: http://localhost:7861/interface.html\n",
|
||||
"Model available publicly for 8 hours at: https://fe2a5e78.ngrok.io/interface.html\n"
|
||||
"Model available locally at: http://localhost:7865/interface.html\n",
|
||||
"Model available publicly for 8 hours at: http://f7ae4c9b.ngrok.io/interface.html\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
@ -2,7 +2,7 @@
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -37,13 +37,13 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model = load_model('.models/mnist-cnn.h5')"
|
||||
"model = load_model('../.models/mnist-cnn.h5')"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -55,7 +55,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
|
||||
@ -28,16 +28,16 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model = load_model('.models/sentiment.h5')"
|
||||
"model = load_model('../.models/sentiment.h5')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -54,32 +54,22 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def postprocessing(prediction):\n",
|
||||
" if prediction[0] > 0.5:\n",
|
||||
" return \"Happy\"\n",
|
||||
" return \"Positive\"\n",
|
||||
" else:\n",
|
||||
" return \"Sad\""
|
||||
" return \"Negative\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"NOTE: Gradio is in beta stage, please report all bugs to: a12d@stanford.edu\n",
|
||||
"Model available locally at: http://localhost:7875/interface.html\n",
|
||||
"Model available publicly for 8 hours at: http://1c48cf0f.ngrok.io/interface.html\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"gradio.Interface(input='textbox', output='class', model=model, model_type='keras', preprocessing_fn=preprocessing, postprocessing_fn=postprocessing).launch()"
|
||||
]
|
||||
|
||||
BIN
examples/css/.DS_Store
vendored
Normal file
BIN
examples/css/.DS_Store
vendored
Normal file
Binary file not shown.
1912
examples/css/bootstrap-grid.css
vendored
Normal file
1912
examples/css/bootstrap-grid.css
vendored
Normal file
File diff suppressed because it is too large
Load Diff
1
examples/css/bootstrap-grid.css.map
Normal file
1
examples/css/bootstrap-grid.css.map
Normal file
File diff suppressed because one or more lines are too long
7
examples/css/bootstrap-grid.min.css
vendored
Normal file
7
examples/css/bootstrap-grid.min.css
vendored
Normal file
File diff suppressed because one or more lines are too long
1
examples/css/bootstrap-grid.min.css.map
Normal file
1
examples/css/bootstrap-grid.min.css.map
Normal file
File diff suppressed because one or more lines are too long
331
examples/css/bootstrap-reboot.css
vendored
Normal file
331
examples/css/bootstrap-reboot.css
vendored
Normal file
@ -0,0 +1,331 @@
|
||||
/*!
|
||||
* Bootstrap Reboot v4.1.3 (https://getbootstrap.com/)
|
||||
* Copyright 2011-2018 The Bootstrap Authors
|
||||
* Copyright 2011-2018 Twitter, Inc.
|
||||
* Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
|
||||
* Forked from Normalize.css, licensed MIT (https://github.com/necolas/normalize.css/blob/master/LICENSE.md)
|
||||
*/
|
||||
*,
|
||||
*::before,
|
||||
*::after {
|
||||
box-sizing: border-box;
|
||||
}
|
||||
|
||||
html {
|
||||
font-family: sans-serif;
|
||||
line-height: 1.15;
|
||||
-webkit-text-size-adjust: 100%;
|
||||
-ms-text-size-adjust: 100%;
|
||||
-ms-overflow-style: scrollbar;
|
||||
-webkit-tap-highlight-color: rgba(0, 0, 0, 0);
|
||||
}
|
||||
|
||||
@-ms-viewport {
|
||||
width: device-width;
|
||||
}
|
||||
|
||||
article, aside, figcaption, figure, footer, header, hgroup, main, nav, section {
|
||||
display: block;
|
||||
}
|
||||
|
||||
body {
|
||||
margin: 0;
|
||||
font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji";
|
||||
font-size: 1rem;
|
||||
font-weight: 400;
|
||||
line-height: 1.5;
|
||||
color: #212529;
|
||||
text-align: left;
|
||||
background-color: #fff;
|
||||
}
|
||||
|
||||
[tabindex="-1"]:focus {
|
||||
outline: 0 !important;
|
||||
}
|
||||
|
||||
hr {
|
||||
box-sizing: content-box;
|
||||
height: 0;
|
||||
overflow: visible;
|
||||
}
|
||||
|
||||
h1, h2, h3, h4, h5, h6 {
|
||||
margin-top: 0;
|
||||
margin-bottom: 0.5rem;
|
||||
}
|
||||
|
||||
p {
|
||||
margin-top: 0;
|
||||
margin-bottom: 1rem;
|
||||
}
|
||||
|
||||
abbr[title],
|
||||
abbr[data-original-title] {
|
||||
text-decoration: underline;
|
||||
-webkit-text-decoration: underline dotted;
|
||||
text-decoration: underline dotted;
|
||||
cursor: help;
|
||||
border-bottom: 0;
|
||||
}
|
||||
|
||||
address {
|
||||
margin-bottom: 1rem;
|
||||
font-style: normal;
|
||||
line-height: inherit;
|
||||
}
|
||||
|
||||
ol,
|
||||
ul,
|
||||
dl {
|
||||
margin-top: 0;
|
||||
margin-bottom: 1rem;
|
||||
}
|
||||
|
||||
ol ol,
|
||||
ul ul,
|
||||
ol ul,
|
||||
ul ol {
|
||||
margin-bottom: 0;
|
||||
}
|
||||
|
||||
dt {
|
||||
font-weight: 700;
|
||||
}
|
||||
|
||||
dd {
|
||||
margin-bottom: .5rem;
|
||||
margin-left: 0;
|
||||
}
|
||||
|
||||
blockquote {
|
||||
margin: 0 0 1rem;
|
||||
}
|
||||
|
||||
dfn {
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
b,
|
||||
strong {
|
||||
font-weight: bolder;
|
||||
}
|
||||
|
||||
small {
|
||||
font-size: 80%;
|
||||
}
|
||||
|
||||
sub,
|
||||
sup {
|
||||
position: relative;
|
||||
font-size: 75%;
|
||||
line-height: 0;
|
||||
vertical-align: baseline;
|
||||
}
|
||||
|
||||
sub {
|
||||
bottom: -.25em;
|
||||
}
|
||||
|
||||
sup {
|
||||
top: -.5em;
|
||||
}
|
||||
|
||||
a {
|
||||
color: #007bff;
|
||||
text-decoration: none;
|
||||
background-color: transparent;
|
||||
-webkit-text-decoration-skip: objects;
|
||||
}
|
||||
|
||||
a:hover {
|
||||
color: #0056b3;
|
||||
text-decoration: underline;
|
||||
}
|
||||
|
||||
a:not([href]):not([tabindex]) {
|
||||
color: inherit;
|
||||
text-decoration: none;
|
||||
}
|
||||
|
||||
a:not([href]):not([tabindex]):hover, a:not([href]):not([tabindex]):focus {
|
||||
color: inherit;
|
||||
text-decoration: none;
|
||||
}
|
||||
|
||||
a:not([href]):not([tabindex]):focus {
|
||||
outline: 0;
|
||||
}
|
||||
|
||||
pre,
|
||||
code,
|
||||
kbd,
|
||||
samp {
|
||||
font-family: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;
|
||||
font-size: 1em;
|
||||
}
|
||||
|
||||
pre {
|
||||
margin-top: 0;
|
||||
margin-bottom: 1rem;
|
||||
overflow: auto;
|
||||
-ms-overflow-style: scrollbar;
|
||||
}
|
||||
|
||||
figure {
|
||||
margin: 0 0 1rem;
|
||||
}
|
||||
|
||||
img {
|
||||
vertical-align: middle;
|
||||
border-style: none;
|
||||
}
|
||||
|
||||
svg {
|
||||
overflow: hidden;
|
||||
vertical-align: middle;
|
||||
}
|
||||
|
||||
table {
|
||||
border-collapse: collapse;
|
||||
}
|
||||
|
||||
caption {
|
||||
padding-top: 0.75rem;
|
||||
padding-bottom: 0.75rem;
|
||||
color: #6c757d;
|
||||
text-align: left;
|
||||
caption-side: bottom;
|
||||
}
|
||||
|
||||
th {
|
||||
text-align: inherit;
|
||||
}
|
||||
|
||||
label {
|
||||
display: inline-block;
|
||||
margin-bottom: 0.5rem;
|
||||
}
|
||||
|
||||
button {
|
||||
border-radius: 0;
|
||||
}
|
||||
|
||||
button:focus {
|
||||
outline: 1px dotted;
|
||||
outline: 5px auto -webkit-focus-ring-color;
|
||||
}
|
||||
|
||||
input,
|
||||
button,
|
||||
select,
|
||||
optgroup,
|
||||
textarea {
|
||||
margin: 0;
|
||||
font-family: inherit;
|
||||
font-size: inherit;
|
||||
line-height: inherit;
|
||||
}
|
||||
|
||||
button,
|
||||
input {
|
||||
overflow: visible;
|
||||
}
|
||||
|
||||
button,
|
||||
select {
|
||||
text-transform: none;
|
||||
}
|
||||
|
||||
button,
|
||||
html [type="button"],
|
||||
[type="reset"],
|
||||
[type="submit"] {
|
||||
-webkit-appearance: button;
|
||||
}
|
||||
|
||||
button::-moz-focus-inner,
|
||||
[type="button"]::-moz-focus-inner,
|
||||
[type="reset"]::-moz-focus-inner,
|
||||
[type="submit"]::-moz-focus-inner {
|
||||
padding: 0;
|
||||
border-style: none;
|
||||
}
|
||||
|
||||
input[type="radio"],
|
||||
input[type="checkbox"] {
|
||||
box-sizing: border-box;
|
||||
padding: 0;
|
||||
}
|
||||
|
||||
input[type="date"],
|
||||
input[type="time"],
|
||||
input[type="datetime-local"],
|
||||
input[type="month"] {
|
||||
-webkit-appearance: listbox;
|
||||
}
|
||||
|
||||
textarea {
|
||||
overflow: auto;
|
||||
resize: vertical;
|
||||
}
|
||||
|
||||
fieldset {
|
||||
min-width: 0;
|
||||
padding: 0;
|
||||
margin: 0;
|
||||
border: 0;
|
||||
}
|
||||
|
||||
legend {
|
||||
display: block;
|
||||
width: 100%;
|
||||
max-width: 100%;
|
||||
padding: 0;
|
||||
margin-bottom: .5rem;
|
||||
font-size: 1.5rem;
|
||||
line-height: inherit;
|
||||
color: inherit;
|
||||
white-space: normal;
|
||||
}
|
||||
|
||||
progress {
|
||||
vertical-align: baseline;
|
||||
}
|
||||
|
||||
[type="number"]::-webkit-inner-spin-button,
|
||||
[type="number"]::-webkit-outer-spin-button {
|
||||
height: auto;
|
||||
}
|
||||
|
||||
[type="search"] {
|
||||
outline-offset: -2px;
|
||||
-webkit-appearance: none;
|
||||
}
|
||||
|
||||
[type="search"]::-webkit-search-cancel-button,
|
||||
[type="search"]::-webkit-search-decoration {
|
||||
-webkit-appearance: none;
|
||||
}
|
||||
|
||||
::-webkit-file-upload-button {
|
||||
font: inherit;
|
||||
-webkit-appearance: button;
|
||||
}
|
||||
|
||||
output {
|
||||
display: inline-block;
|
||||
}
|
||||
|
||||
summary {
|
||||
display: list-item;
|
||||
cursor: pointer;
|
||||
}
|
||||
|
||||
template {
|
||||
display: none;
|
||||
}
|
||||
|
||||
[hidden] {
|
||||
display: none !important;
|
||||
}
|
||||
/*# sourceMappingURL=bootstrap-reboot.css.map */
|
||||
1
examples/css/bootstrap-reboot.css.map
Normal file
1
examples/css/bootstrap-reboot.css.map
Normal file
File diff suppressed because one or more lines are too long
8
examples/css/bootstrap-reboot.min.css
vendored
Normal file
8
examples/css/bootstrap-reboot.min.css
vendored
Normal file
@ -0,0 +1,8 @@
|
||||
/*!
