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Merge branch 'master' of https://github.com/gradio-app/gradio-UI

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This commit is contained in:
Ali Abid 2020-07-09 14:23:22 -07:00
commit 031162c820
35 changed files with 101 additions and 8924 deletions
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2
MANIFEST.in
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@ -1,2 +1,2 @@
recursive-include gradio/templates *
recursive-include gradio/static *
recursive-include gradio/templates *

42
build/lib/gradio/interface.py
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@ -20,14 +20,15 @@ from IPython import get_ipython
import sys
import weakref
import analytics
import socket
PKG_VERSION_URL = "https://gradio.app/api/pkg-version"
analytics.write_key = "uxIFddIEuuUcFLf9VgH2teTEtPlWdkNy"
analytics_url = 'https://api.gradio.app/'
hostname = socket.gethostname()
ip_address = socket.gethostbyname(hostname)
try:
ip_address = requests.get('https://api.ipify.org').text
except requests.ConnectionError:
ip_address = "No internet connection"
class Interface:
@ -101,14 +102,22 @@ class Interface:
'saliency': saliency,
'live': live,
'capture_session': capture_session,
'host_name': hostname,
'ip_address': ip_address
}
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
try:
requests.post(analytics_url + 'gradio-initiated-analytics/',
data=data)
except requests.ConnectionError:
print("gradio-initiated-analytics/ Connection Error")
pass # do not push analytics if no network
def get_config_file(self):
config = {
@ -140,7 +149,6 @@ class Interface:
return config
def process(self, raw_input):
processed_input = [input_interface.preprocess(
raw_input[i]) for i, input_interface in
@ -210,7 +218,7 @@ class Interface:
requests.post(analytics_url + 'gradio-error-analytics/',
data=data)
except requests.ConnectionError:
print("gradio-error-analytics/ Connection Error")
pass # do not push analytics if no network
if self.verbose:
print("\n----------")
print(
@ -226,7 +234,7 @@ class Interface:
requests.post(analytics_url + 'gradio-error-analytics/',
data=data)
except requests.ConnectionError:
print("gradio-error-analytics/ Connection Error")
pass # do not push analytics if no network
if self.verbose:
print("\n----------")
print(
@ -258,14 +266,6 @@ class Interface:
# if validate and not self.validate_flag:
# self.validate()
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
output_directory = tempfile.mkdtemp()
# Set up a port to serve the directory containing the static files with interface.
server_port, httpd = networking.start_simple_server(self, output_directory, self.server_name)
@ -287,7 +287,7 @@ class Interface:
requests.post(analytics_url + 'gradio-error-analytics/',
data=data)
except requests.ConnectionError:
print("Connection Error")
pass # do not push analytics if no network
pass
try:
@ -321,7 +321,7 @@ class Interface:
requests.post(analytics_url + 'gradio-error-analytics/',
data=data)
except requests.ConnectionError:
print("Connection Error")
pass # do not push analytics if no network
share_url = None
if self.verbose:
print(strings.en["NGROK_NO_INTERNET"])
@ -363,6 +363,7 @@ class Interface:
is_colab
): # Embed the remote interface page if on google colab;
# otherwise, embed the local page.
print("Interface loading below...")
while not networking.url_ok(share_url):
time.sleep(1)
display(IFrame(share_url, width=1000, height=500))
@ -393,14 +394,13 @@ class Interface:
'is_google_colab': is_colab,
'is_sharing_on': share,
'share_url': share_url,
'host_name': hostname,
'ip_address': ip_address
}
try:
requests.post(analytics_url + 'gradio-hosted-launched-analytics/',
requests.post(analytics_url + 'gradio-launched-analytics/',
data=data)
except requests.ConnectionError:
print("Connection Error")
pass # do not push analytics if no network
return httpd, path_to_local_server, share_url
@classmethod

12
build/lib/gradio/networking.py
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@ -15,6 +15,7 @@ import urllib.request
from shutil import copyfile
import requests
import sys
import analytics
INITIAL_PORT_VALUE = (
@ -38,6 +39,8 @@ ASSOCIATION_PATH_IN_ROOT = "apple-app-site-association"
FLAGGING_DIRECTORY = 'static/flagged/'
FLAGGING_FILENAME = 'data.txt'
analytics.write_key = "uxIFddIEuuUcFLf9VgH2teTEtPlWdkNy"
analytics_url = 'https://api.gradio.app/'
def build_template(temp_dir):
@ -159,6 +162,15 @@ def serve_files_in_background(interface, port, directory_to_serve=None, server_n
# Prepare return json dictionary.
self.wfile.write(json.dumps(output).encode())
data = {'input_interface': interface.input_interfaces,
'output_interface': interface.output_interfaces,
}
try:
requests.post(
analytics_url + 'gradio-prediction-analytics/',
data=data)
except requests.ConnectionError:
pass # do not push analytics if no network
elif self.path == "/api/flag/":
self._set_headers()

1
build/lib/gradio/static/css/style.css
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@ -18,7 +18,6 @@ button, input[type="submit"], input[type="reset"], input[type="text"], input[typ
font-size: large;
color: #EEA45D;
text-align: right;
padding-top: 5px;
}
nav {

17
build/lib/gradio/templates/index.html
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@ -1,6 +1,21 @@
<html lang="en">
<head>
<!-- <iframe src=""></iframe> -->
<!-- Global site tag (gtag.js) - Google Analytics -->
<script async
src="https://www.googletagmanager.com/gtag/js?id=UA-156449732-1"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag() {
dataLayer.push(arguments);
}
gtag('js', new Date());
gtag('config', 'UA-156449732-1');
</script>
<!-- <iframe src=""></iframe> -->
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
<title>Gradio</title>
<link rel="stylesheet" href="../static/css/style.css">

2
gradio-0.9.9.5/MANIFEST.in
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@ -1,2 +0,0 @@
recursive-include gradio/static *
recursive-include gradio/templates *

