2022-04-26T21:38:43,893 Created temporary directory: /tmp/pip-ephem-wheel-cache-ywb2q3_z 2022-04-26T21:38:43,900 Created temporary directory: /tmp/pip-req-tracker-u4sd56kh 2022-04-26T21:38:43,900 Initialized build tracking at /tmp/pip-req-tracker-u4sd56kh 2022-04-26T21:38:43,901 Created build tracker: /tmp/pip-req-tracker-u4sd56kh 2022-04-26T21:38:43,901 Entered build tracker: /tmp/pip-req-tracker-u4sd56kh 2022-04-26T21:38:43,903 Created temporary directory: /tmp/pip-wheel-j3i6lblr 2022-04-26T21:38:43,963 Looking in indexes: https://pypi.org/simple, https://www.piwheels.org/simple 2022-04-26T21:38:43,966 2 location(s) to search for versions of gradio: 2022-04-26T21:38:43,966 * https://pypi.org/simple/gradio/ 2022-04-26T21:38:43,966 * https://www.piwheels.org/simple/gradio/ 2022-04-26T21:38:43,967 Fetching project page and analyzing links: https://pypi.org/simple/gradio/ 2022-04-26T21:38:43,969 Getting page https://pypi.org/simple/gradio/ 2022-04-26T21:38:43,973 Found index url 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https://www.piwheels.org/simple/gradio/gradio-2.9.4-py3-none-any.whl#sha256=1624d4970001d2daebebd0c9b601e3809498c6cad64e9a6a116c5971e122a37a (from https://www.piwheels.org/simple/gradio/) 2022-04-26T21:38:45,853 Skipping link: No binaries permitted for gradio: https://www.piwheels.org/simple/gradio/gradio-2.9.3-py3-none-any.whl#sha256=4d1ea221b2c91ee98c8447fae9d8aa5374f73afb35f8426b066394415000b014 (from https://www.piwheels.org/simple/gradio/) 2022-04-26T21:38:45,854 Skipping link: No binaries permitted for gradio: https://www.piwheels.org/simple/gradio/gradio-2.9.2-py3-none-any.whl#sha256=c048025761fa94fc83db71726bb8de1339ba05e4080b4de340d10c3fe27a85ef (from https://www.piwheels.org/simple/gradio/) 2022-04-26T21:38:45,855 Skipping link: No binaries permitted for gradio: https://www.piwheels.org/simple/gradio/gradio-2.9.1-py3-none-any.whl#sha256=f2411ebd1487e3c279c49b7191558cfdf502534a83aafe059d7566a3ef116845 (from https://www.piwheels.org/simple/gradio/) 2022-04-26T21:38:45,856 Skipping link: No binaries permitted for gradio: https://www.piwheels.org/simple/gradio/gradio-2.9.0-py3-none-any.whl#sha256=c840d44d712c9d89597ac77b46cd3dd93ac5c3821b862ca1f91628257a210ef6 (from https://www.piwheels.org/simple/gradio/) 2022-04-26T21:38:45,856 Skipping link: No binaries permitted for gradio: https://www.piwheels.org/simple/gradio/gradio-2.9.0b8-py3-none-any.whl#sha256=bc7d87725d38e73738f59224fb478a20592e2bb8ab9fd2c691b0602e5d86ae18 (from https://www.piwheels.org/simple/gradio/) 2022-04-26T21:38:45,857 Skipping link: No binaries permitted for gradio: https://www.piwheels.org/simple/gradio/gradio-2.9.0b7-py3-none-any.whl#sha256=a2c743f83ec3a0f3482d4541df095925806fc9b9870861c0e8177cbad1fe06f5 (from https://www.piwheels.org/simple/gradio/) 2022-04-26T21:38:45,858 Skipping link: No binaries permitted for gradio: https://www.piwheels.org/simple/gradio/gradio-2.9.0b6-py3-none-any.whl#sha256=da2b5b41964a4c0ee6d135c64a5f94c060b0143496d57fbf95204724672a1991 (from https://www.piwheels.org/simple/gradio/) 2022-04-26T21:38:45,858 Skipping link: No binaries permitted for gradio: https://www.piwheels.org/simple/gradio/gradio-2.9.0b5-py3-none-any.whl#sha256=9fa8fd2534558cd2132b80124a7ef009339ae4d79ad6248dc76bb9b6338a631a (from https://www.piwheels.org/simple/gradio/) 2022-04-26T21:38:45,859 Skipping link: No binaries permitted for gradio: https://www.piwheels.org/simple/gradio/gradio-2.9.0b3-py3-none-any.whl#sha256=a3bb4657afec508ff9c1f2ef494a1b0c046425d770aeb8f484a6c67fd47dbfdf (from https://www.piwheels.org/simple/gradio/) 2022-04-26T21:38:45,860 Skipping link: No binaries permitted for gradio: https://www.piwheels.org/simple/gradio/gradio-2.9.0b2-py3-none-any.whl#sha256=2f7ecdae18ffd1db161bbd44b9aee8327967fda64cb850bc17daa2decc3f5fac (from https://www.piwheels.org/simple/gradio/) 2022-04-26T21:38:45,860 Skipping link: No binaries permitted for gradio: 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Skipping link: No binaries permitted for gradio: https://www.piwheels.org/simple/gradio/gradio-2.8.12-py3-none-any.whl#sha256=f40a1003d1ab5f8efec85df0aa59ec677d9d113bf2596c058f099af720f52e67 (from https://www.piwheels.org/simple/gradio/) 2022-04-26T21:38:45,863 Skipping link: No binaries permitted for gradio: https://www.piwheels.org/simple/gradio/gradio-2.8.11-py3-none-any.whl#sha256=d5219018992fe3a04cf21835a0d3dd477cff5023c07335caac5537842f3708b7 (from https://www.piwheels.org/simple/gradio/) 2022-04-26T21:38:45,864 Skipping link: No binaries permitted for gradio: https://www.piwheels.org/simple/gradio/gradio-2.8.10-py3-none-any.whl#sha256=58de00eee1ff76b070b93e66feb21442c1fb7c669594979c2588fa073fcb2fce (from https://www.piwheels.org/simple/gradio/) 2022-04-26T21:38:45,865 Skipping link: No binaries permitted for gradio: https://www.piwheels.org/simple/gradio/gradio-2.8.9-py3-none-any.whl#sha256=239d0362c8e9a9d99cf849616c4af86196ac63e8955d932fb165804274e947de (from https://www.piwheels.org/simple/gradio/) 2022-04-26T21:38:45,865 Skipping link: No binaries permitted for gradio: https://www.piwheels.org/simple/gradio/gradio-2.8.8-py3-none-any.whl#sha256=f7ba2c257ddcb342b6f15b279ed9355e01a8d0b784fc15b9a67ad3f9be828b9b (from https://www.piwheels.org/simple/gradio/) 2022-04-26T21:38:45,866 Skipping link: No binaries permitted for gradio: https://www.piwheels.org/simple/gradio/gradio-2.8.7-py3-none-any.whl#sha256=bdf3955dfd0c5df1765e9ec4953f98922c1d785f39046182820c53b3f0d27df9 (from https://www.piwheels.org/simple/gradio/) 2022-04-26T21:38:45,867 Skipping link: No binaries permitted for gradio: https://www.piwheels.org/simple/gradio/gradio-2.8.6-py3-none-any.whl#sha256=09a06a0d72e3270434e44997365041e51bcfd8bda790d6b9e0861c2efa18191f (from https://www.piwheels.org/simple/gradio/) 2022-04-26T21:38:45,867 Skipping link: No binaries permitted for gradio: 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2022-04-26T21:38:46,184 Created temporary directory: /tmp/pip-unpack-_oo0cbv2 2022-04-26T21:38:47,111 Downloading gradio-2.9.0b9.tar.gz (3.0 MB) 2022-04-26T21:38:50,083 Added gradio==2.9.0b9 from https://files.pythonhosted.org/packages/6c/6d/6d27f2737e1873b6e4eb0469f9618ab04e5689af89fb3f91a90bfa05c1fc/gradio-2.9.0b9.tar.gz#sha256=d7095c8b9f31cd63be8ff64cfb629d60fe17e5706aef0919420624a8ffb4a69e to build tracker '/tmp/pip-req-tracker-u4sd56kh' 2022-04-26T21:38:50,085 Running setup.py (path:/tmp/pip-wheel-j3i6lblr/gradio_7cb2db11246741818da3aa2beedbe62d/setup.py) egg_info for package gradio 2022-04-26T21:38:50,087 Created temporary directory: /tmp/pip-pip-egg-info-m4jcrqcc 2022-04-26T21:38:50,088 Running command python setup.py egg_info 2022-04-26T21:38:51,924 [![CircleCI](https://circleci.com/gh/gradio-app/gradio.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) [![codecov](https://codecov.io/gh/gradio-app/gradio/branch/master/graph/badge.svg?token=NNVPX9KEGS)](https://codecov.io/gh/gradio-app/gradio) [![PyPI - Downloads](https://img.shields.io/pypi/dm/gradio)](https://pypi.org/project/gradio/) [![Twitter Follow](https://img.shields.io/twitter/follow/gradio.svg?style=social&label=Follow)](https://twitter.com/gradio) 2022-04-26T21:38:51,926 # Welcome to Gradio 2022-04-26T21:38:51,927 Quickly create beautiful user interfaces around your machine learning models. Gradio (pronounced GRAY-dee-oh) makes it easy for you to demo your model in your browser or let people "try it out" by dragging-and-dropping in their own images, pasting text, recording their own voice, etc. and seeing what the model outputs. 2022-04-26T21:38:51,928 ![Interface montage](website/homepage/src/assets/img/montage.gif) 2022-04-26T21:38:51,929 Gradio is useful for: 2022-04-26T21:38:51,930 * **Demoing** your machine learning models for clients / collaborators / users / students 2022-04-26T21:38:51,931 * **Deploying** your models quickly with automatic shareable links and getting feedback on model performance 2022-04-26T21:38:51,933 * **Debugging** your model interactively during development using built-in manipulation and interpretation tools 2022-04-26T21:38:51,934 **You can find an interactive version of the following Getting Started at [https://gradio.app/getting_started](https://gradio.app/getting_started).** 2022-04-26T21:38:51,936 ## Getting Started 2022-04-26T21:38:51,937 **Prerequisite**: Python 3.7+ and that's it! 2022-04-26T21:38:51,938 ### Quick Start 2022-04-26T21:38:51,939 To get Gradio running with a simple "Hello, World" example, follow these three steps: 2022-04-26T21:38:51,940 1. Install Gradio from pip. 2022-04-26T21:38:51,942 ```bash 2022-04-26T21:38:51,942 pip install gradio 2022-04-26T21:38:51,943 ``` 2022-04-26T21:38:51,944 2. Run the code below as a Python script or in a Python notebook (or in a [colab notebook](https://colab.research.google.com/drive/18ODkJvyxHutTN0P5APWyGFO_xwNcgHDZ?usp=sharing)). 2022-04-26T21:38:51,945 ```python 2022-04-26T21:38:51,946 import gradio as gr 2022-04-26T21:38:51,948 def greet(name): 2022-04-26T21:38:51,948 return "Hello " + name + "!!" 2022-04-26T21:38:51,950 iface = gr.Interface(fn=greet, inputs="text", outputs="text") 2022-04-26T21:38:51,951 iface.launch() 2022-04-26T21:38:51,952 ``` 2022-04-26T21:38:51,953 3. The interface below will appear automatically within the Python notebook, or pop in a browser on [http://localhost:7860](http://localhost:7860/) if running from a script. 2022-04-26T21:38:51,954 ![hello_world interface](demo/hello_world/screenshot.gif) 2022-04-26T21:38:51,955 ### Understanding the `Interface` class 2022-04-26T21:38:51,957 Gradio can wrap almost any Python function with an easy-to-use user interface. In the example above, we saw a simple text-based function. But the function could be anything from image enhancer to a tax calculator to (most commonly) the prediction function of a pretrained machine learning model. 2022-04-26T21:38:51,958 The core `Interface` class is initialized with three parameters: 2022-04-26T21:38:51,959 - `fn`: the function to wrap 2022-04-26T21:38:51,960 - `inputs`: the input component type(s), e.g. `"image"` or `"audio"` ([see docs for complete list](/docs)) 2022-04-26T21:38:51,960 - `outputs`: the output component type(s) e.g. `"image"` or `"label"` ([see docs for complete list](/docs)) 2022-04-26T21:38:51,962 With these three arguments, we can quickly create interfaces and `launch()` them. But what if you want to change how the UI components look or behave? 2022-04-26T21:38:51,963 ### Customizable Components 2022-04-26T21:38:51,964 Let's say we want to customize the input text field - for example, we wanted it to be larger and have a text hint. If we use the actual input class for `Textbox` instead of using the string shortcut, we have access to much more customizability. To see a list of all the components we support and how you can customize them, check out the [Docs](https://gradio.app/docs). 2022-04-26T21:38:51,965 ```python 2022-04-26T21:38:51,966 import gradio as gr 2022-04-26T21:38:51,968 def greet(name): 2022-04-26T21:38:51,968 return "Hello " + name + "!" 2022-04-26T21:38:51,970 iface = gr.Interface( 2022-04-26T21:38:51,971 fn=greet, 2022-04-26T21:38:51,971 inputs=gr.inputs.Textbox(lines=2, placeholder="Name Here..."), 2022-04-26T21:38:51,972 outputs="text", 2022-04-26T21:38:51,972 ) 2022-04-26T21:38:51,973 iface.launch() 2022-04-26T21:38:51,974 ``` 2022-04-26T21:38:51,975 ![hello_world_2 interface](demo/hello_world_2/screenshot.gif) 2022-04-26T21:38:51,976 ### Multiple Inputs and Outputs 2022-04-26T21:38:51,977 Let's say we had a much more complex function, with multiple inputs and outputs. In the example below, we have a function that takes a string, boolean, and number, and returns a string and number. Take a look how we pass a list of input and output components. 2022-04-26T21:38:51,978 ```python 2022-04-26T21:38:51,978 import gradio as gr 2022-04-26T21:38:51,980 def greet(name, is_morning, temperature): 2022-04-26T21:38:51,980 salutation = "Good morning" if is_morning else "Good evening" 2022-04-26T21:38:51,981 greeting = "%s %s. It is %s degrees today" % (salutation, name, temperature) 2022-04-26T21:38:51,982 celsius = (temperature - 32) * 5 / 9 2022-04-26T21:38:51,982 return greeting, round(celsius, 2) 2022-04-26T21:38:51,984 iface = gr.Interface( 2022-04-26T21:38:51,984 fn=greet, 2022-04-26T21:38:51,985 inputs=["text", "checkbox", gr.inputs.Slider(0, 100)], 2022-04-26T21:38:51,985 outputs=["text", "number"], 2022-04-26T21:38:51,986 ) 2022-04-26T21:38:51,987 iface.launch() 2022-04-26T21:38:51,988 ``` 2022-04-26T21:38:51,988 ![hello_world_3 interface](demo/hello_world_3/screenshot.gif) 2022-04-26T21:38:51,990 We simply wrap the components in a list. Each component in the `inputs` list corresponds to one of the parameters of the function, in order. Each component in the `outputs` list corresponds to one of the values returned by the function, again in order. 