|
||||
* Bootstrap Reboot v4.1.3 (https://getbootstrap.com/)
|
||||
* Copyright 2011-2018 The Bootstrap Authors
|
||||
* Copyright 2011-2018 Twitter, Inc.
|
||||
* Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
|
||||
* Forked from Normalize.css, licensed MIT (https://github.com/necolas/normalize.css/blob/master/LICENSE.md)
|
||||
*/*,::after,::before{box-sizing:border-box}html{font-family:sans-serif;line-height:1.15;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%;-ms-overflow-style:scrollbar;-webkit-tap-highlight-color:transparent}@-ms-viewport{width:device-width}article,aside,figcaption,figure,footer,header,hgroup,main,nav,section{display:block}body{margin:0;font-family:-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol","Noto Color Emoji";font-size:1rem;font-weight:400;line-height:1.5;color:#212529;text-align:left;background-color:#fff}[tabindex="-1"]:focus{outline:0!important}hr{box-sizing:content-box;height:0;overflow:visible}h1,h2,h3,h4,h5,h6{margin-top:0;margin-bottom:.5rem}p{margin-top:0;margin-bottom:1rem}abbr[data-original-title],abbr[title]{text-decoration:underline;-webkit-text-decoration:underline dotted;text-decoration:underline dotted;cursor:help;border-bottom:0}address{margin-bottom:1rem;font-style:normal;line-height:inherit}dl,ol,ul{margin-top:0;margin-bottom:1rem}ol ol,ol ul,ul ol,ul ul{margin-bottom:0}dt{font-weight:700}dd{margin-bottom:.5rem;margin-left:0}blockquote{margin:0 0 1rem}dfn{font-style:italic}b,strong{font-weight:bolder}small{font-size:80%}sub,sup{position:relative;font-size:75%;line-height:0;vertical-align:baseline}sub{bottom:-.25em}sup{top:-.5em}a{color:#007bff;text-decoration:none;background-color:transparent;-webkit-text-decoration-skip:objects}a:hover{color:#0056b3;text-decoration:underline}a:not([href]):not([tabindex]){color:inherit;text-decoration:none}a:not([href]):not([tabindex]):focus,a:not([href]):not([tabindex]):hover{color:inherit;text-decoration:none}a:not([href]):not([tabindex]):focus{outline:0}code,kbd,pre,samp{font-family:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",monospace;font-size:1em}pre{margin-top:0;margin-bottom:1rem;overflow:auto;-ms-overflow-style:scrollbar}figure{margin:0 0 1rem}img{vertical-align:middle;border-style:none}svg{overflow:hidden;vertical-align:middle}table{border-collapse:collapse}caption{padding-top:.75rem;padding-bottom:.75rem;color:#6c757d;text-align:left;caption-side:bottom}th{text-align:inherit}label{display:inline-block;margin-bottom:.5rem}button{border-radius:0}button:focus{outline:1px dotted;outline:5px auto -webkit-focus-ring-color}button,input,optgroup,select,textarea{margin:0;font-family:inherit;font-size:inherit;line-height:inherit}button,input{overflow:visible}button,select{text-transform:none}[type=reset],[type=submit],button,html [type=button]{-webkit-appearance:button}[type=button]::-moz-focus-inner,[type=reset]::-moz-focus-inner,[type=submit]::-moz-focus-inner,button::-moz-focus-inner{padding:0;border-style:none}input[type=checkbox],input[type=radio]{box-sizing:border-box;padding:0}input[type=date],input[type=datetime-local],input[type=month],input[type=time]{-webkit-appearance:listbox}textarea{overflow:auto;resize:vertical}fieldset{min-width:0;padding:0;margin:0;border:0}legend{display:block;width:100%;max-width:100%;padding:0;margin-bottom:.5rem;font-size:1.5rem;line-height:inherit;color:inherit;white-space:normal}progress{vertical-align:baseline}[type=number]::-webkit-inner-spin-button,[type=number]::-webkit-outer-spin-button{height:auto}[type=search]{outline-offset:-2px;-webkit-appearance:none}[type=search]::-webkit-search-cancel-button,[type=search]::-webkit-search-decoration{-webkit-appearance:none}::-webkit-file-upload-button{font:inherit;-webkit-appearance:button}output{display:inline-block}summary{display:list-item;cursor:pointer}template{display:none}[hidden]{display:none!important}
|
||||
/*# sourceMappingURL=bootstrap-reboot.min.css.map */
|
||||
1
examples/css/bootstrap-reboot.min.css.map
Normal file
1
examples/css/bootstrap-reboot.min.css.map
Normal file
File diff suppressed because one or more lines are too long
9030
examples/css/bootstrap.css
vendored
Normal file
9030
examples/css/bootstrap.css
vendored
Normal file
File diff suppressed because it is too large
Load Diff
1
examples/css/bootstrap.css.map
Normal file
1
examples/css/bootstrap.css.map
Normal file
File diff suppressed because one or more lines are too long
7
examples/css/bootstrap.min.css
vendored
Normal file
7
examples/css/bootstrap.min.css
vendored
Normal file
File diff suppressed because one or more lines are too long
1
examples/css/bootstrap.min.css.map
Normal file
1
examples/css/bootstrap.min.css.map
Normal file
File diff suppressed because one or more lines are too long
147
examples/css/draw-a-digit.css
Normal file
147
examples/css/draw-a-digit.css
Normal file
@ -0,0 +1,147 @@
|
||||
body {
|
||||
padding-top: 5rem;
|
||||
}
|
||||
.starter-template {
|
||||
padding: 3rem 1.5rem;
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
#textbox-output {
|
||||
border-radius: 25px;
|
||||
border: 2px solid #D3D3D3;
|
||||
padding: 20px;
|
||||
width: 400px;
|
||||
height: 400px;
|
||||
}
|
||||
#predict_div {
|
||||
width: 400px;
|
||||
height: 400px;
|
||||
}
|
||||
#predict_text {
|
||||
width: 80vw;
|
||||
height: 80vh;
|
||||
}
|
||||
|
||||
|
||||
h1, h2, h3, h4, h5, h6,
|
||||
.h1, .h2, .h3, .h4, .h5, .h6 {
|
||||
margin-bottom: 0.5rem;
|
||||
font-family: inherit;
|
||||
font-weight: 500;
|
||||
line-height: 1.2;
|
||||
color: inherit;
|
||||
width: 400px;
|
||||
}
|
||||
|
||||
.btn-group > .btn:first-child {
|
||||
margin-left: 75;
|
||||
}
|
||||
|
||||
.btn-group,
|
||||
.btn-group-vertical {
|
||||
position: relative;
|
||||
display: -ms-inline-flexbox;
|
||||
display: inline-flex;
|
||||
vertical-align: middle;
|
||||
width:400px;
|
||||
align-items: center;
|
||||
}
|
||||
|
||||
.uploader {position:relative; overflow:hidden; width:400px; height:400px; background:#f3f3f3; border:2px dashed #e8e8e8; font-family: "Arial"; font-size: 40px; color: darkolivegreen;}
|
||||
|
||||
#filePhoto{
|
||||
position:absolute;
|
||||
width:400px;
|
||||
height:400px;
|
||||
top:-50px;
|
||||
left:0;
|
||||
z-index:2;
|
||||
opacity:0;
|
||||
cursor:pointer;
|
||||
|
||||
}
|
||||
|
||||
.uploader img{
|
||||
position:absolute;
|
||||
width:400px;
|
||||
height:400px;
|
||||
top:-1px;
|
||||
left:-1px;
|
||||
z-index:1;
|
||||
border:none;
|
||||
object-fit: cover;
|
||||
|
||||
}
|
||||
|
||||
|
||||
#predict_div {
|
||||
text-align: center;
|
||||
vertical-align: middle;
|
||||
font-family: 'Arial';
|
||||
color: white;
|
||||
font-size: 60px;
|
||||
word-wrap: break-word;
|
||||
}
|
||||
#textbox-input {
|
||||
border-radius: 25px;
|
||||
border: 2px solid #000;
|
||||
padding: 20px;
|
||||
width: 400px;
|
||||
height: 400px;
|
||||
}
|
||||
|
||||
.footer {
|
||||
position: absolute;
|
||||
bottom: 0;
|
||||
width: 100%;
|
||||
/* Set the fixed height of the footer here */
|
||||
height: 60px;
|
||||
line-height: 60px; /* Vertically center the text there */
|
||||
background-color: #f5f5f5;
|
||||
}
|
||||
|
||||
/* Style all font awesome icons */
|
||||
.fa {
|
||||
padding: 12px;
|
||||
font-size: 30px;
|
||||
width: 40px;
|
||||
text-align: center;
|
||||
text-decoration: none;
|
||||
}
|
||||
|
||||
/* Add a hover effect if you want */
|
||||
.fa:hover {
|
||||
opacity: 0.7;
|
||||
}
|
||||
|
||||
/* Set a specific color for each brand */
|
||||
|
||||
/* Facebook */
|
||||
.fa-facebook {
|
||||
background: #3B5998;
|
||||
color: white;
|
||||
}
|
||||
|
||||
/* Twitter */
|
||||
.fa-twitter {
|
||||
background: #55ACEE;
|
||||
color: white;
|
||||
}
|
||||
|
||||
/* Twitter */
|
||||
.fa-github {
|
||||
background: black;
|
||||
color: white;
|
||||
}
|
||||
|
||||
div#overlay {
|
||||
opacity: 0.5;
|
||||
background: #000;
|
||||
width: 100%;
|
||||
height: 100%;
|
||||
z-index: 10;
|
||||
top: 0;
|
||||
left: 0;
|
||||
position: fixed;
|
||||
}
|
||||
|
||||
