diff --git a/.gitignore b/.gitignore
index ad70c3aa64..3c6d3c4249 100644
--- a/.gitignore
+++ b/.gitignore
@@ -17,4 +17,5 @@ __pycache__/
*.py[cod]
*$py.class
demo/models/*
-dist/*
\ No newline at end of file
+dist/*
+*.h5
diff --git a/README.md b/README.md
index 9a58b220d2..841a383058 100644
--- a/README.md
+++ b/README.md
@@ -4,14 +4,157 @@
-At Gradio, we often try to understand what inputs that a model is particularly sensitive to. To help facilitate this, we've developed and open-sourced `gradio`, a python library that allows you to easily create input and output interfaces over trained models to make it easy for you to "play around" with your model in your browser by dragging-and-dropping in your own images (or pasting your own text, recording your own voice, etc.) and seeing what the model outputs. We are working on making creating a shareable, public link to your model so you can share the interface with others (e.g. your client, your advisor, or your dad), who can use the model without writing any code.
+At Gradio, we often try to understand what inputs a model is particularly sensitive to. To help facilitate this, we've developed and open-sourced `gradio`, a python library that allows you to quickly create input and output interfaces over trained models to make it easy for you to "play around" with your model in your browser by dragging-and-dropping in your own images (or pasting your own text, recording your own voice, etc.) and seeing what the model outputs. `gradio` can also generate a share link which allows anyone, anywhere to use the interface as the model continues to run on your machine.
Gradio is useful for:
* Creating demos of your machine learning code for clients / collaborators / users
* Getting feedback on model performance from users
* Debugging your model interactively during development
-For more details, see 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.
+To get a sense of `gradio`, take a look at a few of these examples, and find more on our website: www.gradio.app.
+
+## Installation
+```
+pip install gradio
+```
+(you may need to replace `pip` with `pip3` if you're running `python3`).
+
+## Usage
+
+Gradio is very easy to use with your existing code. Here are a few working examples:
+
+### 0. Hello World [](https://colab.research.google.com/drive/18ODkJvyxHutTN0P5APWyGFO_xwNcgHDZ?usp=sharing)
+
+Let's start with a basic function (no machine learning yet!) that greets an input name. We'll wrap the function with a `Text` to `Text` interface.
+
+```python
+import gradio as gr
+
+def greet(name):
+ return "Hello " + name + "!"
+
+gr.Interface(fn=greet, inputs="text", outputs="text").launch()
+```
+
+The core Interface class is initialized with three parameters:
+
+- `fn`: the function to wrap
+- `inputs`: the name of the input interface
+- `outputs`: the name of the output interface
+
+Calling the `launch()` function of the `Interface` object produces the interface shown in image below. Click on the screenshot to go the live interface in our getting started page.
+
+
+
+
+
+### 1. Inception Net [](https://colab.research.google.com/drive/1c6gQiW88wKBwWq96nqEwuQ1Kyt5LejiU?usp=sharing)
+
+Now, let's do a machine learning example. We're going to wrap an
+interface around the InceptionV3 image classifier, which we'll load
+using Tensorflow! Since this is an image classification model, we will use the `Image` input interface.
+We'll output a dictionary of labels and their corresponding confidence scores with the `Label` output
+interface. (The original Inception Net architecture [can be found here](https://arxiv.org/abs/1409.4842))
+
+```python
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+import requests
+
+inception_net = tf.keras.applications.InceptionV3() # load the model
+
+# Download human-readable labels for ImageNet.
+response = requests.get("https://git.io/JJkYN")
+labels = response.text.split("\n")
+
+def classify_image(inp):
+ inp = inp.reshape((-1, 299, 299, 3))
+ inp = tf.keras.applications.inception_v3.preprocess_input(inp)
+ prediction = inception_net.predict(inp).flatten()
+ return {labels[i]: float(prediction[i]) for i in range(1000)}
+
+image = gr.inputs.Image(shape=(299, 299, 3))
+label = gr.outputs.Label(num_top_classes=3)
+
+gr.Interface(fn=classify_image, inputs=image, outputs=label).launch()
+```
+This code will produce the interface below. The interface gives you a way to test
+Inception Net by dragging and dropping images, and also allows you to use naturally modify the input image using image editing tools that
+appear when you click EDIT. Notice here we provided actual `gradio.inputs` and `gradio.outputs` objects to the Interface
+function instead of using string shortcuts. This lets us use built-in preprocessing (e.g. image resizing)
+and postprocessing (e.g. choosing the number of labels to display) provided by these
+interfaces.
