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Merge branch 'abidlabs/external' of https://github.com/gradio-app/gradio into abidlabs/external

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This commit is contained in:
dawoodkhan82 2021-05-07 04:29:41 -04:00
commit 1c14d2d2bd
5 changed files with 123 additions and 96 deletions
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2
gradio/__init__.py
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@ -1,6 +1,4 @@
from gradio.interface import * # This makes it possible to import `Interface` as `gradio.Interface`.
from gradio.external import load_interface
from gradio.compound import Parallel, Sequential
import pkg_resources
current_pkg_version = pkg_resources.require("gradio")[0].version

64
gradio/compound.py
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@ -1,64 +0,0 @@
from gradio.interface import Interface
class Parallel:
def __init__(self, interfaces):
fns = []
for io in interfaces:
fns.extend(io.predict)
self.interfaces = interfaces
self.predict = fns
self.compound_interface = Interface(
fn=fns, inputs=interfaces[0].input_interfaces, outputs=interfaces[0].output_interfaces)
def __str__(self):
return self.__repr__()
def __repr__(self):
repr = "Gradio Parallel Interface, consisting of:"
repr += "\n-----------------------------------------"
for i, io in enumerate(self.interfaces):
repr += "\n " + str(io).replace("\n", "\n ")
if i < len(self.interfaces) - 1: # Don't apply to last interface.
repr += "\n&"
return repr
def launch(self, *args, **kwargs):
return self.compound_interface.launch(*args, **kwargs)
class Sequential:
def __init__(self, interfaces):
fns = []
for io in interfaces:
fns.extend(io.predict)
def cascaded_fn(inp):
out = inp
for fn in fns:
out = fn(out)
return out
cascaded_fn.__name__ = " => ".join([f.__name__ for f in fns])
self.interfaces = interfaces
self.predict = [cascaded_fn]
self.input_interfaces = interfaces[0].input_interfaces
self.output_interfaces = interfaces[-1].output_interfaces
self.compound_interface = Interface(
fn=cascaded_fn, inputs=self.input_interfaces, outputs=self.output_interfaces)
def __str__(self):
return self.__repr__()
def __repr__(self):
repr = "Gradio Sequential Interface, consisting of:"
repr += "\n-----------------------------------------"
for i, io in enumerate(self.interfaces):
repr += "\n " + str(io).replace("\n", "\n ")
if i < len(self.interfaces) - 1: # Don't apply to last interface.
repr += "\n=>"
return repr
def launch(self, *args, **kwargs):
return self.compound_interface.launch(*args, **kwargs)

