def interface_params_from_config(config_dict):
## instantiate input component and output component
config_dict["inputs"] = [inputs.get_input_instance(component) for component in config_dict["input_components"]]
config_dict["outputs"] = [outputs.get_output_instance(component) for component in config_dict["output_components"]]
parameters = {
"allow_flagging", "allow_screenshot", "article", "description", "flagging_options", "inputs", "outputs",
"show_input", "show_output", "theme", "title"
}
config_dict = {k: config_dict[k] for k in parameters}
return config_dict
def __init__(self,
fn,
inputs=None,
outputs=None,
verbose=False,
examples=None,
examples_per_page=10,
live=False,
layout="horizontal",
show_input=True,
show_output=True,
capture_session=False,
interpretation=None,
num_shap=2.0,
theme=None,
repeat_outputs_per_model=True,
title=None,
description=None,
article=None,
thumbnail=None,
css=None,
server_port=None,
server_name=networking.LOCALHOST_NAME,
height=500,
width=900,
allow_screenshot=True,
allow_flagging=True,
flagging_options=None,
encrypt=False,
show_tips=False,
embedding=None,
flagging_dir="flagged",
analytics_enabled=True):
"""
Parameters:
fn (Callable): the function to wrap an interface around.
inputs (Union[str, List[Union[str, InputComponent]]]): a single Gradio input component, or list of Gradio input components. Components can either be passed as instantiated objects, or referred to by their string shortcuts. The number of input components should match the number of parameters in fn.
outputs (Union[str, List[Union[str, OutputComponent]]]): a single Gradio output component, or list of Gradio output components. Components can either be passed as instantiated objects, or referred to by their string shortcuts. The number of output components should match the number of values returned by fn.
verbose (bool): whether to print detailed information during launch.
examples (Union[List[List[Any]], str]): sample inputs for the function; if provided, appears below the UI components and can be used to populate the interface. Should be nested list, in which the outer list consists of samples and each inner list consists of an input corresponding to each input component. A string path to a directory of examples can also be provided. If there are multiple input components, a log.csv file must be present in the directory to link corresponding inputs.
examples_per_page (int): If examples are provided, how many to display per page.
live (bool): whether the interface should automatically reload on change.
layout (str): Layout of input and output panels. "horizontal" arranges them as two columns of equal height, "unaligned" arranges them as two columns of unequal height, and "vertical" arranges them vertically.
capture_session (bool): if True, captures the default graph and session (needed for Tensorflow 1.x)
interpretation (Union[Callable, str]): function that provides interpretation explaining prediction output. Pass "default" to use built-in interpreter.
num_shap (float): a multiplier that determines how many examples are computed for shap-based interpretation. Increasing this value will increase shap runtime, but improve results.
title (str): a title for the interface; if provided, appears above the input and output components.
description (str): a description for the interface; if provided, appears above the input and output components.
article (str): an expanded article explaining the interface; if provided, appears below the input and output components. Accepts Markdown and HTML content.
thumbnail (str): path to image or src to use as display picture for models listed in gradio.app/hub
theme (str): Theme to use - one of "default", "compact" or "huggingface".
css (str): custom css or path to custom css file to use with interface.
server_port (int): will start gradio app on this port (if available)
server_name (str): to make app accessible on local network set to "0.0.0.0".
allow_screenshot (bool): if False, users will not see a button to take a screenshot of the interface.
allow_flagging (bool): if False, users will not see a button to flag an input and output.
flagging_options (List[str]): if not None, provides options a user must select when flagging.
encrypt (bool): If True, flagged data will be encrypted by key provided by creator at launch
flagging_dir (str): what to name the dir where flagged data is stored.
show_tips (bool): if True, will occasionally show tips about new Gradio features
"""
if not isinstance(fn, list):
fn = [fn]
if isinstance(inputs, list):
self.input_components = [get_input_instance(i) for i in inputs]
else:
self.input_components = [get_input_instance(inputs)]
if isinstance(outputs, list):
self.output_components = [get_output_instance(i) for i in outputs]
else:
self.output_components = [get_output_instance(outputs)]
if repeat_outputs_per_model:
self.output_components *= len(fn)
self.predict = fn
self.function_names = [func.__name__ for func in fn]
self.__name__ = ", ".join(self.function_names)
self.verbose = verbose
self.status = "OFF"
self.live = live
self.layout = layout
self.show_input = show_input
self.show_output = show_output
self.flag_hash = random.getrandbits(32)
self.capture_session = capture_session
self.interpretation = interpretation
self.session = None
self.server_name = server_name
self.title = title
self.description = description
if article is not None:
article = utils.readme_to_html(article)
article = markdown2.markdown(article)
self.article = article
self.thumbnail = thumbnail
self.theme = theme if theme is not None else os.getenv(
"GRADIO_THEME", "default")
self.height = height
self.width = width
if css is not None and os.path.exists(css):
with open(css) as css_file:
self.css = css_file.read()
else:
self.css = css
if examples is None or isinstance(examples, str) or (
isinstance(examples, list) and
(len(examples) == 0 or isinstance(examples[0], list))):
self.examples = examples
else:
raise ValueError(
"Examples argument must either be a directory or a nested list, where each sublist represents a set of inputs."
)
self.num_shap = num_shap
self.examples_per_page = examples_per_page
self.server_port = server_port
self.simple_server = None
self.allow_screenshot = allow_screenshot
self.allow_flagging = os.getenv("GRADIO_FLAGGING") or allow_flagging
self.flagging_options = flagging_options
self.flagging_dir = flagging_dir
self.encrypt = encrypt
Interface.instances.add(self)
self.analytics_enabled = analytics_enabled
self.save_to = None
self.share = None
self.share_url = None
self.local_url = None
self.embedding = embedding
self.show_tips = show_tips
self.requires_permissions = any([
component.requires_permissions
for component in self.input_components
])
data = {
'fn': fn,
'inputs': inputs,
'outputs': outputs,
'live': live,
'capture_session': capture_session,
'ip_address': ip_address,
'interpretation': interpretation,
'embedding': embedding,
'allow_flagging': allow_flagging,
'allow_screenshot': allow_screenshot,
}
if self.capture_session:
try:
import tensorflow as tf
self.session = tf.get_default_graph(), \
tf.keras.backend.get_session()
except (ImportError, AttributeError):
# If they are using TF >= 2.0 or don't have TF,
# just ignore this.
pass
if self.allow_flagging:
os.makedirs(self.flagging_dir, exist_ok=True)
if self.analytics_enabled:
try:
requests.post(analytics_url + 'gradio-initiated-analytics/',
data=data,
timeout=3)
except (requests.ConnectionError, requests.exceptions.ReadTimeout):
pass # do not push analytics if no network