168
gradio-0.9.9.5/README.md
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@ -1,168 +0,0 @@
[![CircleCI](https://circleci.com/gh/gradio-app/gradio-UI.svg?style=svg)](https://circleci.com/gh/gradio-app/gradio) [![PyPI version](https://badge.fury.io/py/gradio.svg)](https://badge.fury.io/py/gradio)
# Gradio UI
<img src="https://i.ibb.co/GHRk2JP/header-2.png" alt="drawing" width="1000"/>
At Gradio, we often try to understand what inputs a model is particularly sensitive to. To help facilitate this, we've developed and open-sourced `gradio`, a python library that allows you to quickly create input and output interfaces over trained models to make it easy for you to "play around" with your model in your browser by dragging-and-dropping in your own images (or pasting your own text, recording your own voice, etc.) and seeing what the model outputs. `gradio` can also generate a share link which allows anyone, anywhere to use the interface as the model continues to run on your machine.
Gradio is useful for:
* Creating demos of your machine learning code for clients / collaborators / users
* Getting feedback on model performance from users
* Debugging your model interactively during development
To get a sense of `gradio`, take a look at a few of these examples, and find more on our website: www.gradio.app.
## Installation
```
pip install gradio
```
(you may need to replace `pip` with `pip3` if you're running `python3`).
## Usage
Gradio is very easy to use with your existing code. Here are a few working examples:
### 0. Hello World [![alt text](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/18ODkJvyxHutTN0P5APWyGFO_xwNcgHDZ?usp=sharing)
Let's start with a basic function (no machine learning yet!) that greets an input name. We'll wrap the function with a `Text` to `Text` interface.
```python
import gradio as gr
def greet(name):
return "Hello " + name + "!"
gr.Interface(fn=greet, inputs="text", outputs="text").launch()
```
The core Interface class is initialized with three parameters:
- `fn`: the function to wrap
- `inputs`: the name of the input interface
- `outputs`: the name of the output interface
Calling the `launch()` function of the `Interface` object produces the interface shown in image below. Click on the screenshot to go the live interface in our getting started page.
<a href="https://gradio.app/getting_started#interface_4">
<p align="center">
<img src="https://i.ibb.co/Z8p7gLZ/hello-world.png" alt="drawing"/>
</p>
</a>
### 1. Inception Net [![alt text](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1c6gQiW88wKBwWq96nqEwuQ1Kyt5LejiU?usp=sharing)
Now, let's do a machine learning example. We're going to wrap an
interface around the InceptionV3 image classifier, which we'll load
using Tensorflow! Since this is an image classification model, we will use the `Image` input interface.
We'll output a dictionary of labels and their corresponding confidence scores with the `Label` output
interface. (The original Inception Net architecture [can be found here](https://arxiv.org/abs/1409.4842))
```python
import gradio as gr
import tensorflow as tf
import numpy as np
import requests
inception_net = tf.keras.applications.InceptionV3() # load the model
# Download human-readable labels for ImageNet.
response = requests.get("https://git.io/JJkYN")
labels = response.text.split("\n")
def classify_image(inp):
inp = inp.reshape((-1, 299, 299, 3))
inp = tf.keras.applications.inception_v3.preprocess_input(inp)
prediction = inception_net.predict(inp).flatten()
return {labels[i]: float(prediction[i]) for i in range(1000)}
image = gr.inputs.Image(shape=(299, 299, 3))
label = gr.outputs.Label(num_top_classes=3)
gr.Interface(fn=classify_image, inputs=image, outputs=label).launch()
```
This code will produce the interface below. The interface gives you a way to test
Inception Net by dragging and dropping images, and also allows you to use naturally modify the input image using image editing tools that
appear when you click EDIT. Notice here we provided actual `gradio.inputs` and `gradio.outputs` objects to the Interface
function instead of using string shortcuts. This lets us use built-in preprocessing (e.g. image resizing)
and postprocessing (e.g. choosing the number of labels to display) provided by these
interfaces.
<p align="center">
<img src="https://i.ibb.co/BtRNc62/inception-net.png" alt="drawing"/>
</p>
You can supply your own model instead of the pretrained model above, as well as use different kinds of models or functions. Here's a list of the interfaces we currently support, along with their preprocessing / postprocessing parameters:
**Input Interfaces**:
- `Sketchpad(shape=(28, 28), invert_colors=True, flatten=False, scale=1/255, shift=0, dtype='float64')`
- `Webcam(image_width=224, image_height=224, num_channels=3, label=None)`
- `Textbox(lines=1, placeholder=None, label=None, numeric=False)`
- `Radio(choices, label=None)`
- `Dropdown(choices, label=None)`
- `CheckboxGroup(choices, label=None)`
- `Slider(minimum=0, maximum=100, default=None, label=None)`
- `Image(shape=(224, 224, 3), image_mode='RGB', scale=1/127.5, shift=-1, label=None)`
- `Microphone()`
**Output Interfaces**:
- `Label(num_top_classes=None, label=None)`
- `KeyValues(label=None)`
- `Textbox(lines=1, placeholder=None, label=None)`
- `Image(label=None, plot=False)`
Interfaces can also be combined together, for multiple-input or multiple-output models.
### 2. Real-Time MNIST [![alt text](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1LXJqwdkZNkt1J_yfLWQ3FLxbG2cAF8p4?usp=sharing)
Let's wrap a fun `Sketchpad`-to-`Label` UI around MNIST. For this example, we'll take advantage of the `live`
feature in the library. Set `live=True` inside `Interface()`> to have it run continuous predictions.
We've abstracted the model training from the code below, but you can see the full code on the colab link.
```python
import tensorflow as tf
import gradio as gr
from urllib.request import urlretrieve
urlretrieve("https://gr-models.s3-us-west-2.amazonaws.com/mnist-model.h5","mnist-model.h5")
model = tf.keras.models.load_model("mnist-model.h5")
def recognize_digit(inp):
prediction = model.predict(inp.reshape(1, 28, 28, 1)).tolist()[0]
return {str(i): prediction[i] for i in range(10)}
sketchpad = gr.inputs.Sketchpad()
label = gr.outputs.Label(num_top_classes=3)
gr.Interface(fn=recognize_digit, inputs=sketchpad,
outputs=label, live=True).launch()
```
This code will produce the interface below.
<p align="center">
<img src="https://i.ibb.co/9n2mGgk/mnist-live.png" alt="drawing"/>
</p>
## Contributing:
If you would like to contribute and your contribution is small, you can directly open a pull request (PR). If you would like to contribute a larger feature, we recommend first creating an issue with a proposed design for discussion. Please see our contributing guidelines for more info.
## License:
Gradio is licensed under the Apache License 2.0
## See more:
You can find many more examples (like GPT-2, model comparison, multiple inputs, and numerical interfaces) as well as more info on usage on our website: www.gradio.app
See, also, the accompanying paper: ["Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild"](https://arxiv.org/pdf/1906.02569.pdf), *ICML HILL 2019*, and please use the citation below.
```
@article{abid2019gradio,
title={Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild},
author={Abid, Abubakar and Abdalla, Ali and Abid, Ali and Khan, Dawood and Alfozan, Abdulrahman and Zou, James},
journal={arXiv preprint arXiv:1906.02569},
year={2019}
}
```

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gradio-0.9.9.5/gradio.egg-info/PKG-INFO
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@ -1,11 +0,0 @@
Metadata-Version: 1.0
Name: gradio
Version: 0.9.9.5
Summary: Python library for easily interacting with trained machine learning models
Home-page: https://github.com/gradio-app/gradio-UI
Author: Abubakar Abid
Author-email: a12d@stanford.edu
License: UNKNOWN
Description: UNKNOWN
Keywords: machine learning,visualization,reproducibility
Platform: UNKNOWN

89
gradio-0.9.9.5/gradio.egg-info/SOURCES.txt
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@ -1,89 +0,0 @@
MANIFEST.in
README.md
setup.py
gradio/__init__.py
gradio/inputs.py
gradio/interface.py
gradio/networking.py
gradio/outputs.py
gradio/preprocessing_utils.py
gradio/strings.py
gradio/tunneling.py
gradio/validation_data.py
gradio.egg-info/PKG-INFO
gradio.egg-info/SOURCES.txt
gradio.egg-info/dependency_links.txt
gradio.egg-info/requires.txt
gradio.egg-info/top_level.txt
gradio/static/apple-app-site-association
gradio/static/css/gradio.css
gradio/static/css/loading.css
gradio/static/css/style.css
gradio/static/css/interfaces/input/checkbox_group.css
gradio/static/css/interfaces/input/csv.css
gradio/static/css/interfaces/input/dropdown.css
gradio/static/css/interfaces/input/image.css
gradio/static/css/interfaces/input/microphone.css
gradio/static/css/interfaces/input/radio.css
gradio/static/css/interfaces/input/sketchpad.css
gradio/static/css/interfaces/input/slider.css
gradio/static/css/interfaces/input/textbox.css
gradio/static/css/interfaces/input/webcam.css
gradio/static/css/interfaces/output/image.css
gradio/static/css/interfaces/output/key_values.css
gradio/static/css/interfaces/output/label.css
gradio/static/css/interfaces/output/textbox.css
gradio/static/css/vendor/tui-color-picker.css
gradio/static/css/vendor/tui-image-editor.css
gradio/static/img/logo.png
gradio/static/img/logo_error.png
gradio/static/img/logo_inline.png
gradio/static/img/logo_loading.gif
gradio/static/img/logo_mini.png
gradio/static/img/logo_only.png
gradio/static/img/mic.png
gradio/static/img/mic_recording.png
gradio/static/img/table.png
gradio/static/img/webcam.png
gradio/static/img/vendor/icon-a.svg
gradio/static/img/vendor/icon-b.svg
gradio/static/img/vendor/icon-c.svg
gradio/static/img/vendor/icon-d.svg
gradio/static/js/all_io.js
gradio/static/js/all_io.js.bak
gradio/static/js/gradio.js
gradio/static/js/utils.js
gradio/static/js/interfaces/input/checkbox.js
gradio/static/js/interfaces/input/checkbox_group.js
gradio/static/js/interfaces/input/dropdown.js
gradio/static/js/interfaces/input/image.js
gradio/static/js/interfaces/input/microphone.js
gradio/static/js/interfaces/input/radio.js
gradio/static/js/interfaces/input/sketchpad.js
gradio/static/js/interfaces/input/slider.js
gradio/static/js/interfaces/input/textbox.js
gradio/static/js/interfaces/input/webcam.js
gradio/static/js/interfaces/output/image.js
gradio/static/js/interfaces/output/key_values.js
gradio/static/js/interfaces/output/label.js
gradio/static/js/interfaces/output/textbox.js
gradio/static/js/vendor/FileSaver.min.js
gradio/static/js/vendor/black-theme.js
gradio/static/js/vendor/fabric.js
gradio/static/js/vendor/jquery.min.js
gradio/static/js/vendor/p5.dom.min.js
gradio/static/js/vendor/p5.min.js
gradio/static/js/vendor/p5.sound.min.js
gradio/static/js/vendor/papaparse.min.js
gradio/static/js/vendor/sketchpad.js
gradio/static/js/vendor/tui-code-snippet.min.js
gradio/static/js/vendor/tui-color-picker.js
gradio/static/js/vendor/tui-image-editor.js
gradio/static/js/vendor/wavesurfer.min.js
gradio/static/js/vendor/webcam.min.js
gradio/static/js/vendor/white-theme.js
gradio/templates/index.html
test/test_inputs.py
test/test_interface.py
test/test_networking.py
test/test_outputs.py

1
gradio-0.9.9.5/gradio.egg-info/dependency_links.txt
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@ -1 +0,0 @@

7
gradio-0.9.9.5/gradio.egg-info/requires.txt
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@ -1,7 +0,0 @@
numpy
requests
paramiko
scipy
IPython
scikit-image
analytics-python

1
gradio-0.9.9.5/gradio.egg-info/top_level.txt
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@ -1 +0,0 @@
gradio

1
gradio-0.9.9.5/gradio/__init__.py
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@ -1 +0,0 @@
from gradio.interface import * # This makes it possible to import `Interface` as `gradio.Interface`.