+
+
+
+
+
+You can supply your own model instead of the pretrained model above, as well as use different kinds of models or functions. Here's a list of the interfaces we currently support, along with their preprocessing / postprocessing parameters:
+
+**Input Interfaces**:
+- `Sketchpad(shape=(28, 28), invert_colors=True, flatten=False, scale=1/255, shift=0, dtype='float64')`
+- `Webcam(image_width=224, image_height=224, num_channels=3, label=None)`
+- `Textbox(lines=1, placeholder=None, label=None, numeric=False)`
+- `Radio(choices, label=None)`
+- `Dropdown(choices, label=None)`
+- `CheckboxGroup(choices, label=None)`
+- `Slider(minimum=0, maximum=100, default=None, label=None)`
+- `Image(shape=(224, 224, 3), image_mode='RGB', scale=1/127.5, shift=-1, label=None)`
+- `Microphone()`
+
+**Output Interfaces**:
+- `Label(num_top_classes=None, label=None)`
+- `KeyValues(label=None)`
+- `Textbox(lines=1, placeholder=None, label=None)`
+- `Image(label=None, plot=False)`
+
+Interfaces can also be combined together, for multiple-input or multiple-output models.
+
+### 2. Real-Time MNIST [](https://colab.research.google.com/drive/1LXJqwdkZNkt1J_yfLWQ3FLxbG2cAF8p4?usp=sharing)
+
+Let's wrap a fun `Sketchpad`-to-`Label` UI around MNIST. For this example, we'll take advantage of the `live`
+feature in the library. Set `live=True` inside `Interface()`> to have it run continuous predictions.
+We've abstracted the model training from the code below, but you can see the full code on the colab link.
+
+```python
+import tensorflow as tf
+import gradio as gr
+from urllib.request import urlretrieve
+
+urlretrieve("https://gr-models.s3-us-west-2.amazonaws.com/mnist-model.h5","mnist-model.h5")
+model = tf.keras.models.load_model("mnist-model.h5")
+
+def recognize_digit(inp):
+ prediction = model.predict(inp.reshape(1, 28, 28, 1)).tolist()[0]
+ return {str(i): prediction[i] for i in range(10)}
+
+sketchpad = gr.inputs.Sketchpad()
+label = gr.outputs.Label(num_top_classes=3)
+
+gr.Interface(fn=recognize_digit, inputs=sketchpad,
+ outputs=label, live=True).launch()
+```
+
+This code will produce the interface below.
+
+
+
+
+
+## Contributing:
+If you would like to contribute and your contribution is small, you can directly open a pull request (PR). If you would like to contribute a larger feature, we recommend first creating an issue with a proposed design for discussion. Please see our contributing guidelines for more info.
+
+## License:
+Gradio is licensed under the Apache License 2.0
+
+## See more:
+
+You can find many more examples (like GPT-2, model comparison, multiple inputs, and numerical interfaces) as well as more info on usage on our website: www.gradio.app
+
+See, also, the accompanying paper: ["Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild"](https://arxiv.org/pdf/1906.02569.pdf), *ICML HILL 2019*, and please use the citation below.
```
@article{abid2019gradio,
@@ -22,80 +165,4 @@ year={2019}
}
```
-To get a sense of `gradio`, take a look at the at the `examples` and `demo` folders, or read on below! And be sure to visit the gradio website: www.gradio.app.
-
-## Installation
-```
-pip install gradio
-```
-(you may need to replace `pip` with `pip3` if you're running `python3`).