2022-04-26T21:38:51,991 ### Working with Images 2022-04-26T21:38:51,992 Let's try an image-to-image function. When using the `Image` component, your function will receive a numpy array of your specified size, with the shape `(width, height, 3)`, where the last dimension represents the RGB values. We'll return an image as well in the form of a numpy array. 2022-04-26T21:38:51,993 ```python 2022-04-26T21:38:51,994 import numpy as np 2022-04-26T21:38:51,995 import gradio as gr 2022-04-26T21:38:51,996 def sepia(input_img): 2022-04-26T21:38:51,997 sepia_filter = np.array( 2022-04-26T21:38:51,998 [[0.393, 0.769, 0.189], [0.349, 0.686, 0.168], [0.272, 0.534, 0.131]] 2022-04-26T21:38:51,998 ) 2022-04-26T21:38:51,999 sepia_img = input_img.dot(sepia_filter.T) 2022-04-26T21:38:51,1000 sepia_img /= sepia_img.max() 2022-04-26T21:38:52,000 return sepia_img 2022-04-26T21:38:52,002 iface = gr.Interface(sepia, gr.inputs.Image(shape=(200, 200)), "image") 2022-04-26T21:38:52,003 iface.launch() 2022-04-26T21:38:52,004 ``` 2022-04-26T21:38:52,005 ![sepia_filter interface](demo/sepia_filter/screenshot.gif) 2022-04-26T21:38:52,006 Additionally, our `Image` input interface comes with an 'edit' button which opens tools for cropping, flipping, rotating, drawing over, and applying filters to images. We've found that manipulating images in this way will often reveal hidden flaws in a model. 2022-04-26T21:38:52,007 In addition to images, Gradio supports other media input types, such as audio or video uploads, as well as many output components. Read about these in the [Docs](https://gradio.app/docs). 2022-04-26T21:38:52,008 ### Working with DataFrames and Graphs 2022-04-26T21:38:52,010 You can use Gradio to support inputs and outputs from your typical data libraries, such as numpy arrays, pandas dataframes, and plotly graphs. Take a look at the demo below (ignore the complicated data manipulation in the function!) 2022-04-26T21:38:52,011 ```python 2022-04-26T21:38:52,011 import matplotlib.pyplot as plt 2022-04-26T21:38:52,012 import numpy as np 2022-04-26T21:38:52,013 import pandas as pd 2022-04-26T21:38:52,014 import gradio as gr 2022-04-26T21:38:52,016 def sales_projections(employee_data): 2022-04-26T21:38:52,016 sales_data = employee_data.iloc[:, 1:4].astype("int").to_numpy() 2022-04-26T21:38:52,017 regression_values = np.apply_along_axis( 2022-04-26T21:38:52,017 lambda row: np.array(np.poly1d(np.polyfit([0, 1, 2], row, 2))), 0, sales_data 2022-04-26T21:38:52,018 ) 2022-04-26T21:38:52,019 projected_months = np.repeat( 2022-04-26T21:38:52,019 np.expand_dims(np.arange(3, 12), 0), len(sales_data), axis=0 2022-04-26T21:38:52,020 ) 2022-04-26T21:38:52,020 projected_values = np.array( 2022-04-26T21:38:52,021 [ 2022-04-26T21:38:52,022 month * month * regression[0] + month * regression[1] + regression[2] 2022-04-26T21:38:52,022 for month, regression in zip(projected_months, regression_values) 2022-04-26T21:38:52,023 ] 2022-04-26T21:38:52,023 ) 2022-04-26T21:38:52,024 plt.plot(projected_values.T) 2022-04-26T21:38:52,024 plt.legend(employee_data["Name"]) 2022-04-26T21:38:52,025 return employee_data, plt.gcf(), regression_values 2022-04-26T21:38:52,027 iface = gr.Interface( 2022-04-26T21:38:52,027 sales_projections, 2022-04-26T21:38:52,028 gr.inputs.Dataframe( 2022-04-26T21:38:52,028 headers=["Name", "Jan Sales", "Feb Sales", "Mar Sales"], 2022-04-26T21:38:52,029 default=[["Jon", 12, 14, 18], ["Alice", 14, 17, 2], ["Sana", 8, 9.5, 12]], 2022-04-26T21:38:52,030 ), 2022-04-26T21:38:52,030 ["dataframe", "plot", "numpy"], 2022-04-26T21:38:52,031 description="Enter sales figures for employees to predict sales trajectory over year.", 2022-04-26T21:38:52,031 ) 2022-04-26T21:38:52,032 iface.launch() 2022-04-26T21:38:52,033 ``` 2022-04-26T21:38:52,034 ![sales_projections interface](demo/sales_projections/screenshot.gif) 2022-04-26T21:38:52,035 ### Example Inputs 2022-04-26T21:38:52,036 You can provide example data that a user can easily load into the model. This can be helpful to demonstrate the types of inputs the model expects, as well as to provide a way to explore your dataset in conjunction with your model. To load example data, you provide a **nested list** to the `examples=` keyword argument of the Interface constructor. Each sublist within the outer list represents a data sample, and each element within the sublist represents an input for each input component. The format of example data for each component is specified in the [Docs](https://gradio.app/docs). 2022-04-26T21:38:52,037 ```python 2022-04-26T21:38:52,038 import gradio as gr 2022-04-26T21:38:52,039 def calculator(num1, operation, num2): 2022-04-26T21:38:52,040 if operation == "add": 2022-04-26T21:38:52,040 return num1 + num2 2022-04-26T21:38:52,041 elif operation == "subtract": 2022-04-26T21:38:52,041 return num1 - num2 2022-04-26T21:38:52,042 elif operation == "multiply": 2022-04-26T21:38:52,043 return num1 * num2 2022-04-26T21:38:52,043 elif operation == "divide": 2022-04-26T21:38:52,044 return num1 / num2 2022-04-26T21:38:52,045 iface = gr.Interface( 2022-04-26T21:38:52,045 calculator, 2022-04-26T21:38:52,046 ["number", gr.inputs.Radio(["add", "subtract", "multiply", "divide"]), "number"], 2022-04-26T21:38:52,046 "number", 2022-04-26T21:38:52,047 examples=[ 2022-04-26T21:38:52,047 [5, "add", 3], 2022-04-26T21:38:52,048 [4, "divide", 2], 2022-04-26T21:38:52,048 [-4, "multiply", 2.5], 2022-04-26T21:38:52,049 [0, "subtract", 1.2], 2022-04-26T21:38:52,049 ], 2022-04-26T21:38:52,049 title="test calculator", 2022-04-26T21:38:52,050 description="heres a sample toy calculator. enjoy!", 2022-04-26T21:38:52,051 flagging_options=["this", "or", "that"], 2022-04-26T21:38:52,051 ) 2022-04-26T21:38:52,052 iface.launch() 2022-04-26T21:38:52,053 ``` 2022-04-26T21:38:52,054 ![calculator interface](demo/calculator/screenshot.gif) 2022-04-26T21:38:52,055 You can load a large dataset into the examples to browse and interact with the dataset through Gradio. The examples will be automatically paginated (you can configure this through the `examples_per_page` argument of Interface) and you can use CTRL + arrow keys to navigate through the examples quickly. 2022-04-26T21:38:52,056 ### Live Interfaces 2022-04-26T21:38:52,057 You can make interfaces automatically refresh by setting `live=True` in the interface. Now the interface will recalculate as soon as the user input changes. 2022-04-26T21:38:52,058 ```python 2022-04-26T21:38:52,058 import gradio as gr 2022-04-26T21:38:52,060 def calculator(num1, operation, num2): 2022-04-26T21:38:52,061 if operation == "add": 2022-04-26T21:38:52,061 return num1 + num2 2022-04-26T21:38:52,062 elif operation == "subtract": 2022-04-26T21:38:52,062 return num1 - num2 2022-04-26T21:38:52,063 elif operation == "multiply": 2022-04-26T21:38:52,063 return num1 * num2 2022-04-26T21:38:52,064 elif operation == "divide": 2022-04-26T21:38:52,064 return num1 / num2 2022-04-26T21:38:52,066 iface = gr.Interface( 2022-04-26T21:38:52,066 calculator, 2022-04-26T21:38:52,067 ["number", gr.inputs.Radio(["add", "subtract", "multiply", "divide"]), "number"], 2022-04-26T21:38:52,068 "number", 2022-04-26T21:38:52,068 live=True, 2022-04-26T21:38:52,069 ) 2022-04-26T21:38:52,070 iface.launch() 2022-04-26T21:38:52,071 ``` 2022-04-26T21:38:52,072 ![calculator_live interface](demo/calculator_live/screenshot.gif) 2022-04-26T21:38:52,073 Note there is no submit button, because the interface resubmits automatically on change. 2022-04-26T21:38:52,074 ### Using State 2022-04-26T21:38:52,075 Your function may use data that persists beyond a single function call. If the data is something accessible to all function calls and all users, you can create a global variable outside the function call and access it inside the function. For example, you may load a large model outside the function and use it inside the function so that every function call does not need to reload the model. 2022-04-26T21:38:52,076 Another type of data persistence Gradio supports is session **state**, where data persists across multiple submits within a page load. However, data is *not* shared between different users of your model. To store data in a session state, you need to do three things: (1) Pass in an extra parameter into your function, which represents the state of the interface. (2) At the end of the function, return the updated value of the state as an extra return value (3) Add the `'state'` input and `'state'` output components when creating your `Interface`. See the chatbot example below: 2022-04-26T21:38:52,077 ```python 2022-04-26T21:38:52,078 import random 2022-04-26T21:38:52,079 import gradio as gr 2022-04-26T21:38:52,080 def chat(message, history): 2022-04-26T21:38:52,081 history = history or [] 2022-04-26T21:38:52,081 if message.startswith("How many"): 2022-04-26T21:38:52,082 response = random.randint(1, 10) 2022-04-26T21:38:52,082 elif message.startswith("How"): 2022-04-26T21:38:52,083 response = random.choice(["Great", "Good", "Okay", "Bad"]) 2022-04-26T21:38:52,083 elif message.startswith("Where"): 2022-04-26T21:38:52,084 response = random.choice(["Here", "There", "Somewhere"]) 2022-04-26T21:38:52,084 else: 2022-04-26T21:38:52,085 response = "I don't know" 2022-04-26T21:38:52,085 history.append((message, response)) 2022-04-26T21:38:52,086 html = "
" 2022-04-26T21:38:52,086 for user_msg, resp_msg in history: 2022-04-26T21:38:52,087 html += f"
{user_msg}
" 2022-04-26T21:38:52,087 html += f"
{resp_msg}
" 2022-04-26T21:38:52,088 html += "
" 2022-04-26T21:38:52,088 return html, history 2022-04-26T21:38:52,090 iface = gr.Interface( 2022-04-26T21:38:52,090 chat, 2022-04-26T21:38:52,091 ["text", "state"], 2022-04-26T21:38:52,091 ["html", "state"], 2022-04-26T21:38:52,092 css=""" 2022-04-26T21:38:52,092 .chatbox {display:flex;flex-direction:column} 2022-04-26T21:38:52,093 .user_msg, .resp_msg {padding:4px;margin-bottom:4px;border-radius:4px;width:80%} 2022-04-26T21:38:52,093 .user_msg {background-color:cornflowerblue;color:white;align-self:start} 2022-04-26T21:38:52,094 .resp_msg {background-color:lightgray;align-self:self-end} 2022-04-26T21:38:52,095 """, 2022-04-26T21:38:52,095 allow_screenshot=False, 2022-04-26T21:38:52,096 allow_flagging="never", 2022-04-26T21:38:52,096 ) 2022-04-26T21:38:52,097 iface.launch() 2022-04-26T21:38:52,098 ``` 2022-04-26T21:38:52,098 ![chatbot interface](demo/chatbot/screenshot.gif) 2022-04-26T21:38:52,099 Notice how the state persists across submits within each page, but the state is not shared between the two pages. Some more points to note: you can pass in a default value to the state parameter, which is used as the initial value of the state. The state must be a something that can be serialized to a JSON format (e.g. a dictionary, a list, or a single value. Typically, objects will not work). 2022-04-26T21:38:52,100 ### Flagging 2022-04-26T21:38:52,102 Underneath the output interfaces, there is a button marked "Flag". When a user testing your model sees input with interesting output, such as erroneous or unexpected model behaviour, they can flag the input for the interface creator to review. Within the directory provided by the `flagging_dir=` argument to the Interface constructor, a CSV file will log the flagged inputs. If the interface involves file data, such as for Image and Audio components, folders will be created to store those flagged data as well. 2022-04-26T21:38:52,103 For example, with the calculator interface shown above, we would have the flagged data stored in the flagged directory shown below: 2022-04-26T21:38:52,104 ```directory 2022-04-26T21:38:52,104 +-- calculator.py 2022-04-26T21:38:52,105 +-- flagged/ 2022-04-26T21:38:52,105 | +-- logs.csv 2022-04-26T21:38:52,105 ``` 2022-04-26T21:38:52,107 *flagged/logs.csv* 2022-04-26T21:38:52,107 ```csv 2022-04-26T21:38:52,108 num1,operation,num2,Output 2022-04-26T21:38:52,108 5,add,7,12 2022-04-26T21:38:52,109 6,subtract,1.5,4.5 2022-04-26T21:38:52,109 ``` 2022-04-26T21:38:52,110 With the sepia interface shown above, we would have the flagged data stored in the flagged directory shown below: 2022-04-26T21:38:52,111 ```directory 2022-04-26T21:38:52,112 +-- sepia.py 2022-04-26T21:38:52,112 +-- flagged/ 2022-04-26T21:38:52,113 | +-- logs.csv 2022-04-26T21:38:52,113 | +-- im/ 2022-04-26T21:38:52,114 | | +-- 0.png 2022-04-26T21:38:52,114 | | +-- 1.png 2022-04-26T21:38:52,115 | +-- Output/ 2022-04-26T21:38:52,115 | | +-- 0.png 2022-04-26T21:38:52,116 | | +-- 1.png 2022-04-26T21:38:52,116 ``` 2022-04-26T21:38:52,117 *flagged/logs.csv* 2022-04-26T21:38:52,118 ```csv 2022-04-26T21:38:52,118 im,Output 2022-04-26T21:38:52,119 im/0.png,Output/0.png 2022-04-26T21:38:52,119 im/1.png,Output/1.png 2022-04-26T21:38:52,120 ``` 2022-04-26T21:38:52,121 You can review these flagged inputs by manually exploring the flagging directory, or load them into the examples of the Gradio interface by pointing the `examples=` argument to the flagged directory. If you wish for the user to provide a reason for flagging, you can pass a list of strings to the `flagging_options` argument of Interface. Users will have to select one of the strings when flagging, which will be saved as an additional column to the CSV. 2022-04-26T21:38:52,122 ### Sharing Interfaces Publicly 2022-04-26T21:38:52,123 Interfaces can be easily shared publicly by setting `share=True` in the `launch()` method. Like this: 2022-04-26T21:38:52,124 ```python 2022-04-26T21:38:52,125 gr.Interface(classify_image, "image", "label").launch(share=True) 2022-04-26T21:38:52,125 ``` 2022-04-26T21:38:52,126 This generates a public, shareable link that you can send to anybody! When you send this link, the user on the other side can try out the model in their browser. Because the processing happens on your device (as long as your device stays on!), you don't have to worry about any packaging any dependencies. If you're working out of colab notebook, a share link is always automatically created. It usually looks something like this: **XXXXX.gradio.app**. Although the link is served through a gradio link, we are only a proxy for your local server, and do not store any data sent through the interfaces. 