388
examples/css/dropzone.css
Normal file
388
examples/css/dropzone.css
Normal file
@ -0,0 +1,388 @@
|
||||
/*
|
||||
* The MIT License
|
||||
* Copyright (c) 2012 Matias Meno <m@tias.me>
|
||||
*/
|
||||
@-webkit-keyframes passing-through {
|
||||
0% {
|
||||
opacity: 0;
|
||||
-webkit-transform: translateY(40px);
|
||||
-moz-transform: translateY(40px);
|
||||
-ms-transform: translateY(40px);
|
||||
-o-transform: translateY(40px);
|
||||
transform: translateY(40px); }
|
||||
30%, 70% {
|
||||
opacity: 1;
|
||||
-webkit-transform: translateY(0px);
|
||||
-moz-transform: translateY(0px);
|
||||
-ms-transform: translateY(0px);
|
||||
-o-transform: translateY(0px);
|
||||
transform: translateY(0px); }
|
||||
100% {
|
||||
opacity: 0;
|
||||
-webkit-transform: translateY(-40px);
|
||||
-moz-transform: translateY(-40px);
|
||||
-ms-transform: translateY(-40px);
|
||||
-o-transform: translateY(-40px);
|
||||
transform: translateY(-40px); } }
|
||||
@-moz-keyframes passing-through {
|
||||
0% {
|
||||
opacity: 0;
|
||||
-webkit-transform: translateY(40px);
|
||||
-moz-transform: translateY(40px);
|
||||
-ms-transform: translateY(40px);
|
||||
-o-transform: translateY(40px);
|
||||
transform: translateY(40px); }
|
||||
30%, 70% {
|
||||
opacity: 1;
|
||||
-webkit-transform: translateY(0px);
|
||||
-moz-transform: translateY(0px);
|
||||
-ms-transform: translateY(0px);
|
||||
-o-transform: translateY(0px);
|
||||
transform: translateY(0px); }
|
||||
100% {
|
||||
opacity: 0;
|
||||
-webkit-transform: translateY(-40px);
|
||||
-moz-transform: translateY(-40px);
|
||||
-ms-transform: translateY(-40px);
|
||||
-o-transform: translateY(-40px);
|
||||
transform: translateY(-40px); } }
|
||||
@keyframes passing-through {
|
||||
0% {
|
||||
opacity: 0;
|
||||
-webkit-transform: translateY(40px);
|
||||
-moz-transform: translateY(40px);
|
||||
-ms-transform: translateY(40px);
|
||||
-o-transform: translateY(40px);
|
||||
transform: translateY(40px); }
|
||||
30%, 70% {
|
||||
opacity: 1;
|
||||
-webkit-transform: translateY(0px);
|
||||
-moz-transform: translateY(0px);
|
||||
-ms-transform: translateY(0px);
|
||||
-o-transform: translateY(0px);
|
||||
transform: translateY(0px); }
|
||||
100% {
|
||||
opacity: 0;
|
||||
-webkit-transform: translateY(-40px);
|
||||
-moz-transform: translateY(-40px);
|
||||
-ms-transform: translateY(-40px);
|
||||
-o-transform: translateY(-40px);
|
||||
transform: translateY(-40px); } }
|
||||
@-webkit-keyframes slide-in {
|
||||
0% {
|
||||
opacity: 0;
|
||||
-webkit-transform: translateY(40px);
|
||||
-moz-transform: translateY(40px);
|
||||
-ms-transform: translateY(40px);
|
||||
-o-transform: translateY(40px);
|
||||
transform: translateY(40px); }
|
||||
30% {
|
||||
opacity: 1;
|
||||
-webkit-transform: translateY(0px);
|
||||
-moz-transform: translateY(0px);
|
||||
-ms-transform: translateY(0px);
|
||||
-o-transform: translateY(0px);
|
||||
transform: translateY(0px); } }
|
||||
@-moz-keyframes slide-in {
|
||||
0% {
|
||||
opacity: 0;
|
||||
-webkit-transform: translateY(40px);
|
||||
-moz-transform: translateY(40px);
|
||||
-ms-transform: translateY(40px);
|
||||
-o-transform: translateY(40px);
|
||||
transform: translateY(40px); }
|
||||
30% {
|
||||
opacity: 1;
|
||||
-webkit-transform: translateY(0px);
|
||||
-moz-transform: translateY(0px);
|
||||
-ms-transform: translateY(0px);
|
||||
-o-transform: translateY(0px);
|
||||
transform: translateY(0px); } }
|
||||
@keyframes slide-in {
|
||||
0% {
|
||||
opacity: 0;
|
||||
-webkit-transform: translateY(40px);
|
||||
-moz-transform: translateY(40px);
|
||||
-ms-transform: translateY(40px);
|
||||
-o-transform: translateY(40px);
|
||||
transform: translateY(40px); }
|
||||
30% {
|
||||
opacity: 1;
|
||||
-webkit-transform: translateY(0px);
|
||||
-moz-transform: translateY(0px);
|
||||
-ms-transform: translateY(0px);
|
||||
-o-transform: translateY(0px);
|
||||
transform: translateY(0px); } }
|
||||
@-webkit-keyframes pulse {
|
||||
0% {
|
||||
-webkit-transform: scale(1);
|
||||
-moz-transform: scale(1);
|
||||
-ms-transform: scale(1);
|
||||
-o-transform: scale(1);
|
||||
transform: scale(1); }
|
||||
10% {
|
||||
-webkit-transform: scale(1.1);
|
||||
-moz-transform: scale(1.1);
|
||||
-ms-transform: scale(1.1);
|
||||
-o-transform: scale(1.1);
|
||||
transform: scale(1.1); }
|
||||
20% {
|
||||
-webkit-transform: scale(1);
|
||||
-moz-transform: scale(1);
|
||||
-ms-transform: scale(1);
|
||||
-o-transform: scale(1);
|
||||
transform: scale(1); } }
|
||||
@-moz-keyframes pulse {
|
||||
0% {
|
||||
-webkit-transform: scale(1);
|
||||
-moz-transform: scale(1);
|
||||
-ms-transform: scale(1);
|
||||
-o-transform: scale(1);
|
||||
transform: scale(1); }
|
||||
10% {
|
||||
-webkit-transform: scale(1.1);
|
||||
-moz-transform: scale(1.1);
|
||||
-ms-transform: scale(1.1);
|
||||
-o-transform: scale(1.1);
|
||||
transform: scale(1.1); }
|
||||
20% {
|
||||
-webkit-transform: scale(1);
|
||||
-moz-transform: scale(1);
|
||||
-ms-transform: scale(1);
|
||||
-o-transform: scale(1);
|
||||
transform: scale(1); } }
|
||||
@keyframes pulse {
|
||||
0% {
|
||||
-webkit-transform: scale(1);
|
||||
-moz-transform: scale(1);
|
||||
-ms-transform: scale(1);
|
||||
-o-transform: scale(1);
|
||||
transform: scale(1); }
|
||||
10% {
|
||||
-webkit-transform: scale(1.1);
|
||||
-moz-transform: scale(1.1);
|
||||
-ms-transform: scale(1.1);
|
||||
-o-transform: scale(1.1);
|
||||
transform: scale(1.1); }
|
||||
20% {
|
||||
-webkit-transform: scale(1);
|
||||
-moz-transform: scale(1);
|
||||
-ms-transform: scale(1);
|
||||
-o-transform: scale(1);
|
||||
transform: scale(1); } }
|
||||
.dropzone, .dropzone * {
|
||||
box-sizing: border-box; }
|
||||
|
||||
.dropzone {
|
||||
min-height: 150px;
|
||||
border: 2px solid rgba(0, 0, 0, 0.3);
|
||||
background: white;
|
||||
padding: 20px 20px; }
|
||||
.dropzone.dz-clickable {
|
||||
cursor: pointer; }
|
||||
.dropzone.dz-clickable * {
|
||||
cursor: default; }
|
||||
.dropzone.dz-clickable .dz-message, .dropzone.dz-clickable .dz-message * {
|
||||
cursor: pointer; }
|
||||
.dropzone.dz-started .dz-message {
|
||||
display: none; }
|
||||
.dropzone.dz-drag-hover {
|
||||
border-style: solid; }
|
||||
.dropzone.dz-drag-hover .dz-message {
|
||||
opacity: 0.5; }
|
||||
.dropzone .dz-message {
|
||||
text-align: center;
|
||||
margin: 2em 0; }
|
||||
.dropzone .dz-preview {
|
||||
position: relative;
|
||||
display: inline-block;
|
||||
vertical-align: top;
|
||||
margin: 16px;
|
||||
min-height: 100px; }
|
||||
.dropzone .dz-preview:hover {
|
||||
z-index: 1000; }
|
||||
.dropzone .dz-preview:hover .dz-details {
|
||||
opacity: 1; }
|
||||
.dropzone .dz-preview.dz-file-preview .dz-image {
|
||||
border-radius: 20px;
|
||||
background: #999;
|
||||
background: linear-gradient(to bottom, #eee, #ddd); }
|
||||
.dropzone .dz-preview.dz-file-preview .dz-details {
|
||||
opacity: 1; }
|
||||
.dropzone .dz-preview.dz-image-preview {
|
||||
background: white; }
|
||||
.dropzone .dz-preview.dz-image-preview .dz-details {
|
||||
-webkit-transition: opacity 0.2s linear;
|
||||
-moz-transition: opacity 0.2s linear;
|
||||
-ms-transition: opacity 0.2s linear;
|
||||
-o-transition: opacity 0.2s linear;
|
||||
transition: opacity 0.2s linear; }
|
||||
.dropzone .dz-preview .dz-remove {
|
||||
font-size: 14px;
|
||||
text-align: center;
|
||||
display: block;
|
||||
cursor: pointer;
|
||||
border: none; }
|
||||
.dropzone .dz-preview .dz-remove:hover {
|
||||
text-decoration: underline; }
|
||||
.dropzone .dz-preview:hover .dz-details {
|
||||
opacity: 1; }
|
||||
.dropzone .dz-preview .dz-details {
|
||||
z-index: 20;
|
||||
position: absolute;
|
||||
top: 0;
|
||||
left: 0;
|
||||
opacity: 0;
|
||||
font-size: 13px;
|
||||
min-width: 100%;
|
||||
max-width: 100%;
|
||||
padding: 2em 1em;
|
||||
text-align: center;
|
||||
color: rgba(0, 0, 0, 0.9);
|
||||
line-height: 150%; }
|
||||
.dropzone .dz-preview .dz-details .dz-size {
|
||||
margin-bottom: 1em;
|
||||
font-size: 16px; }