288
gradio-0.9.9.5/gradio/inputs.py
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@ -1,288 +0,0 @@
"""
This module defines various classes that can serve as the `input` to an interface. Each class must inherit from
`AbstractInput`, and each class must define a path to its template. All of the subclasses of `AbstractInput` are
automatically added to a registry, which allows them to be easily referenced in other parts of the code.
"""
from abc import ABC, abstractmethod
from gradio import preprocessing_utils, validation_data
import numpy as np
import PIL.Image, PIL.ImageOps
import time
import warnings
import json
import datetime
import os
# Where to find the static resources associated with each template.
# BASE_INPUT_INTERFACE_TEMPLATE_PATH = 'static/js/interfaces/input/{}.js'
BASE_INPUT_INTERFACE_JS_PATH = 'static/js/interfaces/input/{}.js'
class AbstractInput(ABC):
"""
An abstract class for defining the methods that all gradio inputs should have.
When this is subclassed, it is automatically added to the registry
"""
def __init__(self, label):
self.label = label
def get_validation_inputs(self):
"""
An interface can optionally implement a method that returns a list of examples inputs that it should be able to
accept and preprocess for validation purposes.
"""
return []
def get_template_context(self):
"""
:return: a dictionary with context variables for the javascript file associated with the context
"""
return {"label": self.label}
def preprocess(self, inp):
"""
By default, no pre-processing is applied to text.
"""
return inp
def process_example(self, example):
"""
Proprocess example for UI
"""
return example
@classmethod
def get_shortcut_implementations(cls):
"""
Return dictionary of shortcut implementations
"""
return {}
class Sketchpad(AbstractInput):
def __init__(self, shape=(28, 28), invert_colors=True,
flatten=False, label=None):
self.image_width = shape[0]
self.image_height = shape[1]
self.invert_colors = invert_colors
self.flatten = flatten
super().__init__(label)
@classmethod
def get_shortcut_implementations(cls):
return {
"sketchpad": {},
}
def preprocess(self, inp):
"""
Default preprocessing method for the SketchPad is to convert the sketch to black and white and resize 28x28
"""
im_transparent = preprocessing_utils.decode_base64_to_image(inp)
im = PIL.Image.new("RGBA", im_transparent.size, "WHITE") # Create a white background for the alpha channel
im.paste(im_transparent, (0, 0), im_transparent)
im = im.convert('L')
if self.invert_colors:
im = PIL.ImageOps.invert(im)
im = im.resize((self.image_width, self.image_height))
if self.flatten:
array = np.array(im).flatten().reshape(1, self.image_width * self.image_height)
else:
array = np.array(im).flatten().reshape(1, self.image_width, self.image_height)
return array
def process_example(self, example):
return preprocessing_utils.convert_file_to_base64(example)
class Webcam(AbstractInput):
def __init__(self, shape=(224, 224), label=None):
self.image_width = shape[0]
self.image_height = shape[1]
self.num_channels = 3
super().__init__(label)
def get_validation_inputs(self):
return validation_data.BASE64_COLOR_IMAGES
@classmethod
def get_shortcut_implementations(cls):
return {
"webcam": {},
}
def preprocess(self, inp):
"""
Default preprocessing method for is to convert the picture to black and white and resize to be 48x48
"""
im = preprocessing_utils.decode_base64_to_image(inp)
im = im.convert('RGB')
im = preprocessing_utils.resize_and_crop(im, (self.image_width, self.image_height))
return np.array(im)
class Textbox(AbstractInput):
def __init__(self, lines=1, placeholder=None, default=None, numeric=False, label=None):
self.lines = lines
self.placeholder = placeholder
self.default = default
self.numeric = numeric
super().__init__(label)
def get_validation_inputs(self):
return validation_data.ENGLISH_TEXTS
def get_template_context(self):
return {
"lines": self.lines,
"placeholder": self.placeholder,
"default": self.default,
**super().get_template_context()
}
@classmethod
def get_shortcut_implementations(cls):
return {
"text": {},
"textbox": {"lines": 7},
"number": {"numeric": True}
}
def preprocess(self, inp):
"""
Cast type of input
"""
if self.numeric:
return float(inp)
else:
return inp
class Radio(AbstractInput):
def __init__(self, choices, label=None):
self.choices = choices
super().__init__(label)
def get_template_context(self):
return {
"choices": self.choices,
**super().get_template_context()
}
class Dropdown(AbstractInput):
def __init__(self, choices, label=None):
self.choices = choices
super().__init__(label)
def get_template_context(self):
return {
"choices": self.choices,
**super().get_template_context()
}
class CheckboxGroup(AbstractInput):
def __init__(self, choices, label=None):
self.choices = choices
super().__init__(label)
def get_template_context(self):
return {
"choices": self.choices,
**super().get_template_context()
}
class Slider(AbstractInput):
def __init__(self, minimum=0, maximum=100, default=None, label=None):
self.minimum = minimum
self.maximum = maximum
self.default = minimum if default is None else default
super().__init__(label)
def get_template_context(self):
return {
"minimum": self.minimum,
"maximum": self.maximum,
"default": self.default,
**super().get_template_context()
}
@classmethod
def get_shortcut_implementations(cls):
return {
"slider": {},
}
class Checkbox(AbstractInput):
def __init__(self, label=None):
super().__init__(label)
@classmethod
def get_shortcut_implementations(cls):
return {
"checkbox": {},
}
class Image(AbstractInput):
def __init__(self, shape=(224, 224), image_mode='RGB', label=None):
self.image_width = shape[0]
self.image_height = shape[1]
self.image_mode = image_mode
super().__init__(label)
def get_validation_inputs(self):
return validation_data.BASE64_COLOR_IMAGES
@classmethod
def get_shortcut_implementations(cls):
return {
"image": {},
}
def get_template_context(self):
return {
**super().get_template_context()
}
def preprocess(self, inp):
"""
Default preprocessing method for is to convert the picture to black and white and resize to be 48x48
"""
im = preprocessing_utils.decode_base64_to_image(inp)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
im = im.convert(self.image_mode)
im = preprocessing_utils.resize_and_crop(im, (self.image_width, self.image_height))
return np.array(im)
def process_example(self, example):
if os.path.exists(example):
return preprocessing_utils.convert_file_to_base64(example)
else:
return example
class Microphone(AbstractInput):
def __init__(self, label=None):
super().__init__(label)
def preprocess(self, inp):
"""
By default, no pre-processing is applied to a microphone input file
"""
file_obj = preprocessing_utils.decode_base64_to_wav_file(inp)
mfcc_array = preprocessing_utils.generate_mfcc_features_from_audio_file(file_obj.name)
return mfcc_array
# Automatically adds all shortcut implementations in AbstractInput into a dictionary.
shortcuts = {}
for cls in AbstractInput.__subclasses__():
for shortcut, parameters in cls.get_shortcut_implementations().items():
shortcuts[shortcut] = cls(**parameters)