2022-04-26T21:38:52,127 Keep in mind, however, that these links are publicly accessible, meaning that anyone can use your model for prediction! Therefore, make sure not to expose any sensitive information through the functions you write, or allow any critical changes to occur on your device. If you set `share=False` (the default), only a local link is created, which can be shared by [port-forwarding](https://www.ssh.com/ssh/tunneling/example) with specific users. 2022-04-26T21:38:52,128 Share links expire after 72 hours. For permanent hosting, see Hosting Gradio Apps on Spaces below. 2022-04-26T21:38:52,129 ![Sharing diagram](website/homepage/src/assets/img/sharing.svg) 2022-04-26T21:38:52,130 ### Hosting Gradio Apps on Spaces 2022-04-26T21:38:52,131 Huggingface provides the infrastructure to permanently host your Gradio model on the internet, for free! You can either drag and drop a folder containing your Gradio model and all related files, or you can point HF Spaces to your Git repository and HP Spaces will pull the Gradio interface from there. See [Huggingface Spaces](http://huggingface.co/spaces/) for more information. 2022-04-26T21:38:52,132 ![Hosting Demo](website/homepage/src/assets/img/hf_demo.gif) 2022-04-26T21:38:52,133 ## Advanced Features 2022-04-26T21:38:52,134 2022-04-26T21:38:52,135 Here, we go through several advanced functionalities that your Gradio demo can include without you needing to write much more code! 2022-04-26T21:38:52,136 ### Authentication 2022-04-26T21:38:52,137 You may wish to put an authentication page in front of your interface to limit who can open your interface. With the `auth=` keyword argument in the `launch()` method, you can pass a list of acceptable username/password tuples; or, for more complex authentication handling, you can even pass a function that takes a username and password as arguments, and returns True to allow authentication, False otherwise. Here's an example that provides password-based authentication for a single user named "admin": 2022-04-26T21:38:52,138 ```python 2022-04-26T21:38:52,139 gr.Interface(fn=classify_image, inputs=image, outputs=label).launch(auth=("admin", "pass1234")) 2022-04-26T21:38:52,139 ``` 2022-04-26T21:38:52,140 ### Interpreting your Predictions 2022-04-26T21:38:52,141 Most models are black boxes such that the internal logic of the function is hidden from the end user. To encourage transparency, we've made it very easy to add interpretation to your model by simply setting the `interpretation` keyword in the `Interface` class to `default`. This allows your users to understand what parts of the input are responsible for the output. Take a look at the simple interface below which shows an image classifier that also includes interpretation: 2022-04-26T21:38:52,142 ```python 2022-04-26T21:38:52,143 import requests 2022-04-26T21:38:52,143 import tensorflow as tf 2022-04-26T21:38:52,144 import gradio as gr 2022-04-26T21:38:52,145 inception_net = tf.keras.applications.MobileNetV2() # load the model 2022-04-26T21:38:52,146 # Download human-readable labels for ImageNet. 2022-04-26T21:38:52,147 response = requests.get("https://git.io/JJkYN") 2022-04-26T21:38:52,148 labels = response.text.split("\n") 2022-04-26T21:38:52,149 def classify_image(inp): 2022-04-26T21:38:52,150 inp = inp.reshape((-1, 224, 224, 3)) 2022-04-26T21:38:52,150 inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp) 2022-04-26T21:38:52,151 prediction = inception_net.predict(inp).flatten() 2022-04-26T21:38:52,151 return {labels[i]: float(prediction[i]) for i in range(1000)} 2022-04-26T21:38:52,153 image = gr.inputs.Image(shape=(224, 224)) 2022-04-26T21:38:52,153 label = gr.outputs.Label(num_top_classes=3) 2022-04-26T21:38:52,154 gr.Interface( 2022-04-26T21:38:52,155 fn=classify_image, inputs=image, outputs=label, interpretation="default" 2022-04-26T21:38:52,155 ).launch() 2022-04-26T21:38:52,156 ``` 2022-04-26T21:38:52,158 In addition to `default`, Gradio also includes [Shapley-based interpretation](https://christophm.github.io/interpretable-ml-book/shap.html), which provides more accurate interpretations, albeit usually with a slower runtime. To use this, simply set the `interpretation` parameter to `"shap"` (note: also make sure the python package `shap` is installed). Optionally, you can modify the the `num_shap` parameter, which controls the tradeoff between accuracy and runtime (increasing this value generally increases accuracy). Here is an example: 2022-04-26T21:38:52,159 ```python 2022-04-26T21:38:52,159 gr.Interface(fn=classify_image, inputs=image, outputs=label, interpretation="shap", num_shap=5).launch() 2022-04-26T21:38:52,160 ``` 2022-04-26T21:38:52,161 This will work for any function, even if internally, the model is a complex neural network or some other black box. If you use Gradio's `default` or `shap` interpretation, the output component must be a `Label`. All common input components are supported. Here is an example with text input. 2022-04-26T21:38:52,162 ```python 2022-04-26T21:38:52,163 import re 2022-04-26T21:38:52,164 import gradio as gr 2022-04-26T21:38:52,165 male_words, female_words = ["he", "his", "him"], ["she", "hers", "her"] 2022-04-26T21:38:52,166 def gender_of_sentence(sentence): 2022-04-26T21:38:52,167 male_count = len([word for word in sentence.split() if word.lower() in male_words]) 2022-04-26T21:38:52,167 female_count = len( 2022-04-26T21:38:52,168 [word for word in sentence.split() if word.lower() in female_words] 2022-04-26T21:38:52,168 ) 2022-04-26T21:38:52,169 total = max(male_count + female_count, 1) 2022-04-26T21:38:52,169 return {"male": male_count / total, "female": female_count / total} 2022-04-26T21:38:52,171 iface = gr.Interface( 2022-04-26T21:38:52,171 fn=gender_of_sentence, 2022-04-26T21:38:52,172 inputs=gr.inputs.Textbox(default="She went to his house to get her keys."), 2022-04-26T21:38:52,172 outputs="label", 2022-04-26T21:38:52,173 interpretation="default", 2022-04-26T21:38:52,173 ) 2022-04-26T21:38:52,174 iface.launch() 2022-04-26T21:38:52,175 ``` 2022-04-26T21:38:52,176 So what is happening under the hood? With these interpretation methods, Gradio runs the prediction multiple times with modified versions of the input. Based on the results, you'll see that the interface automatically highlights the parts of the text (or image, etc.) that contributed increased the likelihood of the class as red. The intensity of color corresponds to the importance of that part of the input. The parts that decrease the class confidence are highlighted blue. 2022-04-26T21:38:52,177 You can also write your own interpretation function. The demo below adds custom interpretation to the previous demo. This function will take the same inputs as the main wrapped function. The output of this interpretation function will be used to highlight the input of each input interface - therefore the number of outputs here corresponds to the number of input interfaces. To see the format for interpretation for each input interface, check the Docs. 2022-04-26T21:38:52,178 ```python 2022-04-26T21:38:52,179 import re 2022-04-26T21:38:52,180 import gradio as gr 2022-04-26T21:38:52,181 male_words, female_words = ["he", "his", "him"], ["she", "hers", "her"] 2022-04-26T21:38:52,182 def gender_of_sentence(sentence): 2022-04-26T21:38:52,183 male_count = len([word for word in sentence.split() if word.lower() in male_words]) 2022-04-26T21:38:52,183 female_count = len( 2022-04-26T21:38:52,184 [word for word in sentence.split() if word.lower() in female_words] 2022-04-26T21:38:52,184 ) 2022-04-26T21:38:52,185 total = max(male_count + female_count, 1) 2022-04-26T21:38:52,186 return {"male": male_count / total, "female": female_count / total} 2022-04-26T21:38:52,187 def interpret_gender(sentence): 2022-04-26T21:38:52,188 result = gender_of_sentence(sentence) 2022-04-26T21:38:52,188 is_male = result["male"] > result["female"] 2022-04-26T21:38:52,189 interpretation = [] 2022-04-26T21:38:52,189 for word in re.split("( )", sentence): 2022-04-26T21:38:52,190 score = 0 2022-04-26T21:38:52,190 token = word.lower() 2022-04-26T21:38:52,191 if (is_male and token in male_words) or (not is_male and token in female_words): 2022-04-26T21:38:52,191 score = 1 2022-04-26T21:38:52,192 elif (is_male and token in female_words) or ( 2022-04-26T21:38:52,192 not is_male and token in male_words 2022-04-26T21:38:52,193 ): 2022-04-26T21:38:52,193 score = -1 2022-04-26T21:38:52,194 interpretation.append((word, score)) 2022-04-26T21:38:52,194 return interpretation 2022-04-26T21:38:52,196 iface = gr.Interface( 2022-04-26T21:38:52,196 fn=gender_of_sentence, 2022-04-26T21:38:52,197 inputs=gr.inputs.Textbox(default="She went to his house to get her keys."), 2022-04-26T21:38:52,197 outputs="label", 2022-04-26T21:38:52,198 interpretation=interpret_gender, 2022-04-26T21:38:52,198 enable_queue=True, 2022-04-26T21:38:52,199 ) 2022-04-26T21:38:52,200 iface.launch() 2022-04-26T21:38:52,201 ``` 2022-04-26T21:38:52,202 ### Themes and Custom Styling 2022-04-26T21:38:52,203 If you'd like to change how your interface looks, you can select a different theme by simply passing in the `theme` parameter, like so: 2022-04-26T21:38:52,204 ```python 2022-04-26T21:38:52,204 gr.Interface(fn=classify_image, inputs=image, outputs=label, theme="huggingface").launch() 2022-04-26T21:38:52,205 ``` 2022-04-26T21:38:52,206 Here are the themes we currently support: `"default"`, `"huggingface"`, `"grass"`, `"peach"`, and the dark themes corresponding to each of these: `"darkdefault"`, `"darkhuggingface"`, `"darkgrass"`, `"darkpeach"`. 2022-04-26T21:38:52,207 If you'd like to have more fine-grained control over any aspect of the app, you can also write your own css or pass in a css file, with the `css` parameter of the `Interface` class. 2022-04-26T21:38:52,208 ### Custom Flagging Options 2022-04-26T21:38:52,209 In some cases, you might like to provide your users or testers with *more* than just a binary option to flag a sample. You can provide `flagging_options` that they select from a dropdown each time they click the flag button. This lets them provide additional feedback every time they flag a sample. 