-
-## Usage
-
-Gradio is very easy to use with your existing code. Here's a working example:
-
-
-```python
-import gradio
-import tensorflow as tf
-from imagenetlabels import idx_to_labels
-
-def classify_image(inp):
- inp = inp.reshape((1, 224, 224, 3))
- prediction = mobile_net.predict(inp).flatten()
- return {idx_to_labels[i].split(',')[0]: float(prediction[i]) for i in range(1000)}
-
-imagein = gradio.inputs.Image(shape=(224, 224, 3))
-label = gradio.outputs.Label(num_top_classes=3)
-
-gr.Interface(classify_image, imagein, label, capture_session=True).launch();
-```
-
-
-
-
-You can supply your own model instead of the pretrained model above, as well as use different kinds of models or functions. Changing the `input` and `output` parameters in the `Interface` face object allow you to create different interfaces, depending on the needs of your model. Take a look at the python notebooks for more examples. The currently supported interfaces are as follows:
-
-**Input interfaces**:
-* Sketchpad
-* ImageUplaod
-* Webcam
-* Textbox
-
-**Output interfaces**:
-* Label
-* Textbox
-
-## Screenshots
-
-Here are a few screenshots that show examples of gradio interfaces
-
-#### MNIST Digit Recognition (Input: Sketchpad, Output: Label)
-
-```python
-sketchpad = Sketchpad()
-label = Label(num_top_classes=4)
-
-gradio.Interface(predict, sketchpad, label).launch();
-```
-
-
-
-#### Human DNA Variant Effect Prediction (Input: Textbox, Output: Label)
-
-```python
-gradio.Interface(predict, 'textbox', 'label').launch()
-```
-
-
-
-### Contributing:
-If you would like to contribute and your contribution is small, you can directly open a pull request (PR). If you would like to contribute a larger feature, we recommend first creating an issue with a proposed design for discussion. Please see our contributing guidelines for more info.
-
-### License:
-Gradio is licensed under the Apache License 2.0
-
-### See more:
-Find more info on usage here: www.gradio.app.
-
diff --git a/build/lib/gradio/inputs.py b/build/lib/gradio/inputs.py
index e0cc1ea242..cfd1c51e3f 100644
--- a/build/lib/gradio/inputs.py
+++ b/build/lib/gradio/inputs.py
@@ -40,12 +40,6 @@ class AbstractInput(ABC):
"""
return {"label": self.label}
- def sample_inputs(self):
- """
- An interface can optionally implement a method that sends a list of sample inputs for inference.
- """
- return []
-
def preprocess(self, inp):
"""
By default, no pre-processing is applied to text.
@@ -67,17 +61,12 @@ class AbstractInput(ABC):
class Sketchpad(AbstractInput):
- def __init__(self, cast_to="numpy", shape=(28, 28), invert_colors=True,
- flatten=False, scale=1/255, shift=0,
- dtype='float64', sample_inputs=None, label=None):
+ def __init__(self, shape=(28, 28), invert_colors=True,
+ flatten=False, label=None):
self.image_width = shape[0]
self.image_height = shape[1]
self.invert_colors = invert_colors
self.flatten = flatten
- self.scale = scale
- self.shift = shift
- self.dtype = dtype
- self.sample_inputs = sample_inputs
super().__init__(label)
@classmethod
@@ -101,8 +90,6 @@ class Sketchpad(AbstractInput):
array = np.array(im).flatten().reshape(1, self.image_width * self.image_height)
else:
array = np.array(im).flatten().reshape(1, self.image_width, self.image_height)
- array = array * self.scale + self.shift
- array = array.astype(self.dtype)
return array
def process_example(self, example):
@@ -136,8 +123,7 @@ class Webcam(AbstractInput):
class Textbox(AbstractInput):
- def __init__(self, sample_inputs=None, lines=1, placeholder=None, default=None, label=None, numeric=False):