|
||||
.dropzone .dz-preview .dz-details .dz-filename {
|
||||
white-space: nowrap; }
|
||||
.dropzone .dz-preview .dz-details .dz-filename:hover span {
|
||||
border: 1px solid rgba(200, 200, 200, 0.8);
|
||||
background-color: rgba(255, 255, 255, 0.8); }
|
||||
.dropzone .dz-preview .dz-details .dz-filename:not(:hover) {
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis; }
|
||||
.dropzone .dz-preview .dz-details .dz-filename:not(:hover) span {
|
||||
border: 1px solid transparent; }
|
||||
.dropzone .dz-preview .dz-details .dz-filename span, .dropzone .dz-preview .dz-details .dz-size span {
|
||||
background-color: rgba(255, 255, 255, 0.4);
|
||||
padding: 0 0.4em;
|
||||
border-radius: 3px; }
|
||||
.dropzone .dz-preview:hover .dz-image img {
|
||||
-webkit-transform: scale(1.05, 1.05);
|
||||
-moz-transform: scale(1.05, 1.05);
|
||||
-ms-transform: scale(1.05, 1.05);
|
||||
-o-transform: scale(1.05, 1.05);
|
||||
transform: scale(1.05, 1.05);
|
||||
-webkit-filter: blur(8px);
|
||||
filter: blur(8px); }
|
||||
.dropzone .dz-preview .dz-image {
|
||||
border-radius: 20px;
|
||||
overflow: hidden;
|
||||
width: 120px;
|
||||
height: 120px;
|
||||
position: relative;
|
||||
display: block;
|
||||
z-index: 10; }
|
||||
.dropzone .dz-preview .dz-image img {
|
||||
display: block; }
|
||||
.dropzone .dz-preview.dz-success .dz-success-mark {
|
||||
-webkit-animation: passing-through 3s cubic-bezier(0.77, 0, 0.175, 1);
|
||||
-moz-animation: passing-through 3s cubic-bezier(0.77, 0, 0.175, 1);
|
||||
-ms-animation: passing-through 3s cubic-bezier(0.77, 0, 0.175, 1);
|
||||
-o-animation: passing-through 3s cubic-bezier(0.77, 0, 0.175, 1);
|
||||
animation: passing-through 3s cubic-bezier(0.77, 0, 0.175, 1); }
|
||||
.dropzone .dz-preview.dz-error .dz-error-mark {
|
||||
opacity: 1;
|
||||
-webkit-animation: slide-in 3s cubic-bezier(0.77, 0, 0.175, 1);
|
||||
-moz-animation: slide-in 3s cubic-bezier(0.77, 0, 0.175, 1);
|
||||
-ms-animation: slide-in 3s cubic-bezier(0.77, 0, 0.175, 1);
|
||||
-o-animation: slide-in 3s cubic-bezier(0.77, 0, 0.175, 1);
|
||||
animation: slide-in 3s cubic-bezier(0.77, 0, 0.175, 1); }
|
||||
.dropzone .dz-preview .dz-success-mark, .dropzone .dz-preview .dz-error-mark {
|
||||
pointer-events: none;
|
||||
opacity: 0;
|
||||
z-index: 500;
|
||||
position: absolute;
|
||||
display: block;
|
||||
top: 50%;
|
||||
left: 50%;
|
||||
margin-left: -27px;
|
||||
margin-top: -27px; }
|
||||
.dropzone .dz-preview .dz-success-mark svg, .dropzone .dz-preview .dz-error-mark svg {
|
||||
display: block;
|
||||
width: 54px;
|
||||
height: 54px; }
|
||||
.dropzone .dz-preview.dz-processing .dz-progress {
|
||||
opacity: 1;
|
||||
-webkit-transition: all 0.2s linear;
|
||||
-moz-transition: all 0.2s linear;
|
||||
-ms-transition: all 0.2s linear;
|
||||
-o-transition: all 0.2s linear;
|
||||
transition: all 0.2s linear; }
|
||||
.dropzone .dz-preview.dz-complete .dz-progress {
|
||||
opacity: 0;
|
||||
-webkit-transition: opacity 0.4s ease-in;
|
||||
-moz-transition: opacity 0.4s ease-in;
|
||||
-ms-transition: opacity 0.4s ease-in;
|
||||
-o-transition: opacity 0.4s ease-in;
|
||||
transition: opacity 0.4s ease-in; }
|
||||
.dropzone .dz-preview:not(.dz-processing) .dz-progress {
|
||||
-webkit-animation: pulse 6s ease infinite;
|
||||
-moz-animation: pulse 6s ease infinite;
|
||||
-ms-animation: pulse 6s ease infinite;
|
||||
-o-animation: pulse 6s ease infinite;
|
||||
animation: pulse 6s ease infinite; }
|
||||
.dropzone .dz-preview .dz-progress {
|
||||
opacity: 1;
|
||||
z-index: 1000;
|
||||
pointer-events: none;
|
||||
position: absolute;
|
||||
height: 16px;
|
||||
left: 50%;
|
||||
top: 50%;
|
||||
margin-top: -8px;
|
||||
width: 80px;
|
||||
margin-left: -40px;
|
||||
background: rgba(255, 255, 255, 0.9);
|
||||
-webkit-transform: scale(1);
|
||||
border-radius: 8px;
|
||||
overflow: hidden; }
|
||||
.dropzone .dz-preview .dz-progress .dz-upload {
|
||||
background: #333;
|
||||
background: linear-gradient(to bottom, #666, #444);
|
||||
position: absolute;
|
||||
top: 0;
|
||||
left: 0;
|
||||
bottom: 0;
|
||||
width: 0;
|
||||
-webkit-transition: width 300ms ease-in-out;
|
||||
-moz-transition: width 300ms ease-in-out;
|
||||
-ms-transition: width 300ms ease-in-out;
|
||||
-o-transition: width 300ms ease-in-out;
|
||||
transition: width 300ms ease-in-out; }
|
||||
.dropzone .dz-preview.dz-error .dz-error-message {
|
||||
display: block; }
|
||||
.dropzone .dz-preview.dz-error:hover .dz-error-message {
|
||||
opacity: 1;
|
||||
pointer-events: auto; }
|
||||
.dropzone .dz-preview .dz-error-message {
|
||||
pointer-events: none;
|
||||
z-index: 1000;
|
||||
position: absolute;
|
||||
display: block;
|
||||
display: none;
|
||||
opacity: 0;
|
||||
-webkit-transition: opacity 0.3s ease;
|
||||
-moz-transition: opacity 0.3s ease;
|
||||
-ms-transition: opacity 0.3s ease;
|
||||
-o-transition: opacity 0.3s ease;
|
||||
transition: opacity 0.3s ease;
|
||||
border-radius: 8px;
|
||||
font-size: 13px;
|
||||
top: 130px;
|
||||
left: -10px;
|
||||
width: 140px;
|
||||
background: #be2626;
|
||||
background: linear-gradient(to bottom, #be2626, #a92222);
|
||||
padding: 0.5em 1.2em;
|
||||
color: white; }
|
||||
.dropzone .dz-preview .dz-error-message:after {
|
||||
content: '';
|
||||
position: absolute;
|
||||
top: -6px;
|
||||
left: 64px;
|
||||
width: 0;
|
||||
height: 0;
|
||||
border-left: 6px solid transparent;
|
||||
border-right: 6px solid transparent;
|
||||
border-bottom: 6px solid #be2626; }
|
||||
114
examples/css/index.css
Normal file
114
examples/css/index.css
Normal file
@ -0,0 +1,114 @@
|
||||
:root {
|
||||
--jumbotron-padding-y: 3rem;
|
||||
}
|
||||
|
||||
.lead{
|
||||
color:white !important;
|
||||
}
|
||||
|
||||
.jumbotron {
|
||||
padding-top: var(--jumbotron-padding-y);
|
||||
padding-bottom: var(--jumbotron-padding-y);
|
||||
margin-bottom: 0;
|
||||
background-color: #fff;
|
||||
background-image: url("https://static1.squarespace.com/static/5bf1fef3b27e396eb3c91f08/t/5c10b7b40e2e72725c2f3238/1544599520911/?format=2500w");
|
||||
background-position: 10% 20%;
|
||||
color:white
|
||||
}
|
||||
@media (min-width: 768px) {
|
||||
.jumbotron {
|
||||
padding-top: calc(var(--jumbotron-padding-y) * 2);
|
||||
padding-bottom: calc(var(--jumbotron-padding-y) * 2);
|
||||
}
|
||||
}
|
||||
|
||||
.codebox {
|
||||
/* Below are styles for the codebox (not the code itself) */
|
||||
border:1px solid black;
|
||||
background-color:#e3f2fd;
|
||||
width:600px;
|
||||
overflow:auto;
|
||||
padding:10px;
|
||||
}
|
||||
.codebox code {
|
||||
/* Styles in here affect the text of the codebox */
|
||||
font-size:0.9em;
|
||||
/* You could also put all sorts of styling here, like different font, color, underline, etc. for the code. */
|
||||
}
|
||||
|
||||
|
||||
.jumbotron p:last-child {
|
||||
margin-bottom: 0;
|
||||
}
|
||||
|
||||
.jumbotron-heading {
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
.jumbotron .container {
|
||||
max-width: 40rem;
|
||||
}
|
||||
|
||||
footer {
|
||||
padding-top: 3rem;
|
||||
padding-bottom: 3rem;
|
||||
}
|
||||
|
||||
footer p {
|
||||
margin-bottom: .25rem;
|
||||
}
|
||||
|
||||
.btn-pink{
|
||||
background-color: #FF6384;
|
||||
color: white;
|
||||
padding
|
||||
}
|
||||
.btn-pink:hover{
|
||||
background-color: #f15d7c;
|
||||
color: white;
|
||||
}
|
||||
|
||||
/* Style all font awesome icons */
|
||||
.fa {
|
||||
padding: 12px;
|
||||
font-size: 30px;
|
||||
width: 40px;
|
||||
text-align: center;
|
||||
text-decoration: none;
|
||||
}
|
||||
|
||||
/* Add a hover effect if you want */
|
||||
.fa:hover {
|
||||
opacity: 0.7;
|
||||
}
|
||||
|
||||
/* Set a specific color for each brand */
|
||||
|
||||
/* Facebook */
|
||||
.fa-facebook {
|
||||
background: #3B5998;
|
||||
color: white;
|
||||
}
|
||||
|
||||
/* Twitter */
|
||||
.fa-twitter {
|
||||
background: #55ACEE;
|
||||
color: white;
|
||||
}
|
||||
|
||||
/* Twitter */
|
||||
.fa-github {
|
||||
background: black;