2022-04-26T21:38:52,210 Here's an example: 2022-04-26T21:38:52,211 ```python 2022-04-26T21:38:52,212 gr.Interface(fn=classify_image, inputs=image, outputs=label, flagging_options=["incorrect", "ambiguous", "offensive", "other"]).launch() 2022-04-26T21:38:52,212 ``` 2022-04-26T21:38:52,213 ### Loading Hugging Face Models and Spaces 2022-04-26T21:38:52,214 Gradio integrates nicely with the Hugging Face Hub, allowing you to load models and Spaces with just one line of code. To use this, simply use the `load()` method in the `Interface` class. So: 2022-04-26T21:38:52,215 - To load any model from the Hugging Face Hub and create an interface around it, you pass `"model/"` or `"huggingface/"` followed by the model name, like these examples: 2022-04-26T21:38:52,216 ```python 2022-04-26T21:38:52,217 gr.Interface.load("huggingface/gpt2").launch(); 2022-04-26T21:38:52,217 ``` 2022-04-26T21:38:52,218 ```python 2022-04-26T21:38:52,219 gr.Interface.load("huggingface/EleutherAI/gpt-j-6B", 2022-04-26T21:38:52,220 inputs=gr.inputs.Textbox(lines=5, label="Input Text") # customizes the input component 2022-04-26T21:38:52,220 ).launch() 2022-04-26T21:38:52,221 ``` 2022-04-26T21:38:52,221 - To load any Space from the Hugging Face Hub and recreate it locally (so that you can customize the inputs and outputs for example), you pass `"spaces/"` followed by the model name: 2022-04-26T21:38:52,222 ```python 2022-04-26T21:38:52,223 gr.Interface.load("spaces/eugenesiow/remove-bg", inputs="webcam", title="Remove your webcam background!").launch() 2022-04-26T21:38:52,223 ``` 2022-04-26T21:38:52,224 One of the great things about loading Hugging Face models or spaces using Gradio is that you can then immediately use the resulting `Interface` object just like function in your Python code (this works for every type of model/space: text, images, audio, video, and even multimodal models): 2022-04-26T21:38:52,225 ```python 2022-04-26T21:38:52,226 io = gr.Interface.load("models/EleutherAI/gpt-neo-2.7B") 2022-04-26T21:38:52,226 io("It was the best of times") # outputs model completion 2022-04-26T21:38:52,227 ``` 2022-04-26T21:38:52,228 ### Putting Interfaces in Parallel and Series 2022-04-26T21:38:52,229 Gradio also lets you mix interfaces very easily using the `gradio.Parallel` and `gradio.Series` classes. `Parallel` lets you put two similar models (if they have the same input type) in parallel to compare model predictions: 2022-04-26T21:38:52,230 ```python 2022-04-26T21:38:52,231 generator1 = gr.Interface.load("huggingface/gpt2") 2022-04-26T21:38:52,231 generator2 = gr.Interface.load("huggingface/EleutherAI/gpt-neo-2.7B") 2022-04-26T21:38:52,232 generator3 = gr.Interface.load("huggingface/EleutherAI/gpt-j-6B") 2022-04-26T21:38:52,233 gr.Parallel(generator1, generator2, generator3).launch() 2022-04-26T21:38:52,233 ``` 2022-04-26T21:38:52,234 `Series` lets you put models and spaces in series, piping the output of one model into the input of the next model. 2022-04-26T21:38:52,236 ```python 2022-04-26T21:38:52,236 generator = gr.Interface.load("huggingface/gpt2") 2022-04-26T21:38:52,237 translator = gr.Interface.load("huggingface/t5-small") 2022-04-26T21:38:52,238 gr.Series(generator, translator).launch() # this demo generates text, then translates it to German, and outputs the final result. 2022-04-26T21:38:52,238 ``` 2022-04-26T21:38:52,239 And of course, you can also mix `Parallel` and `Series` together whenever that makes sense! 2022-04-26T21:38:52,240 ### Queuing to Manage Long Inference Times 2022-04-26T21:38:52,241 If many people are using your interface or if the inference time of your function is long (> 1min), simply set the `enable_queue` parameter in the `launch` method to `True` to prevent timeouts. 2022-04-26T21:38:52,242 ```python 2022-04-26T21:38:52,242 gr.Interface(fn=classify_image, inputs=image, outputs=label).launch(enable_queue=True) 2022-04-26T21:38:52,243 ``` 2022-04-26T21:38:52,244 This sets up a queue of workers to handle the predictions and return the response to the front end. This is strongly recommended if you are planning on uploading your demo to Hugging Face Spaces (as described above) so that you can manage a large number of users simultaneously using your demo. 2022-04-26T21:38:52,247 ## System Requirements: 2022-04-26T21:38:52,248 Gradio requires Python `3.7+` and has been tested on the latest versions of Windows, MacOS, and various common Linux distributions (e.g. Ubuntu). For Python package requirements, please see the `setup.py` file. 2022-04-26T21:38:52,249 ## Contributing: 2022-04-26T21:38:52,250 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](https://github.com/gradio-app/gradio/blob/master/CONTRIBUTING.md) for more info. 2022-04-26T21:38:52,252 ## License: 2022-04-26T21:38:52,253 Gradio is licensed under the Apache License 2.0 2022-04-26T21:38:52,254 ## See more: 2022-04-26T21:38:52,255 You can find many more examples as well as more info on usage on our website: www.gradio.app 2022-04-26T21:38:52,256 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. 2022-04-26T21:38:52,257 ``` 2022-04-26T21:38:52,257 @article{abid2019gradio, 2022-04-26T21:38:52,258 title={Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild}, 2022-04-26T21:38:52,258 author={Abid, Abubakar and Abdalla, Ali and Abid, Ali and Khan, Dawood and Alfozan, Abdulrahman and Zou, James}, 2022-04-26T21:38:52,259 journal={arXiv preprint arXiv:1906.02569}, 2022-04-26T21:38:52,259 year={2019} 2022-04-26T21:38:52,260 } 2022-04-26T21:38:52,452 ``` 2022-04-26T21:38:52,496 running egg_info 2022-04-26T21:38:52,501 creating /tmp/pip-pip-egg-info-m4jcrqcc/gradio.egg-info 2022-04-26T21:38:52,504 writing /tmp/pip-pip-egg-info-m4jcrqcc/gradio.egg-info/PKG-INFO 2022-04-26T21:38:52,513 writing requirements to /tmp/pip-pip-egg-info-m4jcrqcc/gradio.egg-info/requires.txt 2022-04-26T21:38:52,515 writing dependency_links to /tmp/pip-pip-egg-info-m4jcrqcc/gradio.egg-info/dependency_links.txt 2022-04-26T21:38:52,519 writing top-level names to /tmp/pip-pip-egg-info-m4jcrqcc/gradio.egg-info/top_level.txt 2022-04-26T21:38:52,523 writing manifest file '/tmp/pip-pip-egg-info-m4jcrqcc/gradio.egg-info/SOURCES.txt' 2022-04-26T21:38:52,928 reading manifest file '/tmp/pip-pip-egg-info-m4jcrqcc/gradio.egg-info/SOURCES.txt' 2022-04-26T21:38:52,932 reading manifest template 'MANIFEST.in' 2022-04-26T21:38:53,054 writing manifest file '/tmp/pip-pip-egg-info-m4jcrqcc/gradio.egg-info/SOURCES.txt' 2022-04-26T21:38:53,206 Source in /tmp/pip-wheel-j3i6lblr/gradio_7cb2db11246741818da3aa2beedbe62d has version 2.9.0b9, which satisfies requirement gradio==2.9.0b9 from https://files.pythonhosted.org/packages/6c/6d/6d27f2737e1873b6e4eb0469f9618ab04e5689af89fb3f91a90bfa05c1fc/gradio-2.9.0b9.tar.gz#sha256=d7095c8b9f31cd63be8ff64cfb629d60fe17e5706aef0919420624a8ffb4a69e 2022-04-26T21:38:53,208 Removed gradio==2.9.0b9 from https://files.pythonhosted.org/packages/6c/6d/6d27f2737e1873b6e4eb0469f9618ab04e5689af89fb3f91a90bfa05c1fc/gradio-2.9.0b9.tar.gz#sha256=d7095c8b9f31cd63be8ff64cfb629d60fe17e5706aef0919420624a8ffb4a69e from build tracker '/tmp/pip-req-tracker-u4sd56kh' 2022-04-26T21:38:53,227 Created temporary directory: /tmp/pip-unpack-c3pg0osu 2022-04-26T21:38:53,228 Building wheels for collected packages: gradio 2022-04-26T21:38:53,237 Created temporary directory: /tmp/pip-wheel-5_ps8fep 2022-04-26T21:38:53,238 Building wheel for gradio (setup.py): started 2022-04-26T21:38:53,239 Destination directory: /tmp/pip-wheel-5_ps8fep 2022-04-26T21:38:53,239 Running command /usr/bin/python3 -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-wheel-j3i6lblr/gradio_7cb2db11246741818da3aa2beedbe62d/setup.py'"'"'; __file__='"'"'/tmp/pip-wheel-j3i6lblr/gradio_7cb2db11246741818da3aa2beedbe62d/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' bdist_wheel -d /tmp/pip-wheel-5_ps8fep 2022-04-26T21:38:55,034 [![CircleCI](https://circleci.com/gh/gradio-app/gradio.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) [![codecov](https://codecov.io/gh/gradio-app/gradio/branch/master/graph/badge.svg?token=NNVPX9KEGS)](https://codecov.io/gh/gradio-app/gradio) [![PyPI - Downloads](https://img.shields.io/pypi/dm/gradio)](https://pypi.org/project/gradio/) [![Twitter Follow](https://img.shields.io/twitter/follow/gradio.svg?style=social&label=Follow)](https://twitter.com/gradio) 2022-04-26T21:38:55,035 # Welcome to Gradio 2022-04-26T21:38:55,036 Quickly create beautiful user interfaces around your machine learning models. Gradio (pronounced GRAY-dee-oh) makes it easy for you to demo your model in your browser or let people "try it out" by dragging-and-dropping in their own images, pasting text, recording their own voice, etc. and seeing what the model outputs. 2022-04-26T21:38:55,037 ![Interface montage](website/homepage/src/assets/img/montage.gif) 2022-04-26T21:38:55,038 Gradio is useful for: 2022-04-26T21:38:55,039 * **Demoing** your machine learning models for clients / collaborators / users / students 2022-04-26T21:38:55,041 * **Deploying** your models quickly with automatic shareable links and getting feedback on model performance 2022-04-26T21:38:55,042 * **Debugging** your model interactively during development using built-in manipulation and interpretation tools 2022-04-26T21:38:55,043 **You can find an interactive version of the following Getting Started at [https://gradio.app/getting_started](https://gradio.app/getting_started).** 2022-04-26T21:38:55,044 ## Getting Started 2022-04-26T21:38:55,045 **Prerequisite**: Python 3.7+ and that's it! 2022-04-26T21:38:55,047 ### Quick Start 2022-04-26T21:38:55,048 To get Gradio running with a simple "Hello, World" example, follow these three steps: 2022-04-26T21:38:55,049 1. Install Gradio from pip. 2022-04-26T21:38:55,050 ```bash 2022-04-26T21:38:55,051 pip install gradio 2022-04-26T21:38:55,052 ``` 2022-04-26T21:38:55,053 2. Run the code below as a Python script or in a Python notebook (or in a [colab notebook](https://colab.research.google.com/drive/18ODkJvyxHutTN0P5APWyGFO_xwNcgHDZ?usp=sharing)). 2022-04-26T21:38:55,054 ```python 2022-04-26T21:38:55,055 import gradio as gr 2022-04-26T21:38:55,056 def greet(name): 2022-04-26T21:38:55,057 return "Hello " + name + "!!" 2022-04-26T21:38:55,059 iface = gr.Interface(fn=greet, inputs="text", outputs="text") 2022-04-26T21:38:55,059 iface.launch() 2022-04-26T21:38:55,061 ``` 2022-04-26T21:38:55,062 3. The interface below will appear automatically within the Python notebook, or pop in a browser on [http://localhost:7860](http://localhost:7860/) if running from a script. 2022-04-26T21:38:55,063 ![hello_world interface](demo/hello_world/screenshot.gif) 2022-04-26T21:38:55,064 ### Understanding the `Interface` class 2022-04-26T21:38:55,066 Gradio can wrap almost any Python function with an easy-to-use user interface. In the example above, we saw a simple text-based function. But the function could be anything from image enhancer to a tax calculator to (most commonly) the prediction function of a pretrained machine learning model. 2022-04-26T21:38:55,067 The core `Interface` class is initialized with three parameters: 2022-04-26T21:38:55,068 - `fn`: the function to wrap 2022-04-26T21:38:55,069 - `inputs`: the input component type(s), e.g. `"image"` or `"audio"` ([see docs for complete list](/docs)) 2022-04-26T21:38:55,069 - `outputs`: the output component type(s) e.g. `"image"` or `"label"` ([see docs for complete list](/docs)) 2022-04-26T21:38:55,070 With these three arguments, we can quickly create interfaces and `launch()` them. But what if you want to change how the UI components look or behave? 2022-04-26T21:38:55,072 ### Customizable Components 2022-04-26T21:38:55,073 Let's say we want to customize the input text field - for example, we wanted it to be larger and have a text hint. If we use the actual input class for `Textbox` instead of using the string shortcut, we have access to much more customizability. To see a list of all the components we support and how you can customize them, check out the [Docs](https://gradio.app/docs). 2022-04-26T21:38:55,074 ```python 2022-04-26T21:38:55,075 import gradio as gr 2022-04-26T21:38:55,076 def greet(name): 2022-04-26T21:38:55,077 return "Hello " + name + "!" 2022-04-26T21:38:55,078 iface = gr.Interface( 2022-04-26T21:38:55,079 fn=greet, 2022-04-26T21:38:55,079 inputs=gr.inputs.Textbox(lines=2, placeholder="Name Here..."), 2022-04-26T21:38:55,080 outputs="text", 2022-04-26T21:38:55,080 ) 2022-04-26T21:38:55,081 iface.launch() 2022-04-26T21:38:55,082 ``` 2022-04-26T21:38:55,082 ![hello_world_2 interface](demo/hello_world_2/screenshot.gif) 2022-04-26T21:38:55,083 ### Multiple Inputs and Outputs 2022-04-26T21:38:55,085 Let's say we had a much more complex function, with multiple inputs and outputs. In the example below, we have a function that takes a string, boolean, and number, and returns a string and number. Take a look how we pass a list of input and output components. 2022-04-26T21:38:55,086 ```python 2022-04-26T21:38:55,087 import gradio as gr 2022-04-26T21:38:55,088 def greet(name, is_morning, temperature): 2022-04-26T21:38:55,089 salutation = "Good morning" if is_morning else "Good evening" 2022-04-26T21:38:55,089 greeting = "%s %s. It is %s degrees today" % (salutation, name, temperature) 2022-04-26T21:38:55,090 celsius = (temperature - 32) * 5 / 9 2022-04-26T21:38:55,090 return greeting, round(celsius, 2) 2022-04-26T21:38:55,092 iface = gr.Interface( 2022-04-26T21:38:55,093 fn=greet, 2022-04-26T21:38:55,093 inputs=["text", "checkbox", gr.inputs.Slider(0, 100)], 2022-04-26T21:38:55,094 outputs=["text", "number"], 2022-04-26T21:38:55,094 ) 2022-04-26T21:38:55,095 iface.launch() 2022-04-26T21:38:55,096 ``` 2022-04-26T21:38:55,097 ![hello_world_3 interface](demo/hello_world_3/screenshot.gif) 2022-04-26T21:38:55,098 We simply wrap the components in a list. Each component in the `inputs` list corresponds to one of the parameters of the function, in order. Each component in the `outputs` list corresponds to one of the values returned by the function, again in order. 