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@ -1,413 +0,0 @@
"""
This is the core file in the `gradio` package, and defines the Interface class, including methods for constructing the
interface using the input and output types.
"""
import tempfile
import traceback
import webbrowser
import gradio.inputs
import gradio.outputs
from gradio import networking, strings
from distutils.version import StrictVersion
import pkg_resources
import requests
import random
import time
import inspect
from IPython import get_ipython
import sys
import weakref
import analytics
import socket
PKG_VERSION_URL = "https://gradio.app/api/pkg-version"
analytics.write_key = "uxIFddIEuuUcFLf9VgH2teTEtPlWdkNy"
analytics_url = 'https://api.gradio.app/'
hostname = socket.gethostname()
ip_address = socket.gethostbyname(hostname)
class Interface:
"""
The Interface class represents a general input/output interface for a machine learning model. During construction,
the appropriate inputs and outputs
"""
instances = weakref.WeakSet()
def __init__(self, fn, inputs, outputs, saliency=None, verbose=False, examples=None,
live=False, show_input=True, show_output=True,
capture_session=False, title=None, description=None,
thumbnail=None, server_name=networking.LOCALHOST_NAME):
"""
:param fn: a function that will process the input panel data from the interface and return the output panel data.
:param inputs: a string or `AbstractInput` representing the input interface.
:param outputs: a string or `AbstractOutput` representing the output interface.
"""
def get_input_instance(iface):
if isinstance(iface, str):
return gradio.inputs.shortcuts[iface.lower()]
elif isinstance(iface, gradio.inputs.AbstractInput):
return iface
else:
raise ValueError("Input interface must be of type `str` or "
"`AbstractInput`")
def get_output_instance(iface):
if isinstance(iface, str):
return gradio.outputs.shortcuts[iface.lower()]
elif isinstance(iface, gradio.outputs.AbstractOutput):
return iface
else:
raise ValueError(
"Output interface must be of type `str` or "
"`AbstractOutput`"
)
if isinstance(inputs, list):
self.input_interfaces = [get_input_instance(i) for i in inputs]
else:
self.input_interfaces = [get_input_instance(inputs)]
if isinstance(outputs, list):
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.predict = fn
self.verbose = verbose
self.status = "OFF"
self.saliency = saliency
self.live = live
self.show_input = show_input
self.show_output = show_output
self.flag_hash = random.getrandbits(32)
self.capture_session = capture_session
self.session = None
self.server_name = server_name
self.title = title
self.description = description
self.thumbnail = thumbnail
self.examples = examples
self.server_port = None
self.simple_server = None
Interface.instances.add(self)
data = {'fn': fn,
'inputs': inputs,
'outputs': outputs,
'saliency': saliency,
'live': live,
'capture_session': capture_session,
'host_name': hostname,
'ip_address': ip_address
}
try:
requests.post(analytics_url + 'gradio-initiated-analytics/',
data=data)
except requests.ConnectionError:
print("gradio-initiated-analytics/ Connection Error")
def get_config_file(self):
config = {
"input_interfaces": [
(iface.__class__.__name__.lower(), iface.get_template_context())
for iface in self.input_interfaces],
"output_interfaces": [
(iface.__class__.__name__.lower(), iface.get_template_context())
for iface in self.output_interfaces],
"function_count": len(self.predict),
"live": self.live,
"show_input": self.show_input,
"show_output": self.show_output,
"title": self.title,
"description": self.description,
"thumbnail": self.thumbnail
}
try:
param_names = inspect.getfullargspec(self.predict[0])[0]
for iface, param in zip(config["input_interfaces"], param_names):
if not iface[1]["label"]:
iface[1]["label"] = param.replace("_", " ")
for i, iface in enumerate(config["output_interfaces"]):
ret_name = "Output " + str(i + 1) if len(config["output_interfaces"]) > 1 else "Output"
if not iface[1]["label"]:
iface[1]["label"] = ret_name
except ValueError:
pass
return config
def process(self, raw_input):
processed_input = [input_interface.preprocess(
raw_input[i]) for i, input_interface in
enumerate(self.input_interfaces)]
predictions = []
durations = []
for predict_fn in self.predict:
start = time.time()
if self.capture_session and not(self.session is None):
graph, sess = self.session
with graph.as_default():
with sess.as_default():
prediction = predict_fn(*processed_input)
else:
try:
prediction = predict_fn(*processed_input)
except ValueError as exception:
if str(exception).endswith("is not an element of this "
"graph."):
raise ValueError("It looks like you might be using "
"tensorflow < 2.0. Please "
"pass capture_session=True in "
"Interface to avoid the 'Tensor is "
"not an element of this graph.' "
"error.")
else:
raise exception
duration = time.time() - start
if len(self.output_interfaces) == len(self.predict):
prediction = [prediction]
durations.append(duration)
predictions.extend(prediction)
processed_output = [output_interface.postprocess(
predictions[i]) for i, output_interface in enumerate(self.output_interfaces)]
return processed_output, durations
def validate(self):
if self.validate_flag:
if self.verbose:
print("Interface already validated")
return
validation_inputs = self.input_interface.get_validation_inputs()
n = len(validation_inputs)
if n == 0:
self.validate_flag = True
if self.verbose:
print(
"No validation samples for this interface... skipping validation."
)
return
for m, msg in enumerate(validation_inputs):
if self.verbose:
print(
"Validating samples: {}/{} [".format(m+1, n)
+ "=" * (m + 1)
+ "." * (n - m - 1)
+ "]",
end="\r",
)
try:
processed_input = self.input_interface.preprocess(msg)
prediction = self.predict(processed_input)
except Exception as e:
data = {'error': e}
try:
requests.post(analytics_url + 'gradio-error-analytics/',
data=data)
except requests.ConnectionError:
print("gradio-error-analytics/ Connection Error")
if self.verbose:
print("\n----------")
print(
"Validation failed, likely due to incompatible pre-processing and model input. See below:\n"
)
print(traceback.format_exc())
break
try:
_ = self.output_interface.postprocess(prediction)
except Exception as e:
data = {'error': e}
try:
requests.post(analytics_url + 'gradio-error-analytics/',
data=data)
except requests.ConnectionError:
print("gradio-error-analytics/ Connection Error")
if self.verbose:
print("\n----------")
print(
"Validation failed, likely due to incompatible model output and post-processing."
"See below:\n"
)
print(traceback.format_exc())
break
else: # This means if a break was not explicitly called
self.validate_flag = True
if self.verbose:
print("\n\nValidation passed successfully!")
return
raise RuntimeError("Validation did not pass")
def close(self):
if self.simple_server and not(self.simple_server.fileno() == -1): # checks to see if server is running
print("Closing Gradio server on port {}...".format(self.server_port))
networking.close_server(self.simple_server)
def launch(self, inline=None, inbrowser=None, share=False, validate=True, debug=False):
"""
Standard method shared by interfaces that creates the interface and sets up a websocket to communicate with it.
:param inline: boolean. If True, then a gradio interface is created inline (e.g. in jupyter or colab notebook)
:param inbrowser: boolean. If True, then a new browser window opens with the gradio interface.
:param share: boolean. If True, then a share link is generated using ngrok is displayed to the user.
:param validate: boolean. If True, then the validation is run if the interface has not already been validated.
"""
# if validate and not self.validate_flag:
# self.validate()
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
output_directory = tempfile.mkdtemp()
# Set up a port to serve the directory containing the static files with interface.
server_port, httpd = networking.start_simple_server(self, output_directory, self.server_name)
path_to_local_server = "http://{}:{}/".format(self.server_name, server_port)
networking.build_template(output_directory)
self.server_port = server_port
self.status = "RUNNING"
self.simple_server = httpd
is_colab = False
try: # Check if running interactively using ipython.
from_ipynb = get_ipython()
if "google.colab" in str(from_ipynb):
is_colab = True
except NameError:
data = {'error': 'NameError in launch method'}
try:
requests.post(analytics_url + 'gradio-error-analytics/',
data=data)
except requests.ConnectionError:
print("Connection Error")
pass
try:
current_pkg_version = pkg_resources.require("gradio")[0].version
latest_pkg_version = requests.get(url=PKG_VERSION_URL).json()["version"]
if StrictVersion(latest_pkg_version) > StrictVersion(current_pkg_version):
print("IMPORTANT: You are using gradio version {}, "
"however version {} "
"is available, please upgrade.".format(
current_pkg_version, latest_pkg_version))
print('--------')
except: # TODO(abidlabs): don't catch all exceptions
pass
if not is_colab:
print(strings.en["RUNNING_LOCALLY"].format(path_to_local_server))
else:
if debug:
print("Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. "
"To turn off, set debug=False in launch().")
else:
print("Colab notebook detected. To show errors in colab notebook, set debug=True in launch()")
if share:
try:
share_url = networking.setup_tunnel(server_port)
print("Running on External URL:", share_url)
except RuntimeError:
data = {'error': 'RuntimeError in launch method'}
try:
requests.post(analytics_url + 'gradio-error-analytics/',
data=data)
except requests.ConnectionError:
print("Connection Error")
share_url = None
if self.verbose:
print(strings.en["NGROK_NO_INTERNET"])
else:
if (
is_colab
): # For a colab notebook, create a public link even if share is False.
share_url = networking.setup_tunnel(server_port)
print("Running on External URL:", share_url)
if self.verbose:
print(strings.en["COLAB_NO_LOCAL"])
else: # If it's not a colab notebook and share=False, print a message telling them about the share option.
if self.verbose:
print(strings.en["PUBLIC_SHARE_TRUE"])
share_url = None
if inline is None:
try: # Check if running interactively using ipython.
get_ipython()
inline = True
if inbrowser is None:
inbrowser = False
except NameError:
inline = False
if inbrowser is None:
inbrowser = True
else:
if inbrowser is None:
inbrowser = False
if inbrowser and not is_colab:
webbrowser.open(
path_to_local_server
) # Open a browser tab with the interface.
if inline:
from IPython.display import IFrame, display
if (
is_colab
): # Embed the remote interface page if on google colab;
# otherwise, embed the local page.
while not networking.url_ok(share_url):
time.sleep(1)
display(IFrame(share_url, width=1000, height=500))
else:
display(IFrame(path_to_local_server, width=1000, height=500))
config = self.get_config_file()
config["share_url"] = share_url
processed_examples = []
if self.examples is not None:
for example_set in self.examples:
processed_set = []
for iface, example in zip(self.input_interfaces, example_set):
processed_set.append(iface.process_example(example))
processed_examples.append(processed_set)
config["examples"] = processed_examples
networking.set_config(config, output_directory)
if debug:
while True:
sys.stdout.flush()
time.sleep(0.1)
launch_method = 'browser' if inbrowser else 'inline'
data = {'launch_method': launch_method,
'is_google_colab': is_colab,
'is_sharing_on': share,
'share_url': share_url,
'host_name': hostname,
'ip_address': ip_address
}
try:
requests.post(analytics_url + 'gradio-hosted-launched-analytics/',
data=data)
except requests.ConnectionError:
print("Connection Error")
return httpd, path_to_local_server, share_url
@classmethod
def get_instances(cls):
return list(Interface.instances) #Returns list of all current instances
def reset_all():
for io in Interface.get_instances():
io.close()