|
||||
color: white;
|
||||
}
|
||||
|
||||
.footer {
|
||||
position: static;
|
||||
bottom: 0;
|
||||
width: 100%;
|
||||
/* Set the fixed height of the footer here */
|
||||
height: 60px;
|
||||
line-height: 60px; /* Vertically center the text there */
|
||||
background-color: #f5f5f5;
|
||||
padding: 0px;
|
||||
}
|
||||
86
examples/interface.html
Normal file
86
examples/interface.html
Normal file
@ -0,0 +1,86 @@
|
||||
<bound method Tag.prettify of
|
||||
<!DOCTYPE doctype html>
|
||||
|
||||
<script src="../js/all-io.js"></script>
|
||||
<script crossorigin="anonymous" integrity="sha384-KJ3o2DKtIkvYIK3UENzmM7KCkRr/rE9/Qpg6aAZGJwFDMVNA/GpGFF93hXpG5KkN" src="https://code.jquery.com/jquery-3.2.1.slim.min.js"></script>
|
||||
<script src="../js/bootstrap.min.js"></script>
|
||||
<script src="../js/bootstrap-notify.min.js"></script>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="utf-8"/>
|
||||
<meta content="width=device-width, initial-scale=1, shrink-to-fit=no" name="viewport"/>
|
||||
<meta content="" name="description"/>
|
||||
<meta content="" name="author"/>
|
||||
<title>Gradio</title>
|
||||
<!-- Bootstrap core CSS -->
|
||||
<link href="../css/bootstrap.min.css" rel="stylesheet"/>
|
||||
<link href="../css/draw-a-digit.css" rel="stylesheet"/>
|
||||
<link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css" rel="stylesheet"/>
|
||||
</head>
|
||||
<body>
|
||||
<div id="overlay" style="visibility: hidden"></div>
|
||||
<nav class="navbar navbar-expand-md navbar-dark bg-dark fixed-top">
|
||||
<a class="navbar-brand" href="#">Gradio</a>
|
||||
<button aria-controls="navbarsExampleDefault" aria-expanded="false" aria-label="Toggle navigation" class="navbar-toggler" data-target="#navbarsExampleDefault" data-toggle="collapse" type="button">
|
||||
<span class="navbar-toggler-icon"></span>
|
||||
</button>
|
||||
<div class="collapse navbar-collapse" id="navbarsExampleDefault">
|
||||
<ul class="navbar-nav mr-auto">
|
||||
</ul>
|
||||
<ul class="navbar-nav">
|
||||
<!--<li class="nav-item">-->
|
||||
<!--<a class="nav-link" href="#">Help</a>-->
|
||||
<!--</li>-->
|
||||
<li class="nav-item">
|
||||
<em><strong><span id="model-status" style="color:#3ee263">Model: running</span></strong></em>
|
||||
</li>
|
||||
</ul>
|
||||
</div>
|
||||
</nav>
|
||||
<main class="container starter-template" role="main">
|
||||
<div class="row">
|
||||
<!-- INPUT
|
||||
====================================================================================================================================================== -->
|
||||
<div class="col-6">
|
||||
<h5>Sketch Pad Input: (Use your cursor to draw)</h5>
|
||||
<canvas height="400" id="canvas" width="400"></canvas><br/>
|
||||
<div aria-label="Basic example" class="btn-group" role="group">
|
||||
<button class="btn btn-primary" id="submit-button" type="button">Recognize</button>
|
||||
<button class="btn btn-secondary" id="clear-button" type="button">Clear</button>
|
||||
</div>
|
||||
</div>
|
||||
<script src="../js/sketchpad-input.js"></script>
|
||||
|
||||
<!-- OUTPUT
|
||||
====================================================================================================================================================== -->
|
||||
<div class="col-6">
|
||||
<h5>Class Output:</h5>
|
||||
<div id="predict_div" style="background-color:grey">
|
||||
</div>
|
||||
</div>
|
||||
<script src="../js/class-output.js"></script>
|
||||
|
||||
</div>
|
||||
</main></body></html>
|
||||
<!-- /.container -->
|
||||
<footer class="footer">
|
||||
<div class="container">
|
||||
<span class="text-muted">
|
||||
<!-- Add font awesome icons -->
|
||||
Gradio is open-source, help make it better
|
||||
<a class="fa fa-github" href="https://github.com/abidlabs/gradiome" target="_blank"></a>
|
||||
</span>
|
||||
<span class="text-muted pull-right">
|
||||
<!-- Add font awesome icons -->
|
||||
Found this useful? Kindly spread the word
|
||||
<a class="fa fa-facebook" href="https://www.facebook.com/sharer/sharer.php?u=www.siliconprep.com" target="_blank"></a>
|
||||
<a class="fa fa-twitter" href="https://twitter.com/home?status=Check%20out%20Gradio%20tool%20at%20www.siliconprep.com" target="_blank"></a>
|
||||
</span>
|
||||
</div>
|
||||
</footer>
|
||||
<!-- Bootstrap core JavaScript
|
||||
================================================== -->
|
||||
<!-- Placed at the end of the document so the pages load faster -->
|
||||
|
||||
|
||||
>
|
||||
28
examples/js/all-io.js
Normal file
28
examples/js/all-io.js
Normal file
@ -0,0 +1,28 @@
|
||||
var NGROK_URL = "ws://d2effd81.ngrok.io"
|
||||
var SOCKET_PORT = 9200
|
||||
|
||||
try {
|
||||
var origin = window.location.origin;
|
||||
if (origin.includes("ngrok")){
|
||||
var ws = new WebSocket(NGROK_URL)
|
||||
} else {
|
||||
var ws = new WebSocket("ws://127.0.0.1:" + SOCKET_PORT + "/")
|
||||
}
|
||||
ws.onerror = function(evt) {
|
||||
console.log(evt)
|
||||
};
|
||||
ws.onclose = function(event) {
|
||||
console.log("WebSocket is closed now.");
|
||||
var model_status = $('#model-status')
|
||||
model_status.html('Model: closed');
|
||||
model_status.css('color', '#e23e44');
|
||||
$('#overlay').css('visibility','visible')
|
||||
};
|
||||
|
||||
} catch (e) {
|
||||
console.log(e)
|
||||
}
|
||||
|
||||
const sleep = (milliseconds) => {
|
||||
return new Promise(resolve => setTimeout(resolve, milliseconds))
|
||||
}
|
||||
255
examples/js/audio-input.js
Normal file
255
examples/js/audio-input.js
Normal file
@ -0,0 +1,255 @@
|
||||
import InlineWorker from 'inline-worker';
|
||||
|
||||
export class Recorder {
|
||||
config = {
|
||||
bufferLen: 4096,
|
||||
numChannels: 2,
|
||||
mimeType: 'audio/wav'
|
||||
};
|
||||
|
||||
recording = false;
|
||||
|
||||
callbacks = {
|
||||
getBuffer: [],
|
||||
exportWAV: []
|
||||
};
|
||||
|
||||
constructor(source, cfg) {
|
||||
Object.assign(this.config, cfg);
|
||||
this.context = source.context;
|
||||
this.node = (this.context.createScriptProcessor ||
|
||||
this.context.createJavaScriptNode).call(this.context,
|
||||
this.config.bufferLen, this.config.numChannels, this.config.numChannels);
|
||||
|
||||
this.node.onaudioprocess = (e) => {
|
||||
if (!this.recording) return;
|
||||
|
||||
var buffer = [];
|
||||
for (var channel = 0; channel < this.config.numChannels; channel++) {
|
||||
buffer.push(e.inputBuffer.getChannelData(channel));
|
||||
}
|
||||
this.worker.postMessage({
|
||||
command: 'record',
|
||||
buffer: buffer
|
||||
});
|
||||
};
|
||||
|
||||
source.connect(this.node);
|
||||
this.node.connect(this.context.destination); //this should not be necessary
|
||||
|
||||
let self = {};
|
||||
this.worker = new InlineWorker(function () {
|
||||
let recLength = 0,
|
||||
recBuffers = [],
|
||||
sampleRate,
|
||||
numChannels;
|
||||
|
||||
this.onmessage = function (e) {
|
||||
switch (e.data.command) {
|
||||
case 'init':
|
||||
init(e.data.config);
|
||||
break;
|
||||
case 'record':
|
||||
record(e.data.buffer);
|
||||
break;
|
||||
case 'exportWAV':
|
||||
exportWAV(e.data.type);
|
||||
break;
|
||||
case 'getBuffer':
|
||||
getBuffer();
|
||||
break;
|
||||
case 'clear':
|
||||
clear();
|
||||
break;
|
||||
}
|
||||
};
|
||||
|
||||
function init(config) {
|
||||
sampleRate = config.sampleRate;
|
||||
numChannels = config.numChannels;
|
||||
initBuffers();
|
||||
}
|
||||
|
||||
function record(inputBuffer) {
|
||||
for (var channel = 0; channel < numChannels; channel++) {
|
||||
recBuffers[channel].push(inputBuffer[channel]);
|
||||
}
|
||||
recLength += inputBuffer[0].length;
|
||||
}
|
||||
|
||||
function exportWAV(type) {
|
||||
let buffers = [];
|
||||
for (let channel = 0; channel < numChannels; channel++) {
|
||||
buffers.push(mergeBuffers(recBuffers[channel], recLength));
|
||||
}
|
||||
let interleaved;
|
||||
if (numChannels === 2) {
|
||||
interleaved = interleave(buffers[0], buffers[1]);
|
||||
} else {
|
||||
interleaved = buffers[0];
|
||||
}
|
||||
let dataview = encodeWAV(interleaved);
|