2022-04-26T21:38:55,099 ### Working with Images 2022-04-26T21:38:55,100 Let's try an image-to-image function. When using the `Image` component, your function will receive a numpy array of your specified size, with the shape `(width, height, 3)`, where the last dimension represents the RGB values. We'll return an image as well in the form of a numpy array. 2022-04-26T21:38:55,101 ```python 2022-04-26T21:38:55,102 import numpy as np 2022-04-26T21:38:55,103 import gradio as gr 2022-04-26T21:38:55,105 def sepia(input_img): 2022-04-26T21:38:55,105 sepia_filter = np.array( 2022-04-26T21:38:55,106 [[0.393, 0.769, 0.189], [0.349, 0.686, 0.168], [0.272, 0.534, 0.131]] 2022-04-26T21:38:55,107 ) 2022-04-26T21:38:55,107 sepia_img = input_img.dot(sepia_filter.T) 2022-04-26T21:38:55,108 sepia_img /= sepia_img.max() 2022-04-26T21:38:55,108 return sepia_img 2022-04-26T21:38:55,110 iface = gr.Interface(sepia, gr.inputs.Image(shape=(200, 200)), "image") 2022-04-26T21:38:55,111 iface.launch() 2022-04-26T21:38:55,112 ``` 2022-04-26T21:38:55,113 ![sepia_filter interface](demo/sepia_filter/screenshot.gif) 2022-04-26T21:38:55,114 Additionally, our `Image` input interface comes with an 'edit' button which opens tools for cropping, flipping, rotating, drawing over, and applying filters to images. We've found that manipulating images in this way will often reveal hidden flaws in a model. 2022-04-26T21:38:55,115 In addition to images, Gradio supports other media input types, such as audio or video uploads, as well as many output components. Read about these in the [Docs](https://gradio.app/docs). 2022-04-26T21:38:55,116 ### Working with DataFrames and Graphs 2022-04-26T21:38:55,117 You can use Gradio to support inputs and outputs from your typical data libraries, such as numpy arrays, pandas dataframes, and plotly graphs. Take a look at the demo below (ignore the complicated data manipulation in the function!) 2022-04-26T21:38:55,119 ```python 2022-04-26T21:38:55,119 import matplotlib.pyplot as plt 2022-04-26T21:38:55,120 import numpy as np 2022-04-26T21:38:55,120 import pandas as pd 2022-04-26T21:38:55,121 import gradio as gr 2022-04-26T21:38:55,123 def sales_projections(employee_data): 2022-04-26T21:38:55,124 sales_data = employee_data.iloc[:, 1:4].astype("int").to_numpy() 2022-04-26T21:38:55,124 regression_values = np.apply_along_axis( 2022-04-26T21:38:55,125 lambda row: np.array(np.poly1d(np.polyfit([0, 1, 2], row, 2))), 0, sales_data 2022-04-26T21:38:55,125 ) 2022-04-26T21:38:55,126 projected_months = np.repeat( 2022-04-26T21:38:55,127 np.expand_dims(np.arange(3, 12), 0), len(sales_data), axis=0 2022-04-26T21:38:55,127 ) 2022-04-26T21:38:55,128 projected_values = np.array( 2022-04-26T21:38:55,128 [ 2022-04-26T21:38:55,129 month * month * regression[0] + month * regression[1] + regression[2] 2022-04-26T21:38:55,129 for month, regression in zip(projected_months, regression_values) 2022-04-26T21:38:55,130 ] 2022-04-26T21:38:55,130 ) 2022-04-26T21:38:55,131 plt.plot(projected_values.T) 2022-04-26T21:38:55,131 plt.legend(employee_data["Name"]) 2022-04-26T21:38:55,132 return employee_data, plt.gcf(), regression_values 2022-04-26T21:38:55,134 iface = gr.Interface( 2022-04-26T21:38:55,134 sales_projections, 2022-04-26T21:38:55,135 gr.inputs.Dataframe( 2022-04-26T21:38:55,135 headers=["Name", "Jan Sales", "Feb Sales", "Mar Sales"], 2022-04-26T21:38:55,135 default=[["Jon", 12, 14, 18], ["Alice", 14, 17, 2], ["Sana", 8, 9.5, 12]], 2022-04-26T21:38:55,136 ), 2022-04-26T21:38:55,136 ["dataframe", "plot", "numpy"], 2022-04-26T21:38:55,137 description="Enter sales figures for employees to predict sales trajectory over year.", 2022-04-26T21:38:55,137 ) 2022-04-26T21:38:55,138 iface.launch() 2022-04-26T21:38:55,139 ``` 2022-04-26T21:38:55,139 ![sales_projections interface](demo/sales_projections/screenshot.gif) 2022-04-26T21:38:55,140 ### Example Inputs 2022-04-26T21:38:55,141 You can provide example data that a user can easily load into the model. This can be helpful to demonstrate the types of inputs the model expects, as well as to provide a way to explore your dataset in conjunction with your model. To load example data, you provide a **nested list** to the `examples=` keyword argument of the Interface constructor. Each sublist within the outer list represents a data sample, and each element within the sublist represents an input for each input component. The format of example data for each component is specified in the [Docs](https://gradio.app/docs). 2022-04-26T21:38:55,142 ```python 2022-04-26T21:38:55,143 import gradio as gr 2022-04-26T21:38:55,144 def calculator(num1, operation, num2): 2022-04-26T21:38:55,145 if operation == "add": 2022-04-26T21:38:55,145 return num1 + num2 2022-04-26T21:38:55,146 elif operation == "subtract": 2022-04-26T21:38:55,146 return num1 - num2 2022-04-26T21:38:55,147 elif operation == "multiply": 2022-04-26T21:38:55,148 return num1 * num2 2022-04-26T21:38:55,148 elif operation == "divide": 2022-04-26T21:38:55,149 return num1 / num2 2022-04-26T21:38:55,150 iface = gr.Interface( 2022-04-26T21:38:55,151 calculator, 2022-04-26T21:38:55,151 ["number", gr.inputs.Radio(["add", "subtract", "multiply", "divide"]), "number"], 2022-04-26T21:38:55,152 "number", 2022-04-26T21:38:55,152 examples=[ 2022-04-26T21:38:55,153 [5, "add", 3], 2022-04-26T21:38:55,154 [4, "divide", 2], 2022-04-26T21:38:55,154 [-4, "multiply", 2.5], 2022-04-26T21:38:55,155 [0, "subtract", 1.2], 2022-04-26T21:38:55,155 ], 2022-04-26T21:38:55,155 title="test calculator", 2022-04-26T21:38:55,156 description="heres a sample toy calculator. enjoy!", 2022-04-26T21:38:55,156 flagging_options=["this", "or", "that"], 2022-04-26T21:38:55,157 ) 2022-04-26T21:38:55,158 iface.launch() 2022-04-26T21:38:55,159 ``` 2022-04-26T21:38:55,160 ![calculator interface](demo/calculator/screenshot.gif) 2022-04-26T21:38:55,161 You can load a large dataset into the examples to browse and interact with the dataset through Gradio. The examples will be automatically paginated (you can configure this through the `examples_per_page` argument of Interface) and you can use CTRL + arrow keys to navigate through the examples quickly. 2022-04-26T21:38:55,162 ### Live Interfaces 2022-04-26T21:38:55,163 You can make interfaces automatically refresh by setting `live=True` in the interface. Now the interface will recalculate as soon as the user input changes. 2022-04-26T21:38:55,165 ```python 2022-04-26T21:38:55,165 import gradio as gr 2022-04-26T21:38:55,167 def calculator(num1, operation, num2): 2022-04-26T21:38:55,167 if operation == "add": 2022-04-26T21:38:55,168 return num1 + num2 2022-04-26T21:38:55,168 elif operation == "subtract": 2022-04-26T21:38:55,169 return num1 - num2 2022-04-26T21:38:55,169 elif operation == "multiply": 2022-04-26T21:38:55,170 return num1 * num2 2022-04-26T21:38:55,170 elif operation == "divide": 2022-04-26T21:38:55,171 return num1 / num2 2022-04-26T21:38:55,172 iface = gr.Interface( 2022-04-26T21:38:55,173 calculator, 2022-04-26T21:38:55,173 ["number", gr.inputs.Radio(["add", "subtract", "multiply", "divide"]), "number"], 2022-04-26T21:38:55,174 "number", 2022-04-26T21:38:55,174 live=True, 2022-04-26T21:38:55,175 ) 2022-04-26T21:38:55,176 iface.launch() 2022-04-26T21:38:55,177 ``` 2022-04-26T21:38:55,178 ![calculator_live interface](demo/calculator_live/screenshot.gif) 2022-04-26T21:38:55,179 Note there is no submit button, because the interface resubmits automatically on change. 2022-04-26T21:38:55,179 ### Using State 2022-04-26T21:38:55,180 Your function may use data that persists beyond a single function call. If the data is something accessible to all function calls and all users, you can create a global variable outside the function call and access it inside the function. For example, you may load a large model outside the function and use it inside the function so that every function call does not need to reload the model. 2022-04-26T21:38:55,182 Another type of data persistence Gradio supports is session **state**, where data persists across multiple submits within a page load. However, data is *not* shared between different users of your model. To store data in a session state, you need to do three things: (1) Pass in an extra parameter into your function, which represents the state of the interface. (2) At the end of the function, return the updated value of the state as an extra return value (3) Add the `'state'` input and `'state'` output components when creating your `Interface`. See the chatbot example below: 2022-04-26T21:38:55,183 ```python 2022-04-26T21:38:55,183 import random 2022-04-26T21:38:55,184 import gradio as gr 2022-04-26T21:38:55,186 def chat(message, history): 2022-04-26T21:38:55,186 history = history or [] 2022-04-26T21:38:55,187 if message.startswith("How many"): 2022-04-26T21:38:55,188 response = random.randint(1, 10) 2022-04-26T21:38:55,188 elif message.startswith("How"): 2022-04-26T21:38:55,189 response = random.choice(["Great", "Good", "Okay", "Bad"]) 2022-04-26T21:38:55,189 elif message.startswith("Where"): 2022-04-26T21:38:55,189 response = random.choice(["Here", "There", "Somewhere"]) 2022-04-26T21:38:55,190 else: 2022-04-26T21:38:55,190 response = "I don't know" 2022-04-26T21:38:55,191 history.append((message, response)) 2022-04-26T21:38:55,191 html = "
" 2022-04-26T21:38:55,192 for user_msg, resp_msg in history: 2022-04-26T21:38:55,193 html += f"
{user_msg}
" 2022-04-26T21:38:55,193 html += f"
{resp_msg}
" 2022-04-26T21:38:55,194 html += "
" 2022-04-26T21:38:55,194 return html, history 2022-04-26T21:38:55,196 iface = gr.Interface( 2022-04-26T21:38:55,196 chat, 2022-04-26T21:38:55,197 ["text", "state"], 2022-04-26T21:38:55,197 ["html", "state"], 2022-04-26T21:38:55,198 css=""" 2022-04-26T21:38:55,198 .chatbox {display:flex;flex-direction:column} 2022-04-26T21:38:55,199 .user_msg, .resp_msg {padding:4px;margin-bottom:4px;border-radius:4px;width:80%} 2022-04-26T21:38:55,199 .user_msg {background-color:cornflowerblue;color:white;align-self:start} 2022-04-26T21:38:55,200 .resp_msg {background-color:lightgray;align-self:self-end} 2022-04-26T21:38:55,200 """, 2022-04-26T21:38:55,201 allow_screenshot=False, 2022-04-26T21:38:55,201 allow_flagging="never", 2022-04-26T21:38:55,202 ) 2022-04-26T21:38:55,203 iface.launch() 2022-04-26T21:38:55,204 ``` 2022-04-26T21:38:55,204 ![chatbot interface](demo/chatbot/screenshot.gif) 2022-04-26T21:38:55,205 Notice how the state persists across submits within each page, but the state is not shared between the two pages. Some more points to note: you can pass in a default value to the state parameter, which is used as the initial value of the state. The state must be a something that can be serialized to a JSON format (e.g. a dictionary, a list, or a single value. Typically, objects will not work). 2022-04-26T21:38:55,206 ### Flagging 2022-04-26T21:38:55,207 Underneath the output interfaces, there is a button marked "Flag". When a user testing your model sees input with interesting output, such as erroneous or unexpected model behaviour, they can flag the input for the interface creator to review. Within the directory provided by the `flagging_dir=` argument to the Interface constructor, a CSV file will log the flagged inputs. If the interface involves file data, such as for Image and Audio components, folders will be created to store those flagged data as well. 2022-04-26T21:38:55,208 For example, with the calculator interface shown above, we would have the flagged data stored in the flagged directory shown below: 2022-04-26T21:38:55,209 ```directory 2022-04-26T21:38:55,210 +-- calculator.py 2022-04-26T21:38:55,210 +-- flagged/ 2022-04-26T21:38:55,211 | +-- logs.csv 2022-04-26T21:38:55,211 ``` 2022-04-26T21:38:55,212 *flagged/logs.csv* 2022-04-26T21:38:55,213 ```csv 2022-04-26T21:38:55,213 num1,operation,num2,Output 2022-04-26T21:38:55,214 5,add,7,12 2022-04-26T21:38:55,215 6,subtract,1.5,4.5 2022-04-26T21:38:55,215 ``` 2022-04-26T21:38:55,216 With the sepia interface shown above, we would have the flagged data stored in the flagged directory shown below: 2022-04-26T21:38:55,217 ```directory 2022-04-26T21:38:55,218 +-- sepia.py 2022-04-26T21:38:55,218 +-- flagged/ 2022-04-26T21:38:55,219 | +-- logs.csv 2022-04-26T21:38:55,219 | +-- im/ 2022-04-26T21:38:55,220 | | +-- 0.png 2022-04-26T21:38:55,220 | | +-- 1.png 2022-04-26T21:38:55,221 | +-- Output/ 2022-04-26T21:38:55,221 | | +-- 0.png 2022-04-26T21:38:55,222 | | +-- 1.png 2022-04-26T21:38:55,222 ``` 2022-04-26T21:38:55,223 *flagged/logs.csv* 2022-04-26T21:38:55,223 ```csv 2022-04-26T21:38:55,224 im,Output 2022-04-26T21:38:55,224 im/0.png,Output/0.png 2022-04-26T21:38:55,225 im/1.png,Output/1.png 2022-04-26T21:38:55,225 ``` 2022-04-26T21:38:55,226 You can review these flagged inputs by manually exploring the flagging directory, or load them into the examples of the Gradio interface by pointing the `examples=` argument to the flagged directory. If you wish for the user to provide a reason for flagging, you can pass a list of strings to the `flagging_options` argument of Interface. Users will have to select one of the strings when flagging, which will be saved as an additional column to the CSV. 2022-04-26T21:38:55,228 ### Sharing Interfaces Publicly 2022-04-26T21:38:55,229 Interfaces can be easily shared publicly by setting `share=True` in the `launch()` method. Like this: 2022-04-26T21:38:55,230 ```python 2022-04-26T21:38:55,230 gr.Interface(classify_image, "image", "label").launch(share=True) 2022-04-26T21:38:55,231 ``` 2022-04-26T21:38:55,232 This generates a public, shareable link that you can send to anybody! When you send this link, the user on the other side can try out the model in their browser. Because the processing happens on your device (as long as your device stays on!), you don't have to worry about any packaging any dependencies. If you're working out of colab notebook, a share link is always automatically created. It usually looks something like this: **XXXXX.gradio.app**. Although the link is served through a gradio link, we are only a proxy for your local server, and do not store any data sent through the interfaces. 