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@ -1,252 +0,0 @@
"""
Defines helper methods useful for setting up ports, launching servers, and handling `ngrok`
"""
import os
import socket
import threading
from http.server import HTTPServer as BaseHTTPServer, SimpleHTTPRequestHandler
import pkg_resources
from distutils import dir_util
from gradio import inputs, outputs
import json
from gradio.tunneling import create_tunnel
import urllib.request
from shutil import copyfile
import requests
import sys
INITIAL_PORT_VALUE = (
7860
) # The http server will try to open on port 7860. If not available, 7861, 7862, etc.
TRY_NUM_PORTS = (
100
) # Number of ports to try before giving up and throwing an exception.
LOCALHOST_NAME = os.getenv('GRADIO_SERVER_NAME', "127.0.0.1")
GRADIO_API_SERVER = "https://api.gradio.app/v1/tunnel-request"
STATIC_TEMPLATE_LIB = pkg_resources.resource_filename("gradio", "templates/")
STATIC_PATH_LIB = pkg_resources.resource_filename("gradio", "static/")
STATIC_PATH_TEMP = "static/"
TEMPLATE_TEMP = "index.html"
BASE_JS_FILE = "static/js/all_io.js"
CONFIG_FILE = "static/config.json"
ASSOCIATION_PATH_IN_STATIC = "static/apple-app-site-association"
ASSOCIATION_PATH_IN_ROOT = "apple-app-site-association"
FLAGGING_DIRECTORY = 'static/flagged/'
FLAGGING_FILENAME = 'data.txt'
def build_template(temp_dir):
"""
Create HTML file with supporting JS and CSS files in a given directory.
:param temp_dir: string with path to temp directory in which the html file should be built
"""
dir_util.copy_tree(STATIC_TEMPLATE_LIB, temp_dir)
dir_util.copy_tree(STATIC_PATH_LIB, os.path.join(
temp_dir, STATIC_PATH_TEMP))
# Move association file to root of temporary directory.
copyfile(os.path.join(temp_dir, ASSOCIATION_PATH_IN_STATIC),
os.path.join(temp_dir, ASSOCIATION_PATH_IN_ROOT))
def render_template_with_tags(template_path, context):
"""
Combines the given template with a given context dictionary by replacing all of the occurrences of tags (enclosed
in double curly braces) with corresponding values.
:param template_path: a string with the path to the template file
:param context: a dictionary whose string keys are the tags to replace and whose string values are the replacements.
"""
print(template_path, context)
with open(template_path) as fin:
old_lines = fin.readlines()
new_lines = render_string_or_list_with_tags(old_lines, context)
with open(template_path, "w") as fout:
for line in new_lines:
fout.write(line)
def render_string_or_list_with_tags(old_lines, context):
# Handle string case
if isinstance(old_lines, str):
for key, value in context.items():
old_lines = old_lines.replace(r"{{" + key + r"}}", str(value))
return old_lines
# Handle list case
new_lines = []
for line in old_lines:
for key, value in context.items():
line = line.replace(r"{{" + key + r"}}", str(value))
new_lines.append(line)
return new_lines
def set_config(config, temp_dir):
config_file = os.path.join(temp_dir, CONFIG_FILE)
with open(config_file, "w") as output:
json.dump(config, output)
def get_first_available_port(initial, final):
"""
Gets the first open port in a specified range of port numbers
:param initial: the initial value in the range of port numbers
:param final: final (exclusive) value in the range of port numbers, should be greater than `initial`
:return:
"""
for port in range(initial, final):
try:
s = socket.socket() # create a socket object
s.bind((LOCALHOST_NAME, port)) # Bind to the port
s.close()
return port
except OSError:
pass
raise OSError(
"All ports from {} to {} are in use. Please close a port.".format(
initial, final
)
)
def serve_files_in_background(interface, port, directory_to_serve=None, server_name=LOCALHOST_NAME):
class HTTPHandler(SimpleHTTPRequestHandler):
"""This handler uses server.base_path instead of always using os.getcwd()"""
def _set_headers(self):
self.send_response(200)
self.send_header("Content-type", "application/json")
self.end_headers()
def translate_path(self, path):
path = SimpleHTTPRequestHandler.translate_path(self, path)
relpath = os.path.relpath(path, os.getcwd())
fullpath = os.path.join(self.server.base_path, relpath)
return fullpath
def log_message(self, format, *args):
return
def do_POST(self):
# Read body of the request.
if self.path == "/api/predict/":
# Make the prediction.
self._set_headers()
data_string = self.rfile.read(
int(self.headers["Content-Length"]))
msg = json.loads(data_string)
raw_input = msg["data"]
prediction, durations = interface.process(raw_input)
output = {"data": prediction, "durations": durations}
if interface.saliency is not None:
saliency = interface.saliency(raw_input, prediction)
output['saliency'] = saliency.tolist()
# if interface.always_flag:
# msg = json.loads(data_string)
# flag_dir = os.path.join(FLAGGING_DIRECTORY, str(interface.hash))
# os.makedirs(flag_dir, exist_ok=True)
# output_flag = {'input': interface.input_interface.rebuild_flagged(flag_dir, msg['data']),
# 'output': interface.output_interface.rebuild_flagged(flag_dir, processed_output),
# }
# with open(os.path.join(flag_dir, FLAGGING_FILENAME), 'a+') as f:
# f.write(json.dumps(output_flag))
# f.write("\n")
# Prepare return json dictionary.
self.wfile.write(json.dumps(output).encode())
elif self.path == "/api/flag/":
self._set_headers()
data_string = self.rfile.read(
int(self.headers["Content-Length"]))
msg = json.loads(data_string)
flag_dir = os.path.join(FLAGGING_DIRECTORY,
str(interface.flag_hash))
os.makedirs(flag_dir, exist_ok=True)
output = {'inputs': [interface.input_interfaces[
i].rebuild_flagged(
flag_dir, msg['data']['input_data']) for i
in range(len(interface.input_interfaces))],
'outputs': [interface.output_interfaces[
i].rebuild_flagged(
flag_dir, msg['data']['output_data']) for i
in range(len(interface.output_interfaces))],
'message': msg['data']['message']}
with open(os.path.join(flag_dir, FLAGGING_FILENAME), 'a+') as f:
f.write(json.dumps(output))
f.write("\n")
else:
self.send_error(404, 'Path not found: {}'.format(self.path))
class HTTPServer(BaseHTTPServer):
"""The main server, you pass in base_path which is the path you want to serve requests from"""
def __init__(self, base_path, server_address, RequestHandlerClass=HTTPHandler):
self.base_path = base_path
BaseHTTPServer.__init__(self, server_address, RequestHandlerClass)
httpd = HTTPServer(directory_to_serve, (server_name, port))
# Now loop forever
def serve_forever():
try:
while True:
sys.stdout.flush()
httpd.serve_forever()
except (KeyboardInterrupt, OSError):
httpd.shutdown()
httpd.server_close()
thread = threading.Thread(target=serve_forever, daemon=False)
thread.start()
return httpd
def start_simple_server(interface, directory_to_serve=None, server_name=None):
port = get_first_available_port(
INITIAL_PORT_VALUE, INITIAL_PORT_VALUE + TRY_NUM_PORTS
)
httpd = serve_files_in_background(interface, port, directory_to_serve, server_name)
return port, httpd
def close_server(server):
server.server_close()
def url_request(url):
try:
req = urllib.request.Request(
url=url, headers={"content-type": "application/json"}
)
res = urllib.request.urlopen(req, timeout=10)
return res
except Exception as e:
raise RuntimeError(str(e))
def setup_tunnel(local_server_port):
response = url_request(GRADIO_API_SERVER)
if response and response.code == 200:
try:
payload = json.loads(response.read().decode("utf-8"))[0]
return create_tunnel(payload, LOCALHOST_NAME, local_server_port)
except Exception as e:
raise RuntimeError(str(e))
def url_ok(url):
try:
r = requests.head(url)
return r.status_code == 200
except ConnectionError:
return False