- self.sample_inputs = sample_inputs
+ def __init__(self, lines=1, placeholder=None, default=None, numeric=False, label=None):
self.lines = lines
self.placeholder = placeholder
self.default = default
@@ -227,7 +213,7 @@ class Slider(AbstractInput):
@classmethod
def get_shortcut_implementations(cls):
return {
- "checkbox": {},
+ "slider": {},
}
@@ -243,8 +229,7 @@ class Checkbox(AbstractInput):
class Image(AbstractInput):
- def __init__(self, cast_to=None, shape=(224, 224), image_mode='RGB', label=None):
- self.cast_to = cast_to
+ def __init__(self, shape=(224, 224), image_mode='RGB', label=None):
self.image_width = shape[0]
self.image_height = shape[1]
self.image_mode = image_mode
@@ -264,29 +249,10 @@ class Image(AbstractInput):
**super().get_template_context()
}
- def cast_to_base64(self, inp):
- return inp
-
- def cast_to_im(self, inp):
- return preprocessing_utils.decode_base64_to_image(inp)
-
- def cast_to_numpy(self, inp):
- im = self.cast_to_im(inp)
- arr = np.array(im).flatten()
- return arr
-
def preprocess(self, inp):
"""
Default preprocessing method for is to convert the picture to black and white and resize to be 48x48
"""
- cast_to_type = {
- "base64": self.cast_to_base64,
- "numpy": self.cast_to_numpy,
- "pillow": self.cast_to_im
- }
- if self.cast_to:
- return cast_to_type[self.cast_to](inp)
-
im = preprocessing_utils.decode_base64_to_image(inp)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
@@ -303,6 +269,8 @@ class Image(AbstractInput):
class Microphone(AbstractInput):
+ def __init__(self, label=None):
+ super().__init__(label)
def preprocess(self, inp):
"""
diff --git a/build/lib/gradio/interface.py b/build/lib/gradio/interface.py
index 2867d5ecdb..7ed0bce563 100644
--- a/build/lib/gradio/interface.py
+++ b/build/lib/gradio/interface.py
@@ -19,9 +19,15 @@ import inspect
from IPython import get_ipython
import sys
import weakref
+import analytics
+import socket
PKG_VERSION_URL = "https://gradio.app/api/pkg-version"
+analytics.write_key = "uxIFddIEuuUcFLf9VgH2teTEtPlWdkNy"
+analytics_url = 'https://api.gradio.app/'
+hostname = socket.gethostname()
+ip_address = socket.gethostbyname(hostname)
class Interface:
@@ -89,6 +95,21 @@ class Interface:
self.simple_server = None
Interface.instances.add(self)
+ data = {'fn': fn,
+ 'inputs': inputs,
+ 'outputs': outputs,
+ 'saliency': saliency,
+ 'live': live,
+ 'capture_session': capture_session,
+ 'host_name': hostname,
+ 'ip_address': ip_address
+ }
+ try:
+ requests.post(analytics_url + 'gradio-initiated-analytics/',
+ data=data)
+ except requests.ConnectionError:
+ print("gradio-initiated-analytics/ Connection Error")
+
def get_config_file(self):
config = {
"input_interfaces": [
@@ -184,6 +205,12 @@ class Interface:
processed_input = self.input_interface.preprocess(msg)
prediction = self.predict(processed_input)
except Exception as e:
+ data = {'error': e}
+ try:
+ requests.post(analytics_url + 'gradio-error-analytics/',
+ data=data)
+ except requests.ConnectionError:
+ print("gradio-error-analytics/ Connection Error")
if self.verbose:
print("\n----------")
print(
@@ -194,6 +221,12 @@ class Interface:
try:
_ = self.output_interface.postprocess(prediction)
except Exception as e:
+ data = {'error': e}
+ try:
+ requests.post(analytics_url + 'gradio-error-analytics/',
+ data=data)
+ except requests.ConnectionError:
+ print("gradio-error-analytics/ Connection Error")
if self.verbose:
print("\n----------")
print(
@@ -250,6 +283,12 @@ class Interface:
is_colab = True
print("Google colab notebook detected.")