29
gradio/external.py
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@ -1,6 +1,5 @@
import json
import requests
from gradio.interface import Interface
from gradio import inputs, outputs
@ -23,40 +22,40 @@ def get_huggingface_interface(model_name, api_key):
},
'text-generation': {
'inputs': inputs.Textbox(label="Input"),
'outputs': outputs.Textbox(label="Question"),
'preprocess': lambda x: x,
'outputs': outputs.Textbox(label="Output"),
'preprocess': lambda x: {"inputs": x},
'postprocess': lambda r: r[0]["generated_text"],
# 'examples': [['My name is Clara and I am']]
},
'summarization': {
'inputs': inputs.Textbox(label="Input"),
'outputs': outputs.Textbox(label="Summary"),
'preprocess': lambda x: x,
'preprocess': lambda x: {"inputs": x},
'postprocess': lambda r: r[0]["summary_text"]
},
'translation': {
'inputs': inputs.Textbox(label="Input"),
'outputs': outputs.Textbox(label="Translation"),
'preprocess': lambda x: x,
'preprocess': lambda x: {"inputs": x},
'postprocess': lambda r: r[0]["translation_text"]
},
'text2text-generation': {
'inputs': inputs.Textbox(label="Input"),
'outputs': outputs.Textbox(label="Generated Text"),
'preprocess': lambda x: x,
'preprocess': lambda x: {"inputs": x},
'postprocess': lambda r: r[0]["generated_text"]
},
'text-classification': {
'inputs': inputs.Textbox(label="Input"),
'outputs': "label",
'preprocess': lambda x: x,
'outputs': outputs.Label(label="Class"),
'preprocess': lambda x: {"inputs": x},
'postprocess': lambda r: {'Negative': r[0][0]["score"],
'Positive': r[0][1]["score"]}
},
'fill-mask': {
'inputs': inputs.Textbox(label="Input"),
'outputs': "label",
'preprocess': lambda x: x,
'preprocess': lambda x: {"inputs": x},
'postprocess': lambda r: {i["token_str"]: i["score"] for i in r}
},
'zero-shot-classification': {
@ -79,6 +78,7 @@ def get_huggingface_interface(model_name, api_key):
def query_huggingface_api(*input):
payload = pipeline['preprocess'](*input)
payload.update({'options': {'wait_for_model': True}})
data = json.dumps(payload)
response = requests.request("POST", api_url, data=data)
result = json.loads(response.content.decode("utf-8"))
@ -127,10 +127,15 @@ def get_gradio_interface(model_name, api_key):
return interface_info
def load_interface(model, src, api_key=None, verbose=True):
def load_interface(name, src=None, api_key=None):
if src is None:
tokens = name.split("/")
assert len(tokens) > 1, "Either `src` parameter must be provided, or `name` must be formatted as \{src\}/\{repo name\}"
src = tokens[0]
name = "/".join(tokens[1:])
assert src.lower() in repos, "parameter: src must be one of {}".format(repos.keys())
interface_info = repos[src](model, api_key)
return Interface(**interface_info)
interface_info = repos[src](name, api_key)
return interface_info
repos = {
# for each repo, we have a method that returns the Interface given the model name & optionally an api_key

47
gradio/interface.py
View File

@ -8,6 +8,7 @@ from gradio.inputs import InputComponent
from gradio.outputs import OutputComponent
from gradio import networking, strings, utils
from gradio.interpretation import quantify_difference_in_label
from gradio.external import load_interface
from gradio import encryptor
import pkg_resources
import requests
@ -45,14 +46,20 @@ class Interface:
return list(
Interface.instances)
def __init__(self, fn, inputs, outputs, verbose=False, examples=None,
@classmethod
def load(cls, name, src=None, api_key=None, **kwargs):
interface_info = load_interface(name, src, api_key)
interface_info.update(kwargs)
return cls(**interface_info)
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,
capture_session=False, interpretation=None, repeat_outputs_per_model=True,
title=None, description=None, article=None, thumbnail=None,
css=None, server_port=None, server_name=networking.LOCALHOST_NAME,
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=True, embedding=None, flagging_dir="flagged", analytics_enabled=True):
show_tips=False, embedding=None, flagging_dir="flagged", analytics_enabled=True):
"""
Parameters:
@ -80,7 +87,6 @@ class Interface:
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
"""
def get_input_instance(iface):
if isinstance(iface, str):
shortcut = InputComponent.get_all_shortcut_implementations()[iface]
@ -103,6 +109,8 @@ class Interface:
"`OutputComponent`"
)
if not isinstance(fn, list):
fn = [fn]
if isinstance(inputs, list):
self.input_interfaces = [get_input_instance(i) for i in inputs]
else:
@ -111,12 +119,13 @@ class Interface:
self.output_interfaces = [get_output_instance(i) for i in outputs]
else:
self.output_interfaces = [get_output_instance(outputs)]
if not isinstance(fn, list):
fn = [fn]
self.output_interfaces *= len(fn)
# self.original_output_interfaces = copy.copy(self.output_interfaces)
if repeat_outputs_per_model:
self.output_interfaces *= 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
@ -135,6 +144,8 @@ class Interface:
article = markdown2.markdown(article)
self.article = article
self.thumbnail = thumbnail
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()
@ -192,12 +203,15 @@ class Interface:
data=data, timeout=3)
except (requests.ConnectionError, requests.exceptions.ReadTimeout):
pass # do not push analytics if no network
def __call__(self, params_per_function):
return self.predict[0](params_per_function)
def __str__(self):
return self.__repr__()
def __repr__(self):
repr = "Gradio interface for function: {}".format(",".join(fn.__name__ for fn in self.predict))
repr = "Gradio Interface for: {}".format(", ".join(fn.__name__ for fn in self.predict))
repr += "\n" + "-"*len(repr)
repr += "\ninputs:"
for component in self.input_interfaces:
@ -387,7 +401,7 @@ class Interface:
print("PASSED")
continue
def launch(self, inline=None, inbrowser=None, share=False, debug=False, auth=None, auth_message=None, private_endpoint=None, prevent_thread_lock=False):
def launch(self, inline=None, inbrowser=None, share=False, debug=False, auth=None, auth_message=None, private_endpoint=None):
"""
Parameters:
inline (bool): whether to display in the interface inline on python notebooks.
@ -477,9 +491,9 @@ class Interface:
if share:
while not networking.url_ok(share_url):
time.sleep(1)
display(IFrame(share_url, width=900, height=500))
display(IFrame(share_url, width=self.width, height=self.height))
else:
display(IFrame(path_to_local_server, width=900, height=500))
display(IFrame(path_to_local_server, width=self.width, height=self.height))
except ImportError:
pass # IPython is not available so does not print inline.
@ -493,12 +507,8 @@ class Interface:
while True:
sys.stdout.flush()
time.sleep(0.1)
is_in_interactive_mode = bool(getattr(sys, 'ps1', sys.flags.interactive))
if not prevent_thread_lock and not is_in_interactive_mode:
print("going to lock thread and run in foreground ...")
self.run_until_interrupted(thread, path_to_local_server)
return app, path_to_local_server, share_url, thread
return app, path_to_local_server, share_url
def integrate(self, comet_ml=None):
@ -508,6 +518,7 @@ class Interface:
else:
comet_ml.log_text(self.local_url)
comet_ml.end()
def show_tip(io):
if not(io.show_tips):