||||
let audioBlob = new Blob([dataview], {type: type});
|
||||
|
||||
this.postMessage({command: 'exportWAV', data: audioBlob});
|
||||
}
|
||||
|
||||
function getBuffer() {
|
||||
let buffers = [];
|
||||
for (let channel = 0; channel < numChannels; channel++) {
|
||||
buffers.push(mergeBuffers(recBuffers[channel], recLength));
|
||||
}
|
||||
this.postMessage({command: 'getBuffer', data: buffers});
|
||||
}
|
||||
|
||||
function clear() {
|
||||
recLength = 0;
|
||||
recBuffers = [];
|
||||
initBuffers();
|
||||
}
|
||||
|
||||
function initBuffers() {
|
||||
for (let channel = 0; channel < numChannels; channel++) {
|
||||
recBuffers[channel] = [];
|
||||
}
|
||||
}
|
||||
|
||||
function mergeBuffers(recBuffers, recLength) {
|
||||
let result = new Float32Array(recLength);
|
||||
let offset = 0;
|
||||
for (let i = 0; i < recBuffers.length; i++) {
|
||||
result.set(recBuffers[i], offset);
|
||||
offset += recBuffers[i].length;
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
function interleave(inputL, inputR) {
|
||||
let length = inputL.length + inputR.length;
|
||||
let result = new Float32Array(length);
|
||||
|
||||
let index = 0,
|
||||
inputIndex = 0;
|
||||
|
||||
while (index < length) {
|
||||
result[index++] = inputL[inputIndex];
|
||||
result[index++] = inputR[inputIndex];
|
||||
inputIndex++;
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
function floatTo16BitPCM(output, offset, input) {
|
||||
for (let i = 0; i < input.length; i++, offset += 2) {
|
||||
let s = Math.max(-1, Math.min(1, input[i]));
|
||||
output.setInt16(offset, s < 0 ? s * 0x8000 : s * 0x7FFF, true);
|
||||
}
|
||||
}
|
||||
|
||||
function writeString(view, offset, string) {
|
||||
for (let i = 0; i < string.length; i++) {
|
||||
view.setUint8(offset + i, string.charCodeAt(i));
|
||||
}
|
||||
}
|
||||
|
||||
function encodeWAV(samples) {
|
||||
let buffer = new ArrayBuffer(44 + samples.length * 2);
|
||||
let view = new DataView(buffer);
|
||||
|
||||
/* RIFF identifier */
|
||||
writeString(view, 0, 'RIFF');
|
||||
/* RIFF chunk length */
|
||||
view.setUint32(4, 36 + samples.length * 2, true);
|
||||
/* RIFF type */
|
||||
writeString(view, 8, 'WAVE');
|
||||
/* format chunk identifier */
|
||||
writeString(view, 12, 'fmt ');
|
||||
/* format chunk length */
|
||||
view.setUint32(16, 16, true);
|
||||
/* sample format (raw) */
|
||||
view.setUint16(20, 1, true);
|
||||
/* channel count */
|
||||
view.setUint16(22, numChannels, true);
|
||||
/* sample rate */
|
||||
view.setUint32(24, sampleRate, true);
|
||||
/* byte rate (sample rate * block align) */
|
||||
view.setUint32(28, sampleRate * 4, true);
|
||||
/* block align (channel count * bytes per sample) */
|
||||
view.setUint16(32, numChannels * 2, true);
|
||||
/* bits per sample */
|
||||
view.setUint16(34, 16, true);
|
||||
/* data chunk identifier */
|
||||
writeString(view, 36, 'data');
|
||||
/* data chunk length */
|
||||
view.setUint32(40, samples.length * 2, true);
|
||||
|
||||
floatTo16BitPCM(view, 44, samples);
|
||||
|
||||
return view;
|
||||
}
|
||||
}, self);
|
||||
|
||||
this.worker.postMessage({
|
||||
command: 'init',
|
||||
config: {
|
||||
sampleRate: this.context.sampleRate,
|
||||
numChannels: this.config.numChannels
|
||||
}
|
||||
});
|
||||
|
||||
this.worker.onmessage = (e) => {
|
||||
let cb = this.callbacks[e.data.command].pop();
|
||||
if (typeof cb == 'function') {
|
||||
cb(e.data.data);
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
|
||||
record() {
|
||||
this.recording = true;
|
||||
}
|
||||
|
||||
stop() {
|
||||
this.recording = false;
|
||||
}
|
||||
|
||||
clear() {
|
||||
this.worker.postMessage({command: 'clear'});
|
||||
}
|
||||
|
||||
getBuffer(cb) {
|
||||
cb = cb || this.config.callback;
|
||||
if (!cb) throw new Error('Callback not set');
|
||||
|
||||
this.callbacks.getBuffer.push(cb);
|
||||
|
||||
this.worker.postMessage({command: 'getBuffer'});
|
||||
}
|
||||
|
||||
exportWAV(cb, mimeType) {
|
||||
mimeType = mimeType || this.config.mimeType;
|
||||
cb = cb || this.config.callback;
|
||||
if (!cb) throw new Error('Callback not set');
|
||||
|
||||
this.callbacks.exportWAV.push(cb);
|
||||
|
||||
this.worker.postMessage({
|
||||
command: 'exportWAV',
|
||||
type: mimeType
|
||||
});
|
||||
}
|
||||
|
||||
static
|
||||
forceDownload(blob, filename) {
|
||||
let url = (window.URL || window.webkitURL).createObjectURL(blob);
|
||||
let link = window.document.createElement('a');
|
||||
link.href = url;
|
||||
link.download = filename || 'output.wav';
|
||||
let click = document.createEvent("Event");
|
||||
click.initEvent("click", true, true);
|
||||
link.dispatchEvent(click);
|
||||
}
|
||||
}
|
||||
|
||||
export default Recorder;
|
||||
2
examples/js/bootstrap-notify.min.js
vendored
Normal file
2
examples/js/bootstrap-notify.min.js
vendored
Normal file
File diff suppressed because one or more lines are too long
6461
examples/js/bootstrap.bundle.js
vendored
Normal file
6461
examples/js/bootstrap.bundle.js
vendored
Normal file
File diff suppressed because it is too large
Load Diff
1
examples/js/bootstrap.bundle.js.map
Normal file
1
examples/js/bootstrap.bundle.js.map
Normal file
File diff suppressed because one or more lines are too long
7
examples/js/bootstrap.bundle.min.js
vendored
Normal file
7
examples/js/bootstrap.bundle.min.js
vendored
Normal file
File diff suppressed because one or more lines are too long
1
examples/js/bootstrap.bundle.min.js.map
Normal file
1
examples/js/bootstrap.bundle.min.js.map
Normal file
File diff suppressed because one or more lines are too long
3944
examples/js/bootstrap.js
vendored
Normal file
3944
examples/js/bootstrap.js
vendored
Normal file
File diff suppressed because it is too large
Load Diff
1
examples/js/bootstrap.js.map
Normal file
1
examples/js/bootstrap.js.map
Normal file
File diff suppressed because one or more lines are too long
7
examples/js/bootstrap.min.js
vendored
Normal file
7
examples/js/bootstrap.min.js
vendored
Normal file
File diff suppressed because one or more lines are too long
1
examples/js/bootstrap.min.js.map
Normal file
1
examples/js/bootstrap.min.js.map
Normal file
File diff suppressed because one or more lines are too long
43
examples/js/class-output.js
Normal file
43
examples/js/class-output.js
Normal file
@ -0,0 +1,43 @@
|
||||
var predict_div = $("#predict_div").val();
|
||||
|
||||
function notifyError(error) {
|
||||
$.notify({
|
||||
// options
|
||||
message: 'Not able to communicate with model (is python code still running?)'
|
||||
},{
|
||||
// settings
|
||||
type: 'danger',
|
||||
animate: {
|
||||
enter: 'animated fadeInDown',
|
||||
exit: 'animated fadeOutUp'
|
||||
},
|
||||
placement: {
|
||||
from: "bottom",
|
||||
align: "right"
|
||||
},
|
||||
delay: 5000
|
||||
|
||||
});
|
||||
}
|
||||
|
||||
try {
|
||||
ws.onerror = function(evt) {
|
||||
notifyError(evt)
|
||||
};
|
||||
|
||||
ws.onmessage = function (event) {
|
||||
console.log(event.data);
|
||||
sleep(300).then(() => {
|
||||
if (event.data.length == 1) {
|
||||
$("#predict_div").css({ 'font-size':'300px'});
|
||||
} else {
|
||||
$("#predict_div").css({ 'font-size':'60px'});
|
||||
}
|
||||
$("#predict_div").text(event.data);
|
||||
})
|
||||
|
||||
}
|
||||
} catch (e) {
|
||||
notifyError(e)
|
||||
}
|
||||
|
||||
128
examples/js/draw-a-digit.js
Normal file
128
examples/js/draw-a-digit.js
Normal file
@ -0,0 +1,128 @@
|
||||
var canvas = document.getElementById('canvas');
|
||||
context = canvas.getContext("2d");
|
||||
context.fillStyle = "black";
|
||||
context.fillRect(0, 0, 400, 400);
|
||||
|
||||
var predict_canvas = document.getElementById("predict_canvas");
|
||||
var ctx = predict_canvas.getContext("2d");
|
||||
|
||||
const sleep = (milliseconds) => {
|
||||
return new Promise(resolve => setTimeout(resolve, milliseconds))
|
||||
}
|
||||
|
||||
function notifyError(error) {
|
||||
$.notify({
|
||||
// options
|
||||
message: 'Not able to communicate with model (is python code still running?)'