except NameError:
+ data = {'error': 'NameError in launch method'}
+ try:
+ requests.post(analytics_url + 'gradio-error-analytics/',
+ data=data)
+ except requests.ConnectionError:
+ print("Connection Error")
pass
try:
@@ -278,6 +317,12 @@ class Interface:
share_url = networking.setup_tunnel(server_port)
print("Running on External URL:", share_url)
except RuntimeError:
+ data = {'error': 'RuntimeError in launch method'}
+ try:
+ requests.post(analytics_url + 'gradio-error-analytics/',
+ data=data)
+ except requests.ConnectionError:
+ print("Connection Error")
share_url = None
if self.verbose:
print(strings.en["NGROK_NO_INTERNET"])
@@ -343,6 +388,19 @@ class Interface:
sys.stdout.flush()
time.sleep(0.1)
+ launch_method = 'browser' if inbrowser else 'inline'
+ data = {'launch_method': launch_method,
+ 'is_google_colab': is_colab,
+ 'is_sharing_on': share,
+ 'share_url': share_url,
+ 'host_name': hostname,
+ 'ip_address': ip_address
+ }
+ try:
+ requests.post(analytics_url + 'gradio-hosted-launched-analytics/',
+ data=data)
+ except requests.ConnectionError:
+ print("Connection Error")
return httpd, path_to_local_server, share_url
@classmethod
diff --git a/build/lib/gradio/networking.py b/build/lib/gradio/networking.py
index bafe9fbda6..5d0853e4c8 100644
--- a/build/lib/gradio/networking.py
+++ b/build/lib/gradio/networking.py
@@ -14,7 +14,7 @@ from gradio.tunneling import create_tunnel
import urllib.request
from shutil import copyfile
import requests
-import os
+import sys
INITIAL_PORT_VALUE = (
@@ -117,7 +117,6 @@ def get_first_available_port(initial, final):
def serve_files_in_background(interface, port, directory_to_serve=None, server_name=LOCALHOST_NAME):
class HTTPHandler(SimpleHTTPRequestHandler):
"""This handler uses server.base_path instead of always using os.getcwd()"""
-
def _set_headers(self):
self.send_response(200)
self.send_header("Content-type", "application/json")
@@ -134,7 +133,6 @@ def serve_files_in_background(interface, port, directory_to_serve=None, server_n
def do_POST(self):
# Read body of the request.
-
if self.path == "/api/predict/":
# Make the prediction.
self._set_headers()
@@ -198,12 +196,13 @@ def serve_files_in_background(interface, port, directory_to_serve=None, server_n
# Now loop forever
def serve_forever():
- # try:
- while True:
- # sys.stdout.flush()
- httpd.serve_forever()
- # except (KeyboardInterrupt, OSError):
- # httpd.server_close()
+ try:
+ while True:
+ sys.stdout.flush()
+ httpd.serve_forever()
+ except (KeyboardInterrupt, OSError):
+ httpd.shutdown()
+ httpd.server_close()
thread = threading.Thread(target=serve_forever, daemon=False)
thread.start()
@@ -215,13 +214,11 @@ def start_simple_server(interface, directory_to_serve=None, server_name=None):
port = get_first_available_port(
INITIAL_PORT_VALUE, INITIAL_PORT_VALUE + TRY_NUM_PORTS
)
- httpd = serve_files_in_background(
- interface, port, directory_to_serve, server_name)
+ httpd = serve_files_in_background(interface, port, directory_to_serve, server_name)
return port, httpd
def close_server(server):
- server.shutdown()
server.server_close()