2022-04-26T21:38:55,233 Keep in mind, however, that these links are publicly accessible, meaning that anyone can use your model for prediction! Therefore, make sure not to expose any sensitive information through the functions you write, or allow any critical changes to occur on your device. If you set `share=False` (the default), only a local link is created, which can be shared by [port-forwarding](https://www.ssh.com/ssh/tunneling/example) with specific users. 2022-04-26T21:38:55,234 Share links expire after 72 hours. For permanent hosting, see Hosting Gradio Apps on Spaces below. 2022-04-26T21:38:55,235 ![Sharing diagram](website/homepage/src/assets/img/sharing.svg) 2022-04-26T21:38:55,236 ### Hosting Gradio Apps on Spaces 2022-04-26T21:38:55,237 Huggingface provides the infrastructure to permanently host your Gradio model on the internet, for free! You can either drag and drop a folder containing your Gradio model and all related files, or you can point HF Spaces to your Git repository and HP Spaces will pull the Gradio interface from there. See [Huggingface Spaces](http://huggingface.co/spaces/) for more information. 2022-04-26T21:38:55,238 ![Hosting Demo](website/homepage/src/assets/img/hf_demo.gif) 2022-04-26T21:38:55,239 ## Advanced Features 2022-04-26T21:38:55,240 2022-04-26T21:38:55,241 Here, we go through several advanced functionalities that your Gradio demo can include without you needing to write much more code! 2022-04-26T21:38:55,242 ### Authentication 2022-04-26T21:38:55,243 You may wish to put an authentication page in front of your interface to limit who can open your interface. With the `auth=` keyword argument in the `launch()` method, you can pass a list of acceptable username/password tuples; or, for more complex authentication handling, you can even pass a function that takes a username and password as arguments, and returns True to allow authentication, False otherwise. Here's an example that provides password-based authentication for a single user named "admin": 2022-04-26T21:38:55,244 ```python 2022-04-26T21:38:55,244 gr.Interface(fn=classify_image, inputs=image, outputs=label).launch(auth=("admin", "pass1234")) 2022-04-26T21:38:55,245 ``` 2022-04-26T21:38:55,246 ### Interpreting your Predictions 2022-04-26T21:38:55,247 Most models are black boxes such that the internal logic of the function is hidden from the end user. To encourage transparency, we've made it very easy to add interpretation to your model by simply setting the `interpretation` keyword in the `Interface` class to `default`. This allows your users to understand what parts of the input are responsible for the output. Take a look at the simple interface below which shows an image classifier that also includes interpretation: 2022-04-26T21:38:55,248 ```python 2022-04-26T21:38:55,249 import requests 2022-04-26T21:38:55,249 import tensorflow as tf 2022-04-26T21:38:55,250 import gradio as gr 2022-04-26T21:38:55,251 inception_net = tf.keras.applications.MobileNetV2() # load the model 2022-04-26T21:38:55,252 # Download human-readable labels for ImageNet. 2022-04-26T21:38:55,253 response = requests.get("https://git.io/JJkYN") 2022-04-26T21:38:55,253 labels = response.text.split("\n") 2022-04-26T21:38:55,255 def classify_image(inp): 2022-04-26T21:38:55,255 inp = inp.reshape((-1, 224, 224, 3)) 2022-04-26T21:38:55,256 inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp) 2022-04-26T21:38:55,257 prediction = inception_net.predict(inp).flatten() 2022-04-26T21:38:55,257 return {labels[i]: float(prediction[i]) for i in range(1000)} 2022-04-26T21:38:55,259 image = gr.inputs.Image(shape=(224, 224)) 2022-04-26T21:38:55,259 label = gr.outputs.Label(num_top_classes=3) 2022-04-26T21:38:55,260 gr.Interface( 2022-04-26T21:38:55,261 fn=classify_image, inputs=image, outputs=label, interpretation="default" 2022-04-26T21:38:55,261 ).launch() 2022-04-26T21:38:55,262 ``` 2022-04-26T21:38:55,264 In addition to `default`, Gradio also includes [Shapley-based interpretation](https://christophm.github.io/interpretable-ml-book/shap.html), which provides more accurate interpretations, albeit usually with a slower runtime. To use this, simply set the `interpretation` parameter to `"shap"` (note: also make sure the python package `shap` is installed). Optionally, you can modify the the `num_shap` parameter, which controls the tradeoff between accuracy and runtime (increasing this value generally increases accuracy). Here is an example: 2022-04-26T21:38:55,265 ```python 2022-04-26T21:38:55,265 gr.Interface(fn=classify_image, inputs=image, outputs=label, interpretation="shap", num_shap=5).launch() 2022-04-26T21:38:55,266 ``` 2022-04-26T21:38:55,267 This will work for any function, even if internally, the model is a complex neural network or some other black box. If you use Gradio's `default` or `shap` interpretation, the output component must be a `Label`. All common input components are supported. Here is an example with text input. 2022-04-26T21:38:55,268 ```python 2022-04-26T21:38:55,268 import re 2022-04-26T21:38:55,269 import gradio as gr 2022-04-26T21:38:55,270 male_words, female_words = ["he", "his", "him"], ["she", "hers", "her"] 2022-04-26T21:38:55,272 def gender_of_sentence(sentence): 2022-04-26T21:38:55,272 male_count = len([word for word in sentence.split() if word.lower() in male_words]) 2022-04-26T21:38:55,273 female_count = len( 2022-04-26T21:38:55,273 [word for word in sentence.split() if word.lower() in female_words] 2022-04-26T21:38:55,274 ) 2022-04-26T21:38:55,274 total = max(male_count + female_count, 1) 2022-04-26T21:38:55,275 return {"male": male_count / total, "female": female_count / total} 2022-04-26T21:38:55,277 iface = gr.Interface( 2022-04-26T21:38:55,277 fn=gender_of_sentence, 2022-04-26T21:38:55,278 inputs=gr.inputs.Textbox(default="She went to his house to get her keys."), 2022-04-26T21:38:55,278 outputs="label", 2022-04-26T21:38:55,279 interpretation="default", 2022-04-26T21:38:55,279 ) 2022-04-26T21:38:55,280 iface.launch() 2022-04-26T21:38:55,281 ``` 2022-04-26T21:38:55,282 So what is happening under the hood? With these interpretation methods, Gradio runs the prediction multiple times with modified versions of the input. Based on the results, you'll see that the interface automatically highlights the parts of the text (or image, etc.) that contributed increased the likelihood of the class as red. The intensity of color corresponds to the importance of that part of the input. The parts that decrease the class confidence are highlighted blue. 2022-04-26T21:38:55,283 You can also write your own interpretation function. The demo below adds custom interpretation to the previous demo. This function will take the same inputs as the main wrapped function. The output of this interpretation function will be used to highlight the input of each input interface - therefore the number of outputs here corresponds to the number of input interfaces. To see the format for interpretation for each input interface, check the Docs. 2022-04-26T21:38:55,284 ```python 2022-04-26T21:38:55,284 import re 2022-04-26T21:38:55,285 import gradio as gr 2022-04-26T21:38:55,286 male_words, female_words = ["he", "his", "him"], ["she", "hers", "her"] 2022-04-26T21:38:55,288 def gender_of_sentence(sentence): 2022-04-26T21:38:55,288 male_count = len([word for word in sentence.split() if word.lower() in male_words]) 2022-04-26T21:38:55,289 female_count = len( 2022-04-26T21:38:55,290 [word for word in sentence.split() if word.lower() in female_words] 2022-04-26T21:38:55,290 ) 2022-04-26T21:38:55,291 total = max(male_count + female_count, 1) 2022-04-26T21:38:55,291 return {"male": male_count / total, "female": female_count / total} 2022-04-26T21:38:55,293 def interpret_gender(sentence): 2022-04-26T21:38:55,293 result = gender_of_sentence(sentence) 2022-04-26T21:38:55,294 is_male = result["male"] > result["female"] 2022-04-26T21:38:55,294 interpretation = [] 2022-04-26T21:38:55,295 for word in re.split("( )", sentence): 2022-04-26T21:38:55,295 score = 0 2022-04-26T21:38:55,296 token = word.lower() 2022-04-26T21:38:55,296 if (is_male and token in male_words) or (not is_male and token in female_words): 2022-04-26T21:38:55,297 score = 1 2022-04-26T21:38:55,297 elif (is_male and token in female_words) or ( 2022-04-26T21:38:55,298 not is_male and token in male_words 2022-04-26T21:38:55,298 ): 2022-04-26T21:38:55,299 score = -1 2022-04-26T21:38:55,299 interpretation.append((word, score)) 2022-04-26T21:38:55,300 return interpretation 2022-04-26T21:38:55,302 iface = gr.Interface( 2022-04-26T21:38:55,302 fn=gender_of_sentence, 2022-04-26T21:38:55,303 inputs=gr.inputs.Textbox(default="She went to his house to get her keys."), 2022-04-26T21:38:55,303 outputs="label", 2022-04-26T21:38:55,304 interpretation=interpret_gender, 2022-04-26T21:38:55,304 enable_queue=True, 2022-04-26T21:38:55,305 ) 2022-04-26T21:38:55,305 iface.launch() 2022-04-26T21:38:55,306 ``` 2022-04-26T21:38:55,307 ### Themes and Custom Styling 2022-04-26T21:38:55,308 If you'd like to change how your interface looks, you can select a different theme by simply passing in the `theme` parameter, like so: 2022-04-26T21:38:55,309 ```python 2022-04-26T21:38:55,310 gr.Interface(fn=classify_image, inputs=image, outputs=label, theme="huggingface").launch() 2022-04-26T21:38:55,310 ``` 2022-04-26T21:38:55,311 Here are the themes we currently support: `"default"`, `"huggingface"`, `"grass"`, `"peach"`, and the dark themes corresponding to each of these: `"darkdefault"`, `"darkhuggingface"`, `"darkgrass"`, `"darkpeach"`. 2022-04-26T21:38:55,312 If you'd like to have more fine-grained control over any aspect of the app, you can also write your own css or pass in a css file, with the `css` parameter of the `Interface` class. 2022-04-26T21:38:55,313 ### Custom Flagging Options 2022-04-26T21:38:55,314 In some cases, you might like to provide your users or testers with *more* than just a binary option to flag a sample. You can provide `flagging_options` that they select from a dropdown each time they click the flag button. This lets them provide additional feedback every time they flag a sample. 