|
||||
},{
|
||||
// settings
|
||||
type: 'danger',
|
||||
animate: {
|
||||
enter: 'animated fadeInDown',
|
||||
exit: 'animated fadeOutUp'
|
||||
},
|
||||
placement: {
|
||||
from: "bottom",
|
||||
align: "right"
|
||||
},
|
||||
delay: 5000
|
||||
|
||||
});
|
||||
}
|
||||
|
||||
try {
|
||||
var ws = new WebSocket("ws://127.0.0.1:5679/")
|
||||
ws.onerror = function(evt) {
|
||||
notifyError(evt)
|
||||
};
|
||||
|
||||
ws.onmessage = function (event) {
|
||||
console.log(event.data);
|
||||
ctx.clearRect(0, 0, context.canvas.width, context.canvas.height); // Clears the canvas
|
||||
ctx.font = "330px Arial";
|
||||
ctx.fillStyle = "white";
|
||||
sleep(300).then(() => {
|
||||
ctx.fillText(event.data, 110, 310);
|
||||
})
|
||||
|
||||
}
|
||||
} catch (e) {
|
||||
notifyError(e)
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
$('#canvas').mousedown(function(e){
|
||||
var mouseX = e.pageX - this.getBoundingClientRect().left + document.documentElement.scrollLeft;
|
||||
var mouseY = e.pageY - this.getBoundingClientRect().top + document.documentElement.scrollTop
|
||||
;
|
||||
|
||||
paint = true;
|
||||
addClick(mouseX, mouseY);
|
||||
redraw();
|
||||
});
|
||||
$('#canvas').mousemove(function(e){
|
||||
if(paint){
|
||||
addClick(e.pageX - this.getBoundingClientRect().left + document.documentElement.scrollLeft, e.pageY - this.getBoundingClientRect().top + document.documentElement.scrollTop, true);
|
||||
redraw();
|
||||
}
|
||||
});
|
||||
$('#canvas').mouseup(function(e){
|
||||
paint = false;
|
||||
});
|
||||
$('#canvas').mouseleave(function(e){
|
||||
paint = false;
|
||||
});
|
||||
var clickX = new Array();
|
||||
var clickY = new Array();
|
||||
var clickDrag = new Array();
|
||||
var paint;
|
||||
|
||||
function addClick(x, y, dragging)
|
||||
{
|
||||
clickX.push(x);
|
||||
clickY.push(y);
|
||||
clickDrag.push(dragging);
|
||||
}
|
||||
function redraw(){
|
||||
context.clearRect(0, 0, context.canvas.width, context.canvas.height); // Clears the canvas
|
||||
context.fillStyle = "black";
|
||||
context.fillRect(0, 0, 400, 400);
|
||||
|
||||
context.strokeStyle = "#FFF";
|
||||
context.lineJoin = "round";
|
||||
context.lineWidth = 25;
|
||||
|
||||
for(var i=0; i < clickX.length; i++) {
|
||||
context.beginPath();
|
||||
if(clickDrag[i] && i){
|
||||
context.moveTo(clickX[i-1], clickY[i-1]);
|
||||
}else{
|
||||
context.moveTo(clickX[i]-1, clickY[i]);
|
||||
}
|
||||
context.lineTo(clickX[i], clickY[i]);
|
||||
context.closePath();
|
||||
context.stroke();
|
||||
}
|
||||
}
|
||||
|
||||
$('#clear-button').click(function(e){
|
||||
context.clearRect(0, 0, context.canvas.width, context.canvas.height); // Clears the canvas
|
||||
clickX = new Array();
|
||||
clickY = new Array();
|
||||
clickDrag = new Array();
|
||||
context.fillStyle = "black";
|
||||
context.fillRect(0, 0, 400, 400);
|
||||
ctx.clearRect(0, 0, context.canvas.width, context.canvas.height); // Clears the canvas
|
||||
})
|
||||
|
||||
|
||||
$('#submit-button').click(function(e){
|
||||
var dataURL = canvas.toDataURL("image/png");
|
||||
ws.send(dataURL, function(e){
|
||||
notifyError(e)
|
||||
});
|
||||
|
||||
})
|
||||
|
||||
3530
examples/js/dropzone.js
Normal file
3530
examples/js/dropzone.js
Normal file
File diff suppressed because it is too large
Load Diff
180
examples/js/emotion-detector.js
Normal file
180
examples/js/emotion-detector.js
Normal file
@ -0,0 +1,180 @@
|
||||
videoWidth = 400;
|
||||
videoHeight = 400;
|
||||
|
||||
function isAndroid() {
|
||||
return /Android/i.test(navigator.userAgent);
|
||||
}
|
||||
|
||||
function isiOS() {
|
||||
return /iPhone|iPad|iPod/i.test(navigator.userAgent);
|
||||
}
|
||||
|
||||
function isMobile() {
|
||||
return isAndroid() || isiOS();
|
||||
}
|
||||
|
||||
var predict_canvas = document.getElementById("predict_canvas");
|
||||
var predict_ctx = predict_canvas.getContext("2d");
|
||||
var canvas = document.getElementById("canvas");
|
||||
var ctx = canvas.getContext("2d");
|
||||
|
||||
|
||||
const sleep = (milliseconds) => {
|
||||
return new Promise(resolve => setTimeout(resolve, milliseconds))
|
||||
}
|
||||
|
||||
async function setupCamera() {
|
||||
if (!navigator.mediaDevices || !navigator.mediaDevices.getUserMedia) {
|
||||
throw new Error(
|
||||
'Browser API navigator.mediaDevices.getUserMedia not available');
|
||||
}
|
||||
|
||||
const video = document.getElementById('video');
|
||||
video.width = videoWidth;
|
||||
video.height = videoHeight;
|
||||
|
||||
|
||||
const mobile = isMobile();
|
||||
const stream = await navigator.mediaDevices.getUserMedia({
|
||||
'audio': false,
|
||||
'video': {
|
||||
facingMode: 'user',
|
||||
width: mobile ? undefined : videoWidth,
|
||||
height: mobile ? undefined : videoHeight,
|
||||
},
|
||||
});
|
||||
|
||||
video.srcObject = stream;
|
||||
|
||||
return new Promise((resolve) => {
|
||||
video.onloadedmetadata = () => {
|
||||
resolve(video);
|
||||
};
|
||||
});
|
||||
|
||||
}
|
||||
|
||||
async function loadVideo() {
|
||||
const video = await setupCamera();
|
||||
video.play();
|
||||
|
||||
return video;
|
||||
}
|
||||
|
||||
function detectPoseInRealTime(video) {
|
||||
const flipHorizontal = true;
|
||||
async function poseDetectionFrame() {
|
||||
|
||||
ctx.clearRect(0, 0, videoWidth, videoHeight);
|
||||
|
||||
ctx.save();
|
||||
ctx.scale(-1, 1);
|
||||
ctx.translate(-videoWidth, 0);
|
||||
ctx.drawImage(video, 0, 0, videoWidth, videoHeight);
|
||||
ctx.restore();
|
||||
|
||||
requestAnimationFrame(poseDetectionFrame);
|
||||
|
||||
}
|
||||
|
||||
poseDetectionFrame();
|
||||
}
|
||||
|
||||
async function bindPage() {
|
||||
|
||||
let video;
|
||||
|
||||
try {
|
||||
video = await loadVideo();
|
||||
} catch (e) {
|
||||
let info = document.getElementById('info');
|
||||
info.textContent = 'this browser does not support video capture,' +
|
||||
'or this device does not have a camera';
|
||||
info.style.display = 'block';
|
||||
throw e;
|
||||
}
|
||||
|
||||
detectPoseInRealTime(video);
|
||||
|
||||
}
|
||||
|
||||
navigator.getUserMedia = navigator.getUserMedia ||
|
||||
navigator.webkitGetUserMedia || navigator.mozGetUserMedia;
|
||||
// kick off the demo
|
||||
bindPage();
|
||||
|
||||
|
||||
function notifyError(error) {
|
||||
$.notify({
|
||||
// options
|
||||
message: 'Not able to communicate with model (is python code still running?)'
|
||||
},{
|
||||
// settings
|
||||
type: 'danger',
|
||||
animate: {
|
||||
enter: 'animated fadeInDown',
|
||||
exit: 'animated fadeOutUp'
|
||||
},
|
||||
placement: {
|
||||
from: "bottom",
|
||||
align: "right"
|
||||
},
|
||||
delay: 5000
|
||||
|
||||
});
|
||||
}
|
||||
|
||||
try {
|
||||
var ws = new WebSocket("ws://127.0.0.1:5679/")
|
||||
ws.onerror = function(evt) {
|
||||
notifyError(evt)
|
||||
};
|
||||
|
||||
ws.onmessage = function (event) {
|
||||
var emotion_dict = {0: "Angry", 1: "Disgust", 2: "Fear", 3: "Happy", 4: "Sad", 5: "Surprise", 6: "Neutral"}
|
||||
console.log(event.data);
|
||||
predict_ctx.clearRect(0, 0, ctx.canvas.width, ctx.canvas.height); // Clears the canvas
|
||||
predict_ctx.font = "60px Arial";
|
||||
predict_ctx.fillStyle = "white";
|
||||
sleep(300).then(() => {
|
||||
// predict_ctx.fillText(emotion_dict[event.data], 110, 310);
|
||||
predict_ctx.textAlign = "center";
|
||||
predict_ctx.fillText(emotion_dict[event.data], 200, 200);
|
||||
})
|
||||
|
||||
}
|
||||
} catch (e) {
|
||||
notifyError(e)
|
||||
}
|
||||
|
||||
$('#clear-button').click(function(e){
|
||||
context.clearRect(0, 0, context.canvas.width, context.canvas.height); // Clears the canvas
|
||||
clickX = new Array();
|
||||
clickY = new Array();
|
||||
clickDrag = new Array();
|
||||
context.fillStyle = "black";
|
||||
context.fillRect(0, 0, 400, 400);
|
||||
ctx.clearRect(0, 0, context.canvas.width, context.canvas.height); // Clears the canvas
|
||||
})
|
||||
|
||||
|
||||
$('#submit-button').click(function(e){
|
||||
var dataURL = canvas.toDataURL("image/png");
|
||||
ws.send(dataURL, function(e){
|
||||
notifyError(e)
|
||||
});
|
||||
|
||||
})
|
||||
|
||||
$('#capture-button').click(function(e){
|
||||
ctx.clearRect(0, 0, videoWidth, videoHeight);
|
||||
ctx.save();
|
||||
ctx.scale(-1, 1);
|
||||
ctx.translate(-videoWidth, 0);
|
||||
ctx.drawImage(video, 0, 0, videoWidth, videoHeight);
|
||||
ctx.restore();
|
||||
var dataURL = canvas.toDataURL("image/png");
|
||||
ws.send(dataURL, function(e){
|
||||
notifyError(e)
|
||||
});
|
||||
})
|
||||
19
examples/js/image-upload-input.js
Normal file
19
examples/js/image-upload-input.js
Normal file
@ -0,0 +1,19 @@
|
||||
|
||||
var imageLoader = document.getElementById('filePhoto');
|
||||
imageLoader.addEventListener('change', handleImage, false);
|
||||
|
||||
function handleImage(e) {
|
||||
var reader = new FileReader();
|
||||
reader.onload = function (event) {
|
||||
|
||||
$('.uploader img').attr('src',event.target.result);
|
||||
}
|
||||
reader.readAsDataURL(e.target.files[0]);
|
||||
}
|
||||
|
||||
$('#submit-button').click(function(e){
|
||||
var src = $('.uploader img').attr('src');
|
||||
ws.send(src, function(e){
|
||||
notifyError(e)
|
||||
});
|
||||
})
|
||||
2
examples/js/jquery-3.3.1.min.js
vendored
Normal file
2
examples/js/jquery-3.3.1.min.js
vendored
Normal file
File diff suppressed because one or more lines are too long
100
examples/js/sketchpad-input.js
Normal file
100
examples/js/sketchpad-input.js
Normal file
@ -0,0 +1,100 @@
|
||||
var canvas = document.getElementById('canvas');
|
||||
context = canvas.getContext("2d");
|
||||
context.fillStyle = "black";
|
||||
context.fillRect(0, 0, 400, 400);
|
||||
|
||||
|
||||
function notifyError(error) {
|
||||
$.notify({
|
||||
// options
|
||||
message: 'Not able to communicate with model (is python code still running?)'