diff --git a/build/lib/gradio/outputs.py b/build/lib/gradio/outputs.py
index f34c7d2981..dbf87eb00a 100644
--- a/build/lib/gradio/outputs.py
+++ b/build/lib/gradio/outputs.py
@@ -10,6 +10,7 @@ import json
from gradio import preprocessing_utils
import datetime
import operator
+from numbers import Number
# Where to find the static resources associated with each template.
BASE_OUTPUT_INTERFACE_JS_PATH = 'static/js/interfaces/output/{}.js'
@@ -53,7 +54,7 @@ class Label(AbstractOutput):
super().__init__(label)
def postprocess(self, prediction):
- if isinstance(prediction, str):
+ if isinstance(prediction, str) or isinstance(prediction, Number):
return {"label": prediction}
elif isinstance(prediction, dict):
sorted_pred = sorted(
@@ -104,15 +105,11 @@ class KeyValues(AbstractOutput):
class Textbox(AbstractOutput):
- def __init__(self, lines=1, placeholder=None, label=None):
- self.lines = lines
- self.placeholder = placeholder
+ def __init__(self, label=None):
super().__init__(label)
def get_template_context(self):
return {
- "lines": self.lines,
- "placeholder": self.placeholder,
**super().get_template_context()
}
@@ -121,7 +118,6 @@ class Textbox(AbstractOutput):
return {
"text": {},
"number": {},
- "textbox": {"lines": 7}
}
def postprocess(self, prediction):
@@ -133,7 +129,7 @@ class Textbox(AbstractOutput):
class Image(AbstractOutput):
- def __init__(self, label=None, plot=False):
+ def __init__(self, plot=False, label=None):
self.plot = plot
super().__init__(label)
diff --git a/build/lib/gradio/static/css/interfaces/output/textbox.css b/build/lib/gradio/static/css/interfaces/output/textbox.css
index 95b2440800..a6ed5e1ab0 100644
--- a/build/lib/gradio/static/css/interfaces/output/textbox.css
+++ b/build/lib/gradio/static/css/interfaces/output/textbox.css
@@ -1,5 +1,4 @@
.output_text {
- resize: none;
width: 100%;
font-size: 18px;
outline: none;
@@ -8,4 +7,6 @@
border: solid 1px black;
box-sizing: border-box;
padding: 4px;
+ min-height: 30px;
+ font-family: monospace;
}
diff --git a/build/lib/gradio/static/js/all_io.js b/build/lib/gradio/static/js/all_io.js
index 183450f4a5..dcee3bf99e 100644
--- a/build/lib/gradio/static/js/all_io.js
+++ b/build/lib/gradio/static/js/all_io.js
@@ -47,7 +47,11 @@ var io_master_template = {
}
if (this.config.live) {
- this.gather();
+ var io = this;
+ var refresh_lag = this.config.refresh_lag || 0;
+ window.setTimeout(function() {
+ io.gather();
+ }, refresh_lag);
} else {
this.target.find(".loading").addClass("invisible");
this.target.find(".output_interface").removeClass("invisible");
diff --git a/build/lib/gradio/static/js/all_io.js.bak b/build/lib/gradio/static/js/all_io.js.bak
new file mode 100644
index 0000000000..183450f4a5
--- /dev/null
+++ b/build/lib/gradio/static/js/all_io.js.bak
@@ -0,0 +1,73 @@
+var io_master_template = {
+ gather: function() {
+ this.clear();
+ for (let iface of this.input_interfaces) {
+ iface.submit();
+ }
+ },
+ clear: function() {
+ this.last_input = new Array(this.input_interfaces.length);
+ this.input_count = 0;
+ },
+ input: function(interface_id, data) {
+ this.last_input[interface_id] = data;
+ this.input_count += 1;
+ if (this.input_count == this.input_interfaces.length) {
+ this.submit();
+ }
+ },
+ submit: function() {
+ let io = this;
+ if (!this.config.live) {
+ this.target.find(".loading").removeClass("invisible");
+ this.target.find(".loading_in_progress").show();
+ this.target.find(".loading_failed").hide();
+ this.target.find(".output_interface").addClass("invisible");
+ this.target.find(".output_interfaces .panel_header").addClass("invisible");
+ }
+ this.fn(this.last_input).then((output) => {
+ io.output(output);
+ }).catch((error) => {
+ console.error(error);
+ this.target.find(".loading_in_progress").hide();
+ this.target.find(".loading_failed").show();
+ })
+ },
+ output: function(data) {
+ this.last_output = data["data"];
+
+ for (let i = 0; i < this.output_interfaces.length; i++) {
+ this.output_interfaces[i].output(data["data"][i]);
+ }
+ if (data["durations"]) {
+ let ratio = this.output_interfaces.length / data["durations"].length;
+ for (let i = 0; i < this.output_interfaces.length; i = i + ratio) {
+ this.output_interfaces[i].target.parent().find(`.loading_time[interface="${i + ratio - 1}"]`).text("Latency: " + ((data["durations"][i / ratio])).toFixed(2) + "s");