2022-04-26T21:38:55,315 Here's an example: 2022-04-26T21:38:55,316 ```python 2022-04-26T21:38:55,317 gr.Interface(fn=classify_image, inputs=image, outputs=label, flagging_options=["incorrect", "ambiguous", "offensive", "other"]).launch() 2022-04-26T21:38:55,317 ``` 2022-04-26T21:38:55,318 ### Loading Hugging Face Models and Spaces 2022-04-26T21:38:55,319 Gradio integrates nicely with the Hugging Face Hub, allowing you to load models and Spaces with just one line of code. To use this, simply use the `load()` method in the `Interface` class. So: 2022-04-26T21:38:55,320 - To load any model from the Hugging Face Hub and create an interface around it, you pass `"model/"` or `"huggingface/"` followed by the model name, like these examples: 2022-04-26T21:38:55,321 ```python 2022-04-26T21:38:55,322 gr.Interface.load("huggingface/gpt2").launch(); 2022-04-26T21:38:55,322 ``` 2022-04-26T21:38:55,323 ```python 2022-04-26T21:38:55,324 gr.Interface.load("huggingface/EleutherAI/gpt-j-6B", 2022-04-26T21:38:55,324 inputs=gr.inputs.Textbox(lines=5, label="Input Text") # customizes the input component 2022-04-26T21:38:55,325 ).launch() 2022-04-26T21:38:55,326 ``` 2022-04-26T21:38:55,327 - To load any Space from the Hugging Face Hub and recreate it locally (so that you can customize the inputs and outputs for example), you pass `"spaces/"` followed by the model name: 2022-04-26T21:38:55,328 ```python 2022-04-26T21:38:55,328 gr.Interface.load("spaces/eugenesiow/remove-bg", inputs="webcam", title="Remove your webcam background!").launch() 2022-04-26T21:38:55,329 ``` 2022-04-26T21:38:55,330 One of the great things about loading Hugging Face models or spaces using Gradio is that you can then immediately use the resulting `Interface` object just like function in your Python code (this works for every type of model/space: text, images, audio, video, and even multimodal models): 2022-04-26T21:38:55,331 ```python 2022-04-26T21:38:55,331 io = gr.Interface.load("models/EleutherAI/gpt-neo-2.7B") 2022-04-26T21:38:55,332 io("It was the best of times") # outputs model completion 2022-04-26T21:38:55,332 ``` 2022-04-26T21:38:55,333 ### Putting Interfaces in Parallel and Series 2022-04-26T21:38:55,334 Gradio also lets you mix interfaces very easily using the `gradio.Parallel` and `gradio.Series` classes. `Parallel` lets you put two similar models (if they have the same input type) in parallel to compare model predictions: 2022-04-26T21:38:55,335 ```python 2022-04-26T21:38:55,335 generator1 = gr.Interface.load("huggingface/gpt2") 2022-04-26T21:38:55,336 generator2 = gr.Interface.load("huggingface/EleutherAI/gpt-neo-2.7B") 2022-04-26T21:38:55,336 generator3 = gr.Interface.load("huggingface/EleutherAI/gpt-j-6B") 2022-04-26T21:38:55,338 gr.Parallel(generator1, generator2, generator3).launch() 2022-04-26T21:38:55,338 ``` 2022-04-26T21:38:55,339 `Series` lets you put models and spaces in series, piping the output of one model into the input of the next model. 2022-04-26T21:38:55,340 ```python 2022-04-26T21:38:55,341 generator = gr.Interface.load("huggingface/gpt2") 2022-04-26T21:38:55,341 translator = gr.Interface.load("huggingface/t5-small") 2022-04-26T21:38:55,342 gr.Series(generator, translator).launch() # this demo generates text, then translates it to German, and outputs the final result. 2022-04-26T21:38:55,343 ``` 2022-04-26T21:38:55,344 And of course, you can also mix `Parallel` and `Series` together whenever that makes sense! 2022-04-26T21:38:55,345 ### Queuing to Manage Long Inference Times 2022-04-26T21:38:55,346 If many people are using your interface or if the inference time of your function is long (> 1min), simply set the `enable_queue` parameter in the `launch` method to `True` to prevent timeouts. 2022-04-26T21:38:55,347 ```python 2022-04-26T21:38:55,347 gr.Interface(fn=classify_image, inputs=image, outputs=label).launch(enable_queue=True) 2022-04-26T21:38:55,348 ``` 2022-04-26T21:38:55,349 This sets up a queue of workers to handle the predictions and return the response to the front end. This is strongly recommended if you are planning on uploading your demo to Hugging Face Spaces (as described above) so that you can manage a large number of users simultaneously using your demo. 2022-04-26T21:38:55,352 ## System Requirements: 2022-04-26T21:38:55,353 Gradio requires Python `3.7+` and has been tested on the latest versions of Windows, MacOS, and various common Linux distributions (e.g. Ubuntu). For Python package requirements, please see the `setup.py` file. 2022-04-26T21:38:55,354 ## Contributing: 2022-04-26T21:38:55,355 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](https://github.com/gradio-app/gradio/blob/master/CONTRIBUTING.md) for more info. 2022-04-26T21:38:55,356 ## License: 2022-04-26T21:38:55,357 Gradio is licensed under the Apache License 2.0 2022-04-26T21:38:55,359 ## See more: 2022-04-26T21:38:55,360 You can find many more examples as well as more info on usage on our website: www.gradio.app 2022-04-26T21:38:55,361 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. 2022-04-26T21:38:55,362 ``` 2022-04-26T21:38:55,362 @article{abid2019gradio, 2022-04-26T21:38:55,363 title={Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild}, 2022-04-26T21:38:55,363 author={Abid, Abubakar and Abdalla, Ali and Abid, Ali and Khan, Dawood and Alfozan, Abdulrahman and Zou, James}, 2022-04-26T21:38:55,364 journal={arXiv preprint arXiv:1906.02569}, 2022-04-26T21:38:55,364 year={2019} 2022-04-26T21:38:55,365 } 2022-04-26T21:38:55,365 ``` 2022-04-26T21:38:55,622 running bdist_wheel 2022-04-26T21:38:55,639 running build 2022-04-26T21:38:55,640 running build_py 2022-04-26T21:38:55,983 creating build 2022-04-26T21:38:55,984 creating build/lib 2022-04-26T21:38:55,985 creating build/lib/gradio 2022-04-26T21:38:55,988 copying gradio/strings.py -> build/lib/gradio 2022-04-26T21:38:55,992 copying gradio/blocks.py -> build/lib/gradio 2022-04-26T21:38:55,999 copying gradio/components.py -> build/lib/gradio 2022-04-26T21:38:56,014 copying gradio/utils.py -> build/lib/gradio 2022-04-26T21:38:56,021 copying gradio/processing_utils.py -> build/lib/gradio 2022-04-26T21:38:56,029 copying gradio/outputs.py -> build/lib/gradio 2022-04-26T21:38:56,036 copying gradio/interface.py -> build/lib/gradio 2022-04-26T21:38:56,045 copying gradio/queueing.py -> build/lib/gradio 2022-04-26T21:38:56,052 copying gradio/external.py -> build/lib/gradio 2022-04-26T21:38:56,060 copying gradio/routes.py -> build/lib/gradio 2022-04-26T21:38:56,067 copying gradio/flagging.py -> build/lib/gradio 2022-04-26T21:38:56,074 copying gradio/interpretation.py -> build/lib/gradio 2022-04-26T21:38:56,080 copying gradio/encryptor.py -> build/lib/gradio 2022-04-26T21:38:56,086 copying gradio/tunneling.py -> build/lib/gradio 2022-04-26T21:38:56,091 copying gradio/inputs.py -> build/lib/gradio 2022-04-26T21:38:56,099 copying gradio/templates.py -> build/lib/gradio 2022-04-26T21:38:56,104 copying gradio/networking.py -> build/lib/gradio 2022-04-26T21:38:56,111 copying gradio/__init__.py -> build/lib/gradio 2022-04-26T21:38:56,116 copying gradio/context.py -> build/lib/gradio 2022-04-26T21:38:56,121 copying gradio/mix.py -> build/lib/gradio 2022-04-26T21:38:56,126 copying gradio/media_data.py -> build/lib/gradio 2022-04-26T21:38:56,229 copying gradio/process_examples.py -> build/lib/gradio 2022-04-26T21:38:56,233 running egg_info 2022-04-26T21:38:56,265 writing dependency_links to gradio.egg-info/dependency_links.txt 2022-04-26T21:38:56,267 writing gradio.egg-info/PKG-INFO 2022-04-26T21:38:56,276 writing top-level names to gradio.egg-info/top_level.txt 2022-04-26T21:38:56,278 writing requirements to gradio.egg-info/requires.txt 2022-04-26T21:38:56,343 reading manifest file 'gradio.egg-info/SOURCES.txt' 2022-04-26T21:38:56,354 reading manifest template 'MANIFEST.in' 2022-04-26T21:38:56,477 writing manifest file 'gradio.egg-info/SOURCES.txt' 2022-04-26T21:38:56,506 copying gradio/version.txt -> build/lib/gradio 2022-04-26T21:38:56,511 creating build/lib/gradio/templates 2022-04-26T21:38:56,512 copying gradio/templates/api_docs.html -> build/lib/gradio/templates 2022-04-26T21:38:56,522 creating build/lib/gradio/templates/frontend 2022-04-26T21:38:56,524 copying gradio/templates/frontend/favicon.png -> build/lib/gradio/templates/frontend 2022-04-26T21:38:56,531 copying gradio/templates/frontend/index.html -> build/lib/gradio/templates/frontend 2022-04-26T21:38:56,537 creating build/lib/gradio/templates/frontend/assets 2022-04-26T21:38:56,540 copying gradio/templates/frontend/assets/Block.1239058b.js -> build/lib/gradio/templates/frontend/assets 2022-04-26T21:38:56,547 copying gradio/templates/frontend/assets/BlockLabel.70e6bfff.js -> build/lib/gradio/templates/frontend/assets 2022-04-26T21:38:56,553 copying gradio/templates/frontend/assets/BlockTitle.0ab46369.js -> build/lib/gradio/templates/frontend/assets 2022-04-26T21:38:56,560 copying gradio/templates/frontend/assets/CarouselItem.svelte_svelte_type_style_lang.6d872871.css -> build/lib/gradio/templates/frontend/assets 2022-04-26T21:38:56,565 copying gradio/templates/frontend/assets/CarouselItem.svelte_svelte_type_style_lang.d1af9713.js -> build/lib/gradio/templates/frontend/assets 2022-04-26T21:38:56,570 copying gradio/templates/frontend/assets/Image.5da60e4b.js -> build/lib/gradio/templates/frontend/assets 2022-04-26T21:38:56,575 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gradio/templates/frontend/assets/index.ca19f3d1.css -> build/lib/gradio/templates/frontend/assets 2022-04-26T21:38:57,683 copying gradio/templates/frontend/assets/index.d4156a59.js -> build/lib/gradio/templates/frontend/assets 2022-04-26T21:38:57,688 copying gradio/templates/frontend/assets/index.d4d6897f.js -> build/lib/gradio/templates/frontend/assets 2022-04-26T21:38:57,693 copying gradio/templates/frontend/assets/index.d837f945.js -> build/lib/gradio/templates/frontend/assets 2022-04-26T21:38:57,698 copying gradio/templates/frontend/assets/index.dc017276.js -> build/lib/gradio/templates/frontend/assets 2022-04-26T21:38:57,704 copying gradio/templates/frontend/assets/index.dd17a15f.js -> build/lib/gradio/templates/frontend/assets 2022-04-26T21:38:57,710 copying gradio/templates/frontend/assets/index.e409a55c.css -> build/lib/gradio/templates/frontend/assets 2022-04-26T21:38:57,715 copying gradio/templates/frontend/assets/index.eee45bff.js -> build/lib/gradio/templates/frontend/assets 2022-04-26T21:38:57,720 copying gradio/templates/frontend/assets/index.f5e79f7f.js -> build/lib/gradio/templates/frontend/assets 2022-04-26T21:38:57,725 copying gradio/templates/frontend/assets/music.33e74bdf.svg -> build/lib/gradio/templates/frontend/assets 2022-04-26T21:38:57,730 copying gradio/templates/frontend/assets/square.4e6e3654.svg -> build/lib/gradio/templates/frontend/assets 2022-04-26T21:38:57,734 copying gradio/templates/frontend/assets/undo.f4cff58b.svg -> build/lib/gradio/templates/frontend/assets 2022-04-26T21:38:57,739 copying gradio/templates/frontend/assets/utils.49185b04.js -> build/lib/gradio/templates/frontend/assets 2022-04-26T21:38:57,743 copying gradio/templates/frontend/assets/video.5c567403.svg -> build/lib/gradio/templates/frontend/assets 2022-04-26T21:38:57,748 creating build/lib/gradio/templates/frontend/static 2022-04-26T21:38:57,749 creating build/lib/gradio/templates/frontend/static/img 2022-04-26T21:38:57,751 copying gradio/templates/frontend/static/img/Bunny.obj -> build/lib/gradio/templates/frontend/static/img 2022-04-26T21:38:57,775 copying gradio/templates/frontend/static/img/Duck.glb -> build/lib/gradio/templates/frontend/static/img 2022-04-26T21:38:57,793 copying gradio/templates/frontend/static/img/api-logo.svg -> build/lib/gradio/templates/frontend/static/img 2022-04-26T21:38:57,800 copying gradio/templates/frontend/static/img/camera.svg -> build/lib/gradio/templates/frontend/static/img 2022-04-26T21:38:57,805 copying gradio/templates/frontend/static/img/clear.svg -> build/lib/gradio/templates/frontend/static/img 2022-04-26T21:38:57,809 copying gradio/templates/frontend/static/img/edit.svg -> build/lib/gradio/templates/frontend/static/img 2022-04-26T21:38:57,814 copying gradio/templates/frontend/static/img/logo.svg -> build/lib/gradio/templates/frontend/static/img 2022-04-26T21:38:57,822 copying gradio/templates/frontend/static/img/logo_error.svg -> build/lib/gradio/templates/frontend/static/img 2022-04-26T21:38:57,831 copying gradio/templates/frontend/static/img/undo-solid.svg -> build/lib/gradio/templates/frontend/static/img 2022-04-26T21:38:57,855 installing to build/bdist.linux-armv7l/wheel 2022-04-26T21:38:57,856 running install 2022-04-26T21:38:57,858 running install_lib 2022-04-26T21:38:57,866 creating build/bdist.linux-armv7l 2022-04-26T21:38:57,867 creating build/bdist.linux-armv7l/wheel 2022-04-26T21:38:57,871 creating build/bdist.linux-armv7l/wheel/gradio 2022-04-26T21:38:57,873 copying build/lib/gradio/strings.py -> build/bdist.linux-armv7l/wheel/gradio 2022-04-26T21:38:57,878 copying build/lib/gradio/blocks.py -> build/bdist.linux-armv7l/wheel/gradio 2022-04-26T21:38:57,885 copying build/lib/gradio/components.py -> build/bdist.linux-armv7l/wheel/gradio 2022-04-26T21:38:57,901 copying build/lib/gradio/utils.py -> build/bdist.linux-armv7l/wheel/gradio 2022-04-26T21:38:57,908 copying build/lib/gradio/processing_utils.py -> build/bdist.linux-armv7l/wheel/gradio 2022-04-26T21:38:57,914 copying build/lib/gradio/outputs.py -> build/bdist.linux-armv7l/wheel/gradio 2022-04-26T21:38:57,920 copying build/lib/gradio/interface.py -> build/bdist.linux-armv7l/wheel/gradio 2022-04-26T21:38:57,928 copying build/lib/gradio/queueing.py -> build/bdist.linux-armv7l/wheel/gradio 2022-04-26T21:38:57,934 copying build/lib/gradio/external.py -> build/bdist.linux-armv7l/wheel/gradio 2022-04-26T21:38:57,944 creating build/bdist.linux-armv7l/wheel/gradio/templates 2022-04-26T21:38:57,947 copying build/lib/gradio/templates/api_docs.html -> build/bdist.linux-armv7l/wheel/gradio/templates 