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https://github.com/gradio-app/gradio.git
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upgraded to 0.9.3
This commit is contained in:
Abubakar Abid
parent
595b8f59d3
commit
9e0e1dc383
@ -7,7 +7,7 @@ automatically added to a registry, which allows them to be easily referenced in
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from abc import ABC, abstractmethod
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from gradio import preprocessing_utils, validation_data
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import numpy as np
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from PIL import Image, ImageOps
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import PIL.Image, PIL.ImageOps
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import time
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import warnings
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import json
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@ -84,11 +84,11 @@ class Sketchpad(AbstractInput):
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Default preprocessing method for the SketchPad is to convert the sketch to black and white and resize 28x28
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"""
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im_transparent = preprocessing_utils.decode_base64_to_image(inp)
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im = Image.new("RGBA", im_transparent.size, "WHITE") # Create a white background for the alpha channel
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im = PIL.Image.new("RGBA", im_transparent.size, "WHITE") # Create a white background for the alpha channel
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im.paste(im_transparent, (0, 0), im_transparent)
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im = im.convert('L')
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if self.invert_colors:
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im = ImageOps.invert(im)
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im = PIL.ImageOps.invert(im)
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im = im.resize((self.image_width, self.image_height))
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if self.flatten:
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array = np.array(im).flatten().reshape(1, self.image_width * self.image_height)
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@ -261,6 +261,7 @@ class Slider(AbstractInput):
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"checkbox": {},
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}
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class Checkbox(AbstractInput):
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def __init__(self, label=None):
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super().__init__(label)
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@ -272,7 +273,7 @@ class Checkbox(AbstractInput):
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}
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class ImageIn(AbstractInput):
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class Image(AbstractInput):
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def __init__(self, cast_to=None, shape=(224, 224, 3), image_mode='RGB',
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scale=1/127.5, shift=-1, cropper_aspect_ratio=None, label=None):
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self.cast_to = cast_to
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@ -16,7 +16,6 @@ import requests
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import random
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import time
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from IPython import get_ipython
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import tensorflow as tf
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LOCALHOST_IP = "0.0.0.0"
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TRY_NUM_PORTS = 100
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@ -31,7 +30,7 @@ class Interface:
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def __init__(self, fn, inputs, outputs, saliency=None, verbose=False,
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live=False, show_input=True, show_output=True,
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load_fn=None, capture_session=False,
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load_fn=None, capture_session=False, title=None, description=None,
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server_name=LOCALHOST_IP):
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"""
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:param fn: a function that will process the input panel data from the interface and return the output panel data.
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@ -81,6 +80,8 @@ class Interface:
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self.capture_session = capture_session
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self.session = None
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self.server_name = server_name
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self.title = title
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self.description = description
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def get_config_file(self):
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return {
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@ -93,7 +94,9 @@ class Interface:
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"function_count": len(self.predict),
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"live": self.live,
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"show_input": self.show_input,
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"show_output": self.show_output,
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"show_output": self.show_output,
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"title": self.title,
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"description": self.description,
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}
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def process(self, raw_input):
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@ -109,8 +112,15 @@ class Interface:
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prediction = predict_fn(*processed_input,
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self.context)
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else:
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prediction = predict_fn(*processed_input,
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self.context)
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try:
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prediction = predict_fn(*processed_input, self.context)
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except ValueError:
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print("It looks like you might be "
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"using tensorflow < 2.0. Please pass "
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"capture_session=True in Interface to avoid "
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"a 'Tensor is not an element of this graph.' "
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"error.")
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prediction = predict_fn(*processed_input, self.context)
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else:
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if self.capture_session:
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graph, sess = self.session
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@ -118,7 +128,16 @@ class Interface:
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with sess.as_default():
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prediction = predict_fn(*processed_input)
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else:
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prediction = predict_fn(*processed_input)
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try:
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prediction = predict_fn(*processed_input)
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except ValueError:
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print("It looks like you might be "
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"using tensorflow < 2.0. Please pass "
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"capture_session=True in Interface to avoid "
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"a 'Tensor is not an element of this graph.' "
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"error.")
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prediction = predict_fn(*processed_input)
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if len(self.output_interfaces) / \
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len(self.predict) == 1:
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prediction = [prediction]
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@ -127,7 +146,6 @@ class Interface:
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predictions[i]) for i, output_interface in enumerate(self.output_interfaces)]
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return processed_output
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def validate(self):
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if self.validate_flag:
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if self.verbose:
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@ -180,11 +198,7 @@ class Interface:
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return
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raise RuntimeError("Validation did not pass")
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<<<<<<< HEAD
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def launch(self, inline=None, inbrowser=None, share=False, validate=True, title=None, description=None):
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=======
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def launch(self, inline=None, inbrowser=None, share=False, validate=True):
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>>>>>>> 2bd16c2f9c360c98583b94e2f6a6ea7259a98217
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"""
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Standard method shared by interfaces that creates the interface and sets up a websocket to communicate with it.
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:param inline: boolean. If True, then a gradio interface is created inline (e.g. in jupyter or colab notebook)
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@ -198,6 +212,7 @@ class Interface:
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self.context = context
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if self.capture_session:
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import tensorflow as tf
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self.session = tf.get_default_graph(), \
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tf.keras.backend.get_session()
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@ -294,11 +309,6 @@ class Interface:
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config = self.get_config_file()
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config["share_url"] = share_url
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<<<<<<< HEAD
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config["title"] = title
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config["description"] = description
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=======
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>>>>>>> 2bd16c2f9c360c98583b94e2f6a6ea7259a98217
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networking.set_config(config, output_directory)
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return httpd, path_to_local_server, share_url
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@ -1,11 +1,7 @@
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function gradio(config, fn, target) {
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target = $(target);
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target.html(`
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<<<<<<< HEAD
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<div class="panels container">
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=======
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<div class="panels">
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>>>>>>> 2bd16c2f9c360c98583b94e2f6a6ea7259a98217
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<div class="panel input_panel">
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<div class="input_interfaces">
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</div>
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@ -30,7 +26,7 @@ function gradio(config, fn, target) {
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let input_to_object_map = {
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"csv" : {},
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"imagein" : image_input,
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"image" : image_input,
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"sketchpad" : sketchpad_input,
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"textbox" : textbox_input,
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"webcam" : webcam,
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BIN
dist/gradio-0.9.1-py3.6.egg
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dist/gradio-0.9.1-py3.6.egg
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@ -1,6 +1,6 @@
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Metadata-Version: 1.0
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Name: gradio
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Version: 0.9.2
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Version: 0.9.3
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Summary: Python library for easily interacting with trained machine learning models
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Home-page: https://github.com/abidlabs/gradio
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Author: Abubakar Abid
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