|
||||
},{
|
||||
// settings
|
||||
type: 'danger',
|
||||
animate: {
|
||||
enter: 'animated fadeInDown',
|
||||
exit: 'animated fadeOutUp'
|
||||
},
|
||||
placement: {
|
||||
from: "bottom",
|
||||
align: "right"
|
||||
},
|
||||
delay: 5000
|
||||
|
||||
});
|
||||
}
|
||||
|
||||
|
||||
$('#canvas').mousedown(function(e){
|
||||
var mouseX = e.pageX - this.getBoundingClientRect().left + document.documentElement.scrollLeft;
|
||||
var mouseY = e.pageY - this.getBoundingClientRect().top + document.documentElement.scrollTop
|
||||
;
|
||||
|
||||
paint = true;
|
||||
addClick(mouseX, mouseY);
|
||||
redraw();
|
||||
});
|
||||
$('#canvas').mousemove(function(e){
|
||||
if(paint){
|
||||
addClick(e.pageX - this.getBoundingClientRect().left + document.documentElement.scrollLeft, e.pageY - this.getBoundingClientRect().top + document.documentElement.scrollTop, true);
|
||||
redraw();
|
||||
}
|
||||
});
|
||||
$('#canvas').mouseup(function(e){
|
||||
paint = false;
|
||||
});
|
||||
$('#canvas').mouseleave(function(e){
|
||||
paint = false;
|
||||
});
|
||||
var clickX = new Array();
|
||||
var clickY = new Array();
|
||||
var clickDrag = new Array();
|
||||
var paint;
|
||||
|
||||
function addClick(x, y, dragging)
|
||||
{
|
||||
clickX.push(x);
|
||||
clickY.push(y);
|
||||
clickDrag.push(dragging);
|
||||
}
|
||||
function redraw(){
|
||||
context.clearRect(0, 0, context.canvas.width, context.canvas.height); // Clears the canvas
|
||||
context.fillStyle = "black";
|
||||
context.fillRect(0, 0, 400, 400);
|
||||
|
||||
context.strokeStyle = "#FFF";
|
||||
context.lineJoin = "round";
|
||||
context.lineWidth = 25;
|
||||
|
||||
for(var i=0; i < clickX.length; i++) {
|
||||
context.beginPath();
|
||||
if(clickDrag[i] && i){
|
||||
context.moveTo(clickX[i-1], clickY[i-1]);
|
||||
}else{
|
||||
context.moveTo(clickX[i]-1, clickY[i]);
|
||||
}
|
||||
context.lineTo(clickX[i], clickY[i]);
|
||||
context.closePath();
|
||||
context.stroke();
|
||||
}
|
||||
}
|
||||
|
||||
$('#clear-button').click(function(e){
|
||||
context.clearRect(0, 0, context.canvas.width, context.canvas.height); // Clears the canvas
|
||||
clickX = new Array();
|
||||
clickY = new Array();
|
||||
clickDrag = new Array();
|
||||
context.fillStyle = "black";
|
||||
context.fillRect(0, 0, 400, 400);
|
||||
ctx.clearRect(0, 0, context.canvas.width, context.canvas.height); // Clears the canvas
|
||||
})
|
||||
|
||||
|
||||
$('#submit-button').click(function(e){
|
||||
var dataURL = canvas.toDataURL("image/png");
|
||||
ws.send(dataURL, function(e){
|
||||
notifyError(e)
|
||||
});
|
||||
|
||||
})
|
||||
|
||||
18
examples/js/textbox-input.js
Normal file
18
examples/js/textbox-input.js
Normal file
@ -0,0 +1,18 @@
|
||||
var text = $("#textbox-input").val();
|
||||
|
||||
|
||||
|
||||
$('#clear-button').click(function(e){
|
||||
document.getElementById("textbox-input").value="";
|
||||
})
|
||||
|
||||
|
||||
$('#submit-button').click(function(e){
|
||||
// var dataURL = canvas.toDataURL("image/png");
|
||||
var text = $("#textbox-input").val();
|
||||
console.log(text);
|
||||
ws.send(text, function(e){
|
||||
notifyError(e)
|
||||
});
|
||||
|
||||
})
|
||||
36
examples/js/textbox-output.js
Normal file
36
examples/js/textbox-output.js
Normal file
@ -0,0 +1,36 @@
|
||||
|
||||
function notifyError(error) {
|
||||
$.notify({
|
||||
// options
|
||||
message: 'Not able to communicate with model (is python code still running?)'
|
||||
},{
|
||||
// settings
|
||||
type: 'danger',
|
||||
animate: {
|
||||
enter: 'animated fadeInDown',
|
||||
exit: 'animated fadeOutUp'
|
||||
},
|
||||
placement: {
|
||||
from: "bottom",
|
||||
align: "right"
|
||||
},
|
||||
delay: 5000
|
||||
|
||||
});
|
||||
}
|
||||
|
||||
try {
|
||||
ws.onerror = function(evt) {
|
||||
notifyError(evt)
|
||||
};
|
||||
|
||||
ws.onmessage = function (event) {
|
||||
console.log(event.data);
|
||||
sleep(300).then(() => {
|
||||
$("#textbox-output").val(event.data);
|
||||
})
|
||||
|
||||
}
|
||||
} catch (e) {
|
||||
notifyError(e)
|
||||
}
|
||||
152
examples/js/webcam-input.js
Normal file
152
examples/js/webcam-input.js
Normal file
@ -0,0 +1,152 @@
|
||||
videoWidth = 400;
|
||||
videoHeight = 400;
|
||||
|
||||
function isAndroid() {
|
||||
return /Android/i.test(navigator.userAgent);
|
||||
}
|
||||
|
||||
function isiOS() {
|
||||
return /iPhone|iPad|iPod/i.test(navigator.userAgent);
|
||||
}
|
||||
|
||||
function isMobile() {
|
||||
return isAndroid() || isiOS();
|
||||
}
|
||||
|
||||
var canvas = document.getElementById("canvas");
|
||||
var ctx = canvas.getContext("2d");
|
||||
|
||||
|
||||
async function setupCamera() {
|
||||
if (!navigator.mediaDevices || !navigator.mediaDevices.getUserMedia) {
|
||||
throw new Error(
|
||||
'Browser API navigator.mediaDevices.getUserMedia not available');
|
||||
}
|
||||
|
||||
const video = document.getElementById('video');
|
||||
video.width = videoWidth;
|
||||
video.height = videoHeight;
|
||||
|
||||
|
||||
const mobile = isMobile();
|
||||
const stream = await navigator.mediaDevices.getUserMedia({
|
||||
'audio': false,
|
||||
'video': {
|
||||
facingMode: 'user',
|
||||
width: mobile ? undefined : videoWidth,
|
||||
height: mobile ? undefined : videoHeight,
|
||||
},
|
||||
});
|
||||
|
||||
video.srcObject = stream;
|
||||
|
||||
return new Promise((resolve) => {
|
||||
video.onloadedmetadata = () => {
|
||||
resolve(video);
|
||||
};
|
||||
});
|
||||
|
||||
}
|
||||
|
||||
async function loadVideo() {
|
||||
const video = await setupCamera();
|
||||
video.play();
|
||||
|
||||
return video;
|
||||
}
|
||||
|
||||
function detectPoseInRealTime(video) {
|
||||
const flipHorizontal = true;
|
||||
async function poseDetectionFrame() {
|
||||
|
||||
ctx.clearRect(0, 0, videoWidth, videoHeight);
|
||||
|
||||
ctx.save();
|
||||
ctx.scale(-1, 1);
|
||||
ctx.translate(-videoWidth, 0);
|
||||
ctx.drawImage(video, 0, 0, videoWidth, videoHeight);
|
||||
ctx.restore();
|
||||
|
||||
requestAnimationFrame(poseDetectionFrame);
|
||||
|
||||
}
|
||||
|
||||
poseDetectionFrame();
|
||||
}
|
||||
|
||||
async function bindPage() {
|
||||
|
||||
let video;
|
||||
|
||||
try {
|
||||
video = await loadVideo();
|
||||
} catch (e) {
|
||||
let info = document.getElementById('info');
|
||||
info.textContent = 'this browser does not support video capture,' +
|
||||
'or this device does not have a camera';
|
||||
info.style.display = 'block';
|
||||
throw e;
|
||||
}
|
||||
|
||||
detectPoseInRealTime(video);
|
||||
|
||||
}
|
||||
|
||||
navigator.getUserMedia = navigator.getUserMedia ||
|
||||
navigator.webkitGetUserMedia || navigator.mozGetUserMedia;
|
||||
// kick off the demo
|
||||
bindPage();
|
||||
|
||||
|
||||
function notifyError(error) {
|
||||
$.notify({
|
||||
// options
|
||||
message: 'Not able to communicate with model (is python code still running?)'
|
||||
},{
|
||||
// settings
|
||||
type: 'danger',
|
||||
animate: {
|
||||
enter: 'animated fadeInDown',
|
||||
exit: 'animated fadeOutUp'
|
||||
},
|
||||
placement: {
|
||||
from: "bottom",
|
||||
align: "right"
|
||||
},
|
||||
delay: 5000
|
||||
|
||||
});
|
||||
}
|
||||
|
||||
|
||||
$('#clear-button').click(function(e){
|
||||
context.clearRect(0, 0, context.canvas.width, context.canvas.height); // Clears the canvas
|
||||
clickX = new Array();
|
||||
clickY = new Array();
|
||||
clickDrag = new Array();
|
||||
context.fillStyle = "black";
|
||||
context.fillRect(0, 0, 400, 400);
|
||||
ctx.clearRect(0, 0, context.canvas.width, context.canvas.height); // Clears the canvas
|
||||
})
|
||||
|
||||
|
||||
$('#submit-button').click(function(e){
|
||||
var dataURL = canvas.toDataURL("image/png");
|
||||
ws.send(dataURL, function(e){
|
||||
notifyError(e)
|
||||
});
|
||||
|
||||
})
|
||||
|
||||
$('#capture-button').click(function(e){
|
||||
ctx.clearRect(0, 0, videoWidth, videoHeight);
|
||||
ctx.save();
|
||||
ctx.scale(-1, 1);
|
||||
ctx.translate(-videoWidth, 0);
|
||||
ctx.drawImage(video, 0, 0, videoWidth, videoHeight);
|
||||
ctx.restore();
|
||||
var dataURL = canvas.toDataURL("image/png");
|
||||
ws.send(dataURL, function(e){
|
||||
notifyError(e)
|
||||
});
|
||||
})
|
||||
@ -1,6 +1,6 @@
|
||||
Metadata-Version: 1.0
|
||||
Name: gradio
|
||||
Version: 0.1.6
|
||||
Version: 0.1.7
|
||||
Summary: Python library for easily interacting with trained machine learning models
|
||||
Home-page: https://github.com/abidlabs/gradio
|
||||
Author: Abubakar Abid
|
||||
|
||||
BIN
screenshots/mnist4.png
Normal file
BIN
screenshots/mnist4.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 90 KiB |
BIN
screenshots/mnist5.png
Normal file
BIN
screenshots/mnist5.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 103 KiB |
BIN
screenshots/sentiment_positive.png
Normal file
BIN
screenshots/sentiment_positive.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 65 KiB |
Loading…
x
Reference in New Issue
Block a user