77
gradio/transforms.py Normal file
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@ -0,0 +1,77 @@
"""
Ways to transform interfaces to produce new interfaces
"""
from gradio.interface import Interface
def compare(*interfaces, **options):
fns = []
outputs = []
for io in interfaces:
fns.extend(io.predict)
outputs.extend(io.output_interfaces)
return Interface(fn=fns, inputs=interfaces[0].input_interfaces, outputs=outputs,
repeat_outputs_per_model=False, **options)
# class Compare:
# def __init__(self, interfaces):
# self.interfaces = interfaces
# self.predict = fns
# self.compound_interface = Interface(
# fn=fns, inputs=interfaces[0].input_interfaces, outputs=interfaces[0].output_interfaces)
# def __str__(self):
# return self.__repr__()
# def __repr__(self):
# repr = "Gradio Comparative Interface, consisting of:"
# repr += "\n-----------------------------------------"
# for i, io in enumerate(self.interfaces):
# repr += "\n " + str(io).replace("\n", "\n ")
# if i < len(self.interfaces) - 1: # Don't apply to last interface.
# repr += "\n&"
# return repr
# def launch(self, *args, **kwargs):
# return self.compound_interface.launch(*args, **kwargs)
def connect():
pass
# class Cascade:
# def __init__(self, interfaces):
# fns = []
# for io in interfaces:
# fns.extend(io.predict)
# def cascaded_process(inp):
# out = inp
# for fn in fns:
# out = fn(out)
# return out
# cascaded_fn.__name__ = " => ".join([f.__name__ for f in fns])
# self.interfaces = interfaces
# self.predict = [cascaded_fn]
# self.input_interfaces = interfaces[0].input_interfaces
# self.output_interfaces = interfaces[-1].output_interfaces
# self.compound_interface = Interface(
# fn=cascaded_fn, inputs=self.input_interfaces, outputs=self.output_interfaces)
# def __str__(self):
# return self.__repr__()
# def __repr__(self):
# repr = "Gradio Cascaded Interface, consisting of:"
# repr += "\n-----------------------------------------"
# for i, io in enumerate(self.interfaces):
# repr += "\n " + str(io).replace("\n", "\n ")
# if i < len(self.interfaces) - 1: # Don't apply to last interface.
# repr += "\n=>"
# return repr
# def launch(self, *args, **kwargs):
# return self.compound_interface.launch(*args, **kwargs)