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@ -1,174 +0,0 @@
"""
This module defines various classes that can serve as the `output` to an interface. Each class must inherit from
`AbstractOutput`, and each class must define a path to its template. All of the subclasses of `AbstractOutput` are
automatically added to a registry, which allows them to be easily referenced in other parts of the code.
"""
from abc import ABC, abstractmethod
import numpy as np
import json
from gradio import preprocessing_utils
import datetime
import operator
from numbers import Number
# Where to find the static resources associated with each template.
BASE_OUTPUT_INTERFACE_JS_PATH = 'static/js/interfaces/output/{}.js'
class AbstractOutput(ABC):
"""
An abstract class for defining the methods that all gradio inputs should have.
When this is subclassed, it is automatically added to the registry
"""
def __init__(self, label):
self.label = label
def get_template_context(self):
"""
:return: a dictionary with context variables for the javascript file associated with the context
"""
return {"label": self.label}
def postprocess(self, prediction):
"""
Any postprocessing needed to be performed on function output.
"""
return prediction
@classmethod
def get_shortcut_implementations(cls):
"""
Return dictionary of shortcut implementations
"""
return {}
class Label(AbstractOutput):
LABEL_KEY = "label"
CONFIDENCE_KEY = "confidence"
CONFIDENCES_KEY = "confidences"
def __init__(self, num_top_classes=None, label=None):
self.num_top_classes = num_top_classes
super().__init__(label)
def postprocess(self, prediction):
if isinstance(prediction, str) or isinstance(prediction, Number):
return {"label": str(prediction)}
elif isinstance(prediction, dict):
sorted_pred = sorted(
prediction.items(),
key=operator.itemgetter(1),
reverse=True
)
if self.num_top_classes is not None:
sorted_pred = sorted_pred[:self.num_top_classes]
return {
self.LABEL_KEY: sorted_pred[0][0],
self.CONFIDENCES_KEY: [
{
self.LABEL_KEY: pred[0],
self.CONFIDENCE_KEY: pred[1]
} for pred in sorted_pred
]
}
elif isinstance(prediction, int) or isinstance(prediction, float):
return {self.LABEL_KEY: str(prediction)}
else:
raise ValueError("The `Label` output interface expects one of: a string label, or an int label, a "
"float label, or a dictionary whose keys are labels and values are confidences.")
@classmethod
def get_shortcut_implementations(cls):
return {
"label": {},
}
class KeyValues(AbstractOutput):
def __init__(self, label=None):
super().__init__(label)
def postprocess(self, prediction):
if isinstance(prediction, dict):
return prediction
else:
raise ValueError("The `KeyValues` output interface expects an output that is a dictionary whose keys are "
"labels and values are corresponding values.")
@classmethod
def get_shortcut_implementations(cls):
return {
"key_values": {},
}
class Textbox(AbstractOutput):
def __init__(self, label=None):
super().__init__(label)
def get_template_context(self):
return {
**super().get_template_context()
}
@classmethod
def get_shortcut_implementations(cls):
return {
"text": {},
"number": {},
}
def postprocess(self, prediction):
if isinstance(prediction, str) or isinstance(prediction, int) or isinstance(prediction, float):
return str(prediction)
else:
raise ValueError("The `Textbox` output interface expects an output that is one of: a string, or"
"an int/float that can be converted to a string.")
class Image(AbstractOutput):
def __init__(self, plot=False, label=None):
self.plot = plot
super().__init__(label)
@classmethod
def get_shortcut_implementations(cls):
return {
"image": {},
"plot": {"plot": True}
}
def postprocess(self, prediction):
"""
"""
if self.plot:
try:
return preprocessing_utils.encode_plot_to_base64(prediction)
except:
raise ValueError("The `Image` output interface expects a `matplotlib.pyplot` object"
"if plt=True.")
else:
try:
return preprocessing_utils.encode_array_to_base64(prediction)
except:
raise ValueError("The `Image` output interface (with plt=False) expects a numpy array.")
def rebuild_flagged(self, dir, msg):
"""
Default rebuild method to decode a base64 image
"""
im = preprocessing_utils.decode_base64_to_image(msg)
timestamp = datetime.datetime.now()
filename = 'output_{}.png'.format(timestamp.
strftime("%Y-%m-%d-%H-%M-%S"))
im.save('{}/{}'.format(dir, filename), 'PNG')
return filename
# Automatically adds all shortcut implementations in AbstractInput into a dictionary.
shortcuts = {}
for cls in AbstractOutput.__subclasses__():
for shortcut, parameters in cls.get_shortcut_implementations().items():
shortcuts[shortcut] = cls(**parameters)

148
gradio-0.9.9.5/gradio/preprocessing_utils.py
View File

@ -1,148 +0,0 @@
from PIL import Image, ImageOps
from io import BytesIO
import base64
import tempfile
import scipy.io.wavfile
from scipy.fftpack import dct
import numpy as np
import skimage
#########################
# IMAGE PRE-PROCESSING
#########################
def decode_base64_to_image(encoding):
content = encoding.split(';')[1]
image_encoded = content.split(',')[1]
return Image.open(BytesIO(base64.b64decode(image_encoded)))
def convert_file_to_base64(img):
with open(img, "rb") as image_file:
encoded_string = base64.b64encode(image_file.read())
base64_str = str(encoded_string, 'utf-8')
type = img.split(".")[-1]
return "data:image/" + type + ";base64," + base64_str
def encode_plot_to_base64(plt):
with BytesIO() as output_bytes:
plt.savefig(output_bytes, format="png")
bytes_data = output_bytes.getvalue()
plt.close()
base64_str = str(base64.b64encode(bytes_data), 'utf-8')
return "data:image/png;base64," + base64_str
def encode_array_to_base64(image_array):
with BytesIO() as output_bytes:
PIL_image = Image.fromarray(skimage.img_as_ubyte(image_array))
PIL_image.save(output_bytes, 'PNG')
bytes_data = output_bytes.getvalue()
base64_str = str(base64.b64encode(bytes_data), 'utf-8')
return "data:image/png;base64," + base64_str
def resize_and_crop(img, size, crop_type='center'):
"""
Resize and crop an image to fit the specified size.
args:
size: `(width, height)` tuple.
crop_type: can be 'top', 'middle' or 'bottom', depending on this
value, the image will cropped getting the 'top/left', 'middle' or
'bottom/right' of the image to fit the size.
raises:
ValueError: if an invalid `crop_type` is provided.
"""
if crop_type == "top":
center = (0, 0)
elif crop_type == "center":
center = (0.5, 0.5)
else:
raise ValueError
return ImageOps.fit(img, size, centering=center)
##################
# AUDIO FILES
##################
def decode_base64_to_wav_file(encoding):
inp = encoding.split(';')[1].split(',')[1]
wav_obj = base64.b64decode(inp)
file_obj = tempfile.NamedTemporaryFile()
file_obj.close()
with open(file_obj.name, 'wb') as f:
f.write(wav_obj)
return file_obj
def generate_mfcc_features_from_audio_file(wav_filename,
pre_emphasis=0.95,
frame_size= 0.025,
frame_stride=0.01,
NFFT=512,
nfilt=40,
num_ceps=12,
cep_lifter=22):
"""
Loads and preprocesses a .wav audio file into mfcc coefficients, the typical inputs to models.
Adapted from: https://haythamfayek.com/2016/04/21/speech-processing-for-machine-learning.html
:param wav_filename: string name of audio file to process.
:param pre_emphasis: a float factor, typically 0.95 or 0.97, which amplifies high frequencies.
:param frame_size: a float that is the length, in seconds, of time frame over which to take the fft.
:param frame_stride: a float that is the offset, in seconds, between consecutive time frames.
:param NFFT: The number of points in the short-time fft for each time frame.
:param nfilt: The number of filters on the Mel-scale to extract frequency bands.
:param num_ceps: the number of cepstral coefficients to retrain.
:param cep_lifter: the int factor, by which to de-emphasize higher-frequency.
:return: a numpy array of mfcc coefficients.
"""
sample_rate, signal = scipy.io.wavfile.read(wav_filename)
emphasized_signal = np.append(signal[0], signal[1:] - pre_emphasis * signal[:-1])
frame_length, frame_step = frame_size * sample_rate, frame_stride * sample_rate # Convert from seconds to samples
signal_length = len(emphasized_signal)
frame_length = int(round(frame_length))
frame_step = int(round(frame_step))
num_frames = int(np.ceil(float(np.abs(signal_length - frame_length)) / frame_step)) # Make sure that we have at least 1 frame
pad_signal_length = num_frames * frame_step + frame_length
z = np.zeros((pad_signal_length - signal_length))
pad_signal = np.append(emphasized_signal, z) # Pad Signal to make sure that all frames have equal number of samples without truncating any samples from the original signal
indices = np.tile(np.arange(0, frame_length), (num_frames, 1)) + np.tile(np.arange(0, num_frames * frame_step, frame_step), (frame_length, 1)).T
frames = pad_signal[indices.astype(np.int32, copy=False)]
frames *= np.hamming(frame_length)
mag_frames = np.absolute(np.fft.rfft(frames, NFFT)) # Magnitude of the FFT
pow_frames = ((1.0 / NFFT) * ((mag_frames) ** 2)) # Power Spectrum
low_freq_mel = 0
high_freq_mel = (2595 * np.log10(1 + (sample_rate / 2) / 700)) # Convert Hz to Mel
mel_points = np.linspace(low_freq_mel, high_freq_mel, nfilt + 2) # Equally spaced in Mel scale
hz_points = (700 * (10**(mel_points / 2595) - 1)) # Convert Mel to Hz
bin = np.floor((NFFT + 1) * hz_points / sample_rate)
fbank = np.zeros((nfilt, int(np.floor(NFFT / 2 + 1))))
for m in range(1, nfilt + 1):
f_m_minus = int(bin[m - 1]) # left
f_m = int(bin[m]) # center
f_m_plus = int(bin[m + 1]) # right
for k in range(f_m_minus, f_m):
fbank[m - 1, k] = (k - bin[m - 1]) / (bin[m] - bin[m - 1])
for k in range(f_m, f_m_plus):
fbank[m - 1, k] = (bin[m + 1] - k) / (bin[m + 1] - bin[m])
filter_banks = np.dot(pow_frames, fbank.T)
filter_banks = np.where(filter_banks == 0, np.finfo(float).eps, filter_banks) # Numerical Stability
filter_banks = 20 * np.log10(filter_banks) # dB
mfcc = dct(filter_banks, type=2, axis=1, norm='ortho')[:, 0: (num_ceps + 1)] # Keep filters 1-13 by default.
(nframes, ncoeff) = mfcc.shape
n = np.arange(ncoeff)
lift = 1 + (cep_lifter / 2) * np.sin(np.pi * n / cep_lifter)
mfcc *= lift
filter_banks -= (np.mean(filter_banks, axis=0) + 1e-8)
mfcc -= (np.mean(mfcc, axis=0) + 1e-8)
return mfcc[np.newaxis, :, :] # Create a batch dimension.