+ }
+ }
+
+ if (this.config.live) {
+ this.gather();
+ } else {
+ this.target.find(".loading").addClass("invisible");
+ this.target.find(".output_interface").removeClass("invisible");
+ this.target.find(".output_interfaces .panel_header").removeClass("invisible");
+ }
+ },
+ flag: function(message) {
+ var post_data = {
+ 'data': {
+ 'input_data' : toStringIfObject(this.last_input) ,
+ 'output_data' : toStringIfObject(this.last_output),
+ 'message' : message
+ }
+ }
+ $.ajax({type: "POST",
+ url: "/api/flag/",
+ data: JSON.stringify(post_data),
+ success: function(output){
+ console.log("Flagging successful")
+ },
+ });
+ }
+};
diff --git a/build/lib/gradio/static/js/interfaces/output/textbox.js b/build/lib/gradio/static/js/interfaces/output/textbox.js
index afa3fa4a6c..ebbe239ac6 100644
--- a/build/lib/gradio/static/js/interfaces/output/textbox.js
+++ b/build/lib/gradio/static/js/interfaces/output/textbox.js
@@ -1,13 +1,7 @@
const textbox_output = {
- html: ``,
+ html: ``,
init: function(opts) {
- if (opts.lines) {
- this.target.find(".output_text").attr("rows", opts.lines).css("height", "auto");
- this.target.css("height", "auto");
- }
- if (opts.placeholder) {
- this.target.find(".output_text").attr("placeholder", opts.placeholder)
- }
+ this.target.css("height", "auto");
},
output: function(data) {
this.target.find(".output_text").text(data);
diff --git a/build/lib/gradio/templates/index.html b/build/lib/gradio/templates/index.html
index 0bf35a859f..303750bcb0 100644
--- a/build/lib/gradio/templates/index.html
+++ b/build/lib/gradio/templates/index.html
@@ -31,7 +31,7 @@
+
-At Gradio, we often try to understand what inputs that a model is particularly sensitive to. To help facilitate this, we've developed and open-sourced `gradio`, a python library that allows you to easily create input and output interfaces over trained models to make it easy for you to "play around" with your model in your browser by dragging-and-dropping in your own images (or pasting your own text, recording your own voice, etc.) and seeing what the model outputs. We are working on making creating a shareable, public link to your model so you can share the interface with others (e.g. your client, your advisor, or your dad), who can use the model without writing any code.
+At Gradio, we often try to understand what inputs a model is particularly sensitive to. To help facilitate this, we've developed and open-sourced `gradio`, a python library that allows you to quickly create input and output interfaces over trained models to make it easy for you to "play around" with your model in your browser by dragging-and-dropping in your own images (or pasting your own text, recording your own voice, etc.) and seeing what the model outputs. `gradio` can also generate a share link which allows anyone, anywhere to use the interface as the model continues to run on your machine.
Gradio is useful for:
* Creating demos of your machine learning code for clients / collaborators / users
* Getting feedback on model performance from users
* Debugging your model interactively during development
-For more details, see 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.
+To get a sense of `gradio`, take a look at a few of these examples, and find more on our website: www.gradio.app.
+
+## Installation
+```
+pip install gradio
+```
+(you may need to replace `pip` with `pip3` if you're running `python3`).
+
+## Usage
+
+Gradio is very easy to use with your existing code. Here are a few working examples:
+
+### 0. Hello World [](https://colab.research.google.com/drive/18ODkJvyxHutTN0P5APWyGFO_xwNcgHDZ?usp=sharing)
+
+Let's start with a basic function (no machine learning yet!) that greets an input name. We'll wrap the function with a `Text` to `Text` interface.
+
+```python
+import gradio as gr
+
+def greet(name):
+ return "Hello " + name + "!"
+
+gr.Interface(fn=greet, inputs="text", outputs="text").launch()
+```
+
+The core Interface class is initialized with three parameters:
+
+- `fn`: the function to wrap
+- `inputs`: the name of the input interface
+- `outputs`: the name of the output interface
+
+Calling the `launch()` function of the `Interface` object produces the interface shown in image below. Click on the screenshot to go the live interface in our getting started page.
+
+
+
+
+