2022-04-26T21:38:57,957 creating build/bdist.linux-armv7l/wheel/gradio/templates/frontend 2022-04-26T21:38:57,959 copying build/lib/gradio/templates/frontend/index.html -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend 2022-04-26T21:38:57,963 copying build/lib/gradio/templates/frontend/favicon.png -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend 2022-04-26T21:38:57,969 creating build/bdist.linux-armv7l/wheel/gradio/templates/frontend/static 2022-04-26T21:38:57,973 creating build/bdist.linux-armv7l/wheel/gradio/templates/frontend/static/img 2022-04-26T21:38:57,976 copying build/lib/gradio/templates/frontend/static/img/edit.svg -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/static/img 2022-04-26T21:38:57,982 copying build/lib/gradio/templates/frontend/static/img/camera.svg -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/static/img 2022-04-26T21:38:57,987 copying build/lib/gradio/templates/frontend/static/img/api-logo.svg -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/static/img 2022-04-26T21:38:57,993 copying build/lib/gradio/templates/frontend/static/img/logo_error.svg -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/static/img 2022-04-26T21:38:57,998 copying build/lib/gradio/templates/frontend/static/img/clear.svg -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/static/img 2022-04-26T21:38:58,002 copying build/lib/gradio/templates/frontend/static/img/undo-solid.svg -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/static/img 2022-04-26T21:38:58,007 copying build/lib/gradio/templates/frontend/static/img/Bunny.obj -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/static/img 2022-04-26T21:38:58,031 copying build/lib/gradio/templates/frontend/static/img/Duck.glb -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/static/img 2022-04-26T21:38:58,048 copying build/lib/gradio/templates/frontend/static/img/logo.svg -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/static/img 2022-04-26T21:38:58,057 creating build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,059 copying build/lib/gradio/templates/frontend/assets/index.08be41c3.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,064 copying build/lib/gradio/templates/frontend/assets/index.8b2f2c3a.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,070 copying build/lib/gradio/templates/frontend/assets/index.494e04b8.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,076 copying build/lib/gradio/templates/frontend/assets/Block.1239058b.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,082 copying build/lib/gradio/templates/frontend/assets/edit.8ee16a39.svg -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,087 copying build/lib/gradio/templates/frontend/assets/file.ffdbce7d.svg -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,093 copying build/lib/gradio/templates/frontend/assets/index.03636c83.css -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,098 copying build/lib/gradio/templates/frontend/assets/video.5c567403.svg -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,102 copying build/lib/gradio/templates/frontend/assets/index.7534bec3.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,106 copying build/lib/gradio/templates/frontend/assets/index.48c3fd7b.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,457 copying build/lib/gradio/templates/frontend/assets/file.e562827b.svg -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,462 copying build/lib/gradio/templates/frontend/assets/index.c4ca46bd.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,468 copying build/lib/gradio/templates/frontend/assets/CarouselItem.svelte_svelte_type_style_lang.6d872871.css -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,474 copying build/lib/gradio/templates/frontend/assets/utils.49185b04.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,479 copying build/lib/gradio/templates/frontend/assets/BlockLabel.70e6bfff.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,484 copying build/lib/gradio/templates/frontend/assets/index.04aab694.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,490 copying build/lib/gradio/templates/frontend/assets/index.619f1545.css -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,505 copying build/lib/gradio/templates/frontend/assets/index.dc017276.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,530 copying build/lib/gradio/templates/frontend/assets/Tabs.49dd8780.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,536 copying build/lib/gradio/templates/frontend/assets/ModifyUpload.174395aa.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,542 copying build/lib/gradio/templates/frontend/assets/index.eee45bff.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,548 copying build/lib/gradio/templates/frontend/assets/Model3D.9beb3eb6.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,552 copying build/lib/gradio/templates/frontend/assets/Image.svelte_svelte_type_style_lang.47cf31b6.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,630 copying build/lib/gradio/templates/frontend/assets/Image.svelte_svelte_type_style_lang.a412ec42.css -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,635 copying build/lib/gradio/templates/frontend/assets/chart.3886f367.svg -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,641 copying build/lib/gradio/templates/frontend/assets/image.45facf29.svg -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,646 copying build/lib/gradio/templates/frontend/assets/index.5de99860.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,653 copying build/lib/gradio/templates/frontend/assets/clear.88e065e8.svg -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,658 copying build/lib/gradio/templates/frontend/assets/square.4e6e3654.svg -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,664 copying build/lib/gradio/templates/frontend/assets/index.0e6bbfe1.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,671 copying build/lib/gradio/templates/frontend/assets/CarouselItem.svelte_svelte_type_style_lang.d1af9713.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,676 copying build/lib/gradio/templates/frontend/assets/index.d4156a59.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,682 copying build/lib/gradio/templates/frontend/assets/index.035f535d.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,687 copying build/lib/gradio/templates/frontend/assets/index.d837f945.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,693 copying build/lib/gradio/templates/frontend/assets/music.33e74bdf.svg -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,698 copying build/lib/gradio/templates/frontend/assets/index.2a7e4b97.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,704 copying build/lib/gradio/templates/frontend/assets/index.3c784113.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:58,718 copying build/lib/gradio/templates/frontend/assets/index.220dedad.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:59,068 copying build/lib/gradio/templates/frontend/assets/index.3f5ffbb6.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:59,074 copying build/lib/gradio/templates/frontend/assets/_commonjsHelpers.80d8c10d.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:59,079 copying build/lib/gradio/templates/frontend/assets/index.d4d6897f.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:59,086 copying build/lib/gradio/templates/frontend/assets/undo.f4cff58b.svg -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:59,092 copying build/lib/gradio/templates/frontend/assets/index.3b7472bb.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:59,098 copying build/lib/gradio/templates/frontend/assets/BlockTitle.0ab46369.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:59,103 copying build/lib/gradio/templates/frontend/assets/index.bc17c398.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:59,109 copying build/lib/gradio/templates/frontend/assets/circle.95c5f4a3.svg -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:59,114 copying build/lib/gradio/templates/frontend/assets/index.3b304b06.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:59,120 copying build/lib/gradio/templates/frontend/assets/index.c6180dc0.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:59,125 copying build/lib/gradio/templates/frontend/assets/index.5565f236.css -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:59,140 copying build/lib/gradio/templates/frontend/assets/Image.5da60e4b.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:59,145 copying build/lib/gradio/templates/frontend/assets/index.dd17a15f.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:59,152 copying build/lib/gradio/templates/frontend/assets/index.9a467e12.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:59,161 copying build/lib/gradio/templates/frontend/assets/index.35041fdb.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:59,170 copying build/lib/gradio/templates/frontend/assets/index.1b0e7d36.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:59,176 copying build/lib/gradio/templates/frontend/assets/StatusTracker.eec9373b.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:59,181 copying build/lib/gradio/templates/frontend/assets/StatusTracker.8e1652e2.css -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:59,185 copying build/lib/gradio/templates/frontend/assets/index.013022ba.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:59,192 copying build/lib/gradio/templates/frontend/assets/index.16260897.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:59,199 copying build/lib/gradio/templates/frontend/assets/index.20978623.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:59,205 copying build/lib/gradio/templates/frontend/assets/index.ca19f3d1.css -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:59,210 copying build/lib/gradio/templates/frontend/assets/index.e409a55c.css -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:59,214 copying build/lib/gradio/templates/frontend/assets/index.f5e79f7f.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:59,218 copying build/lib/gradio/templates/frontend/assets/camera.4df08695.svg -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:59,222 copying build/lib/gradio/templates/frontend/assets/index.18705f63.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:59,228 copying build/lib/gradio/templates/frontend/assets/index.3157f7b7.js -> build/bdist.linux-armv7l/wheel/gradio/templates/frontend/assets 2022-04-26T21:38:59,233 copying build/lib/gradio/routes.py -> build/bdist.linux-armv7l/wheel/gradio 2022-04-26T21:38:59,240 copying build/lib/gradio/flagging.py -> build/bdist.linux-armv7l/wheel/gradio 2022-04-26T21:38:59,247 copying build/lib/gradio/interpretation.py -> build/bdist.linux-armv7l/wheel/gradio 2022-04-26T21:38:59,252 copying build/lib/gradio/encryptor.py -> build/bdist.linux-armv7l/wheel/gradio 2022-04-26T21:38:59,256 copying build/lib/gradio/tunneling.py -> build/bdist.linux-armv7l/wheel/gradio 2022-04-26T21:38:59,260 copying build/lib/gradio/inputs.py -> build/bdist.linux-armv7l/wheel/gradio 2022-04-26T21:38:59,268 copying build/lib/gradio/templates.py -> build/bdist.linux-armv7l/wheel/gradio 2022-04-26T21:38:59,275 copying build/lib/gradio/networking.py -> build/bdist.linux-armv7l/wheel/gradio 2022-04-26T21:38:59,283 copying build/lib/gradio/version.txt -> build/bdist.linux-armv7l/wheel/gradio 2022-04-26T21:38:59,288 copying build/lib/gradio/__init__.py -> build/bdist.linux-armv7l/wheel/gradio 2022-04-26T21:38:59,293 copying build/lib/gradio/context.py -> build/bdist.linux-armv7l/wheel/gradio 2022-04-26T21:38:59,297 copying build/lib/gradio/mix.py -> build/bdist.linux-armv7l/wheel/gradio 2022-04-26T21:38:59,302 copying build/lib/gradio/media_data.py -> build/bdist.linux-armv7l/wheel/gradio 2022-04-26T21:38:59,401 copying build/lib/gradio/process_examples.py -> build/bdist.linux-armv7l/wheel/gradio 2022-04-26T21:38:59,406 running install_egg_info 2022-04-26T21:38:59,422 Copying gradio.egg-info to build/bdist.linux-armv7l/wheel/gradio-2.9.0b9-py3.5.egg-info 2022-04-26T21:38:59,453 running install_scripts 2022-04-26T21:38:59,718 creating build/bdist.linux-armv7l/wheel/gradio-2.9.0b9.dist-info/WHEEL 2022-04-26T21:39:02,547 Building wheel for gradio (setup.py): finished with status 'done' 2022-04-26T21:39:02,600 Created wheel for gradio: filename=gradio-2.9.0b9-py3-none-any.whl size=3018042 sha256=ecec199fdde3941ca3091bd7f7ff04eb1adaf8494f3de64e74e855b133bee6a0 2022-04-26T21:39:02,601 Stored in directory: /tmp/pip-ephem-wheel-cache-ywb2q3_z/wheels/ce/ca/78/d6863f871203c700d4f8ba52b0e09e70493b0b5190157bbbc8 2022-04-26T21:39:02,705 Successfully built gradio 2022-04-26T21:39:03,231 Removed build tracker: '/tmp/pip-req-tracker-u4sd56kh'