11
gradio-0.9.9.5/gradio/static/apple-app-site-association
View File

@ -1,11 +0,0 @@
{
"applinks": {
"apps": [],
"details": [
{
"appID": "RHW8FBGSTX.app.gradio.Gradio",
"paths": ["*"]
}
]
}
}

125
gradio-0.9.9.5/gradio/static/css/gradio.css
View File

@ -1,125 +0,0 @@
.panel {
min-width: 300px;
flex: 1 1 0;
display: inline-block;
box-sizing: border-box;
}
.panel:first-child {
margin-right: 24px;
}
.panel:last-child {
margin-left: 8px;
}
.panel_header, .interface {
background-color: whitesmoke;
}
.panel_header {
text-transform: uppercase;
font-family: Arial;
color: #888;
padding: 6px 6px 0;
font-size: 14px;
font-weight: bold;
display: flex;
}
.input_interfaces, .output_interfaces {
margin-bottom: 16px;
}
.interface {
height: 360px;
padding: 8px;
display: flex;
flex-flow: column;
}
.loading {
margin-left: auto;
}
.loading img {
display: none;
}
.panel_buttons {
display: flex;
margin-left: -8px;
}
.panel_buttons > * {
margin-left: 8px;
}
.submit {
display: none;
}
.submit, .clear, .panel_button {
background-color: whitesmoke !important;
flex-grow: 1;
padding: 8px !important;
box-sizing: border-box;
text-transform: uppercase;
font-weight: bold;
border: 0 none !important;
}
.submit {
background-color: #EEA45D !important;
color: white !important;
}
.submit, .flag_message {
flex-grow: 2 !important;
margin-right: 8px;
}
.flag_message {
padding: 8px !important;
background-color: whitesmoke !important;
}
.upload_zone {
font-weight: bold;
font-size: 24px;
color: #BBB;
cursor: pointer;
width: 100%;
height: 100%;
box-sizing: border-box;
display: flex;
align-items: center;
justify-content: center;
text-align: center;
line-height: 1.5em;
flex-flow: column;
}
.upload_zone img {
height: 120px;
}
.drop_zone {
border: dashed 8px #DDD;
}
.edit_holder {
display: flex;
justify-content: flex-end;
margin-bottom: 6px;
}
.interface_button {
padding: 6px;
text-transform: uppercase;
font-weight: bold;
font-size: 14px;
}
.overlay {
position: absolute;
height: 100vh;
width: 100vw;
position: fixed;
z-index: 1;
background-color: rgba(0, 0, 0, 0.7);
top: 0;
left: 0;
}
.flag.flagged {
background-color: pink !important;
}
.loading {
justify-content: center;
align-items: center;
}
.invisible {
display: none !important;
}

11
gradio-0.9.9.5/gradio/static/css/interfaces/input/checkbox_group.css
View File

@ -1,11 +0,0 @@
.checkbox_group {
display: flex;
align-items: center;
font-size: 18px;
}
.checkbox_group input, .checkbox {
margin: 0px 4px 0px 0px;
}
.checkbox_group label {
margin-right: 16px;
}

26
gradio-0.9.9.5/gradio/static/css/interfaces/input/csv.css
View File

@ -1,26 +0,0 @@
.table_holder {
max-width: 100%;
max-height: 100%;
overflow: scroll;
display: none;
}
.csv_preview {
background-color: white;
max-width: 100%;
max-height: 100%;
font-size: 12px;
font-family: monospace;
}
.csv_preview tr {
border-bottom: solid 1px black;
}
.csv_preview tr.header td {
background-color: #EEA45D;
font-weight: bold;
}
.csv_preview td {
padding: 2px 4px;
}
.csv_preview td:nth-child(even) {
background-color: whitesmoke;
}

0
gradio-0.9.9.5/gradio/static/css/interfaces/input/dropdown.css
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gradio-0.9.9.5/gradio/static/css/loading.css
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114
gradio-0.9.9.5/gradio/static/css/style.css
View File

@ -1,114 +0,0 @@
body {
font-family: 'Open Sans', sans-serif;
margin: 0;
}
button, input[type="submit"], input[type="reset"], input[type="text"], input[type="button"], select[type="submit"] {
background: none;
color: inherit;
border: none;
padding: 0;
font: inherit;
cursor: pointer;
outline: inherit;
-webkit-appearance: none;
border-radius: 0;
}
.loading_time {
font-size: large;
color: #EEA45D;
text-align: right;
padding-top: 5px;
}
nav {
text-align: center;
padding: 16px 0 4px;
}
nav img {
margin-right: auto;
height: 32px;
}
#share {
text-align: center;
margin-bottom: 10px;
font-size: 14px;
}
#share-copy {
background-color: whitesmoke;
padding: 4px;
border-radius: 2px;
}
#title {
text-align: center;
}
.container {
max-width: 1028px;
width: 100%;
margin: 0 auto;
}
.panels {
display: flex;
flex-flow: row;
flex-wrap: wrap;
justify-content: center;
}
button.primary {
color: white;
background-color: #EEA45D;
}
button.secondary {
color: black;
background-color: #F6F6F6;
}
#featured_history {
margin: 4px 30px;
}
#featured_table {
border-collapse: collapse;
border: solid 2px whitesmoke;
margin-bottom: 20px;
table-layout: fixed;
}
#featured_table div {
display: inline-block;
padding: 10px;
text-align: center;
cursor: pointer;
max-width: 200px;
max-height: 100px;
overflow-y: auto;
}
#featured_table div:nth-child(even) {
background-color: whitesmoke;
}
#featured_table div:hover {
background-color: #EEA45D;
}
#featured_history img {
height: 60px;
}
#examples table {
border-collapse: collapse;
font-family: monospace;
padding: 8px;
background-color: whitesmoke;
border-right: solid 4px whitesmoke;
border-left: solid 4px whitesmoke;
}
#examples th {
padding: 8px;
text-align: left;
font-size: 18px;
}
#examples td {
padding: 8px;
cursor: pointer;
}
#examples tbody tr:nth-child(odd) {
background-color: white;
}
#examples tbody tr:hover {
background-color: lightgray;
}

10
gradio-0.9.9.5/gradio/strings.py
View File

@ -1,10 +0,0 @@
en = {
"BETA_MESSAGE": "NOTE: Gradio is in beta stage, please report all bugs to: gradio.app@gmail.com",
"RUNNING_LOCALLY": "Running locally at: {}",
"NGROK_NO_INTERNET": "Unable to create public link for interface, please check internet connection or try "
"restarting python interpreter.",
"COLAB_NO_LOCAL": "Cannot display local interface on google colab, public link created.",
"PUBLIC_SHARE_TRUE": "To create a public link, set `share=True` in the argument to `launch()`.",
"MODEL_PUBLICLY_AVAILABLE_URL": "Model available publicly at: {} (may take up to a minute for link to be usable)",
"GENERATING_PUBLIC_LINK": "Generating public link (may take a few seconds...):",
}

107
gradio-0.9.9.5/gradio/tunneling.py
View File

@ -1,107 +0,0 @@
"""
This file provides remote port forwarding functionality using paramiko package,
Inspired by: https://github.com/paramiko/paramiko/blob/master/demos/rforward.py
"""
import select
import socket
import sys
import threading
from io import StringIO
import warnings
import paramiko
DEBUG_MODE = False
def handler(chan, host, port):
sock = socket.socket()
try:
sock.connect((host, port))
except Exception as e:
verbose("Forwarding request to {}:{} failed: {}".format(host, port, e))
return
verbose(
"Connected! Tunnel open {} -> {} -> {}".format(chan.origin_addr,
chan.getpeername(),
(host, port))
)
while True:
r, w, x = select.select([sock, chan], [], [])
if sock in r:
data = sock.recv(1024)
if len(data) == 0:
break
chan.send(data)
if chan in r:
data = chan.recv(1024)
if len(data) == 0:
break
sock.send(data)
chan.close()
sock.close()
verbose("Tunnel closed from {}".format(chan.origin_addr,))
def reverse_forward_tunnel(server_port, remote_host, remote_port, transport):
transport.request_port_forward("", server_port)
while True:
chan = transport.accept(1000)
if chan is None:
continue
thr = threading.Thread(target=handler, args=(chan, remote_host, remote_port))
thr.setDaemon(True)
thr.start()
def verbose(s):
if DEBUG_MODE:
print(s)
def create_tunnel(payload, local_server, local_server_port):
client = paramiko.SSHClient()
# client.set_missing_host_key_policy(paramiko.AutoAddPolicy())
client.set_missing_host_key_policy(paramiko.WarningPolicy())
verbose(
"Connecting to ssh host {}:{} ...".format(payload["host"], int(payload[
"port"]))
)
try:
with warnings.catch_warnings():
warnings.simplefilter("ignore")
client.connect(
hostname=payload["host"],
port=int(payload["port"]),
username=payload["user"],
pkey=paramiko.RSAKey.from_private_key(StringIO(payload["key"])),
)
except Exception as e:
print(
"*** Failed to connect to {}:{}: {}}".format(payload["host"],
int(payload["port"]), e)
)
sys.exit(1)
verbose(
"Now forwarding remote port {} to {}:{} ...".format(int(payload[
"remote_port"]),
local_server,
local_server_port)
)
thread = threading.Thread(
target=reverse_forward_tunnel,
args=(
int(payload["remote_port"]),
local_server,
local_server_port,
client.get_transport(),
),
daemon=True,
)
thread.start()
return payload["share_url"]

16
gradio-0.9.9.5/gradio/validation_data.py
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25
gradio-0.9.9.5/setup.py
View File

@ -1,25 +0,0 @@
try:
from setuptools import setup
except ImportError:
from distutils.core import setup
setup(
name='gradio',
version='0.9.9.5',
include_package_data=True,
description='Python library for easily interacting with trained machine learning models',
author='Abubakar Abid',
author_email='a12d@stanford.edu',
url='https://github.com/gradio-app/gradio-UI',
packages=['gradio'],
keywords=['machine learning', 'visualization', 'reproducibility'],
install_requires=[
'numpy',
'requests',
'paramiko',
'scipy',
'IPython',
'scikit-image',
'analytics-python',
],
)

2
gradio.egg-info/PKG-INFO
View File

@ -1,6 +1,6 @@
Metadata-Version: 1.0
Name: gradio
Version: 0.9.9.6
Version: 0.9.9.9.2
Summary: Python library for easily interacting with trained machine learning models
Home-page: https://github.com/gradio-app/gradio-UI
Author: Abubakar Abid

42
gradio/interface.py
View File

@ -20,14 +20,15 @@ from IPython import get_ipython
import sys
import weakref
import analytics
import socket
PKG_VERSION_URL = "https://gradio.app/api/pkg-version"
analytics.write_key = "uxIFddIEuuUcFLf9VgH2teTEtPlWdkNy"
analytics_url = 'https://api.gradio.app/'
hostname = socket.gethostname()
ip_address = socket.gethostbyname(hostname)
try:
ip_address = requests.get('https://api.ipify.org').text
except requests.ConnectionError:
ip_address = "No internet connection"
class Interface:
@ -101,14 +102,22 @@ class Interface:
'saliency': saliency,
'live': live,
'capture_session': capture_session,
'host_name': hostname,
'ip_address': ip_address
}
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
try:
requests.post(analytics_url + 'gradio-initiated-analytics/',
data=data)
except requests.ConnectionError:
print("gradio-initiated-analytics/ Connection Error")
pass # do not push analytics if no network
def get_config_file(self):
config = {
@ -140,7 +149,6 @@ class Interface:
return config
def process(self, raw_input):
processed_input = [input_interface.preprocess(
raw_input[i]) for i, input_interface in
@ -210,7 +218,7 @@ class Interface:
requests.post(analytics_url + 'gradio-error-analytics/',
data=data)
except requests.ConnectionError:
print("gradio-error-analytics/ Connection Error")
pass # do not push analytics if no network
if self.verbose:
print("\n----------")
print(
@ -226,7 +234,7 @@ class Interface:
requests.post(analytics_url + 'gradio-error-analytics/',
data=data)
except requests.ConnectionError:
print("gradio-error-analytics/ Connection Error")
pass # do not push analytics if no network
if self.verbose:
print("\n----------")
print(
@ -258,14 +266,6 @@ class Interface:
# if validate and not self.validate_flag:
# self.validate()
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
output_directory = tempfile.mkdtemp()
# Set up a port to serve the directory containing the static files with interface.
server_port, httpd = networking.start_simple_server(self, output_directory, self.server_name)
@ -287,7 +287,7 @@ class Interface:
requests.post(analytics_url + 'gradio-error-analytics/',
data=data)
except requests.ConnectionError:
print("Connection Error")
pass # do not push analytics if no network
pass
try:
@ -321,7 +321,7 @@ class Interface:
requests.post(analytics_url + 'gradio-error-analytics/',
data=data)
except requests.ConnectionError:
print("Connection Error")
pass # do not push analytics if no network
share_url = None
if self.verbose:
print(strings.en["NGROK_NO_INTERNET"])
@ -363,6 +363,7 @@ class Interface:
is_colab
): # Embed the remote interface page if on google colab;
# otherwise, embed the local page.
print("Interface loading below...")
while not networking.url_ok(share_url):
time.sleep(1)
display(IFrame(share_url, width=1000, height=500))
@ -393,14 +394,13 @@ class Interface:
'is_google_colab': is_colab,
'is_sharing_on': share,
'share_url': share_url,
'host_name': hostname,
'ip_address': ip_address
}
try:
requests.post(analytics_url + 'gradio-hosted-launched-analytics/',
requests.post(analytics_url + 'gradio-launched-analytics/',
data=data)
except requests.ConnectionError:
print("Connection Error")
pass # do not push analytics if no network
return httpd, path_to_local_server, share_url
@classmethod

12
gradio/networking.py
View File

@ -15,6 +15,7 @@ import urllib.request
from shutil import copyfile
import requests
import sys
import analytics
INITIAL_PORT_VALUE = (
@ -38,6 +39,8 @@ ASSOCIATION_PATH_IN_ROOT = "apple-app-site-association"
FLAGGING_DIRECTORY = 'static/flagged/'
FLAGGING_FILENAME = 'data.txt'
analytics.write_key = "uxIFddIEuuUcFLf9VgH2teTEtPlWdkNy"
analytics_url = 'https://api.gradio.app/'
def build_template(temp_dir):
@ -159,6 +162,15 @@ def serve_files_in_background(interface, port, directory_to_serve=None, server_n
# Prepare return json dictionary.
self.wfile.write(json.dumps(output).encode())
data = {'input_interface': interface.input_interfaces,
'output_interface': interface.output_interfaces,
}
try:
requests.post(
analytics_url + 'gradio-prediction-analytics/',
data=data)
except requests.ConnectionError:
pass # do not push analytics if no network
elif self.path == "/api/flag/":
self._set_headers()

1
gradio/static/css/style.css
View File

@ -18,7 +18,6 @@ button, input[type="submit"], input[type="reset"], input[type="text"], input[typ
font-size: large;
color: #EEA45D;
text-align: right;
padding-top: 5px;
}
nav {

17
gradio/templates/index.html
View File

@ -1,6 +1,21 @@
<html lang="en">
<head>
<!-- <iframe src=""></iframe> -->
<!-- Global site tag (gtag.js) - Google Analytics -->
<script async
src="https://www.googletagmanager.com/gtag/js?id=UA-156449732-1"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag() {
dataLayer.push(arguments);
}
gtag('js', new Date());
gtag('config', 'UA-156449732-1');
</script>
<!-- <iframe src=""></iframe> -->
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
<title>Gradio</title>
<link rel="stylesheet" href="../static/css/style.css">

2
setup.py
View File

@ -5,7 +5,7 @@ except ImportError:
setup(
name='gradio',
version='0.9.9.6',
version='0.9.9.9.2',
include_package_data=True,
description='Python library for easily interacting with trained machine learning models',
author='Abubakar Abid',