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support auto output datatype

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This commit is contained in:
Ali Abid 2020-09-02 09:18:00 -07:00
parent 95c7b78b60
commit c6585b5da6
20 changed files with 214 additions and 157 deletions
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6
build/lib/gradio/inputs.py
View File

@ -294,12 +294,6 @@ class Image(InputComponent):
im.save(file_obj.name)
return file_obj
def process_example(self, example):
if os.path.exists(example):
return processing_utils.encode_file_to_base64(example)
else:
return example
def rebuild(self, dir, data):
"""
Default rebuild method to decode a base64 image

11
build/lib/gradio/networking.py
View File

@ -197,6 +197,17 @@ def serve_files_in_background(interface, port, directory_to_serve=None, server_n
else:
self.send_error(404, 'Path not found: {}'.format(self.path))
def do_GET(self):
if self.path.startswith("/file/"):
self.send_response(200)
self.end_headers()
with open(self.path[6:], "rb") as f:
self.wfile.write(f.read())
else:
super().do_GET()
class HTTPServer(BaseHTTPServer):
"""The main server, you pass in base_path which is the path you want to serve requests from"""

75
build/lib/gradio/outputs.py
View File

@ -15,6 +15,9 @@ import warnings
import tempfile
import scipy
import os
import pandas as pd
import PIL
from types import ModuleType
class OutputComponent(Component):
"""
@ -28,10 +31,10 @@ class Textbox(OutputComponent):
Output type: Union[str, float, int]
'''
def __init__(self, type="str", label=None):
def __init__(self, type="auto", label=None):
'''
Parameters:
type (str): Type of value to be passed to component. "str" expects a string, "number" expects a float value.
type (str): Type of value to be passed to component. "str" expects a string, "number" expects a float value, "auto" detects return type.
label (str): component name in interface.
'''
self.type = type
@ -51,7 +54,7 @@ class Textbox(OutputComponent):
}
def postprocess(self, y):
if self.type == "str":
if self.type == "str" or self.type == "auto":
return y
elif self.type == "number":
return str(y)
@ -69,19 +72,21 @@ class Label(OutputComponent):
CONFIDENCE_KEY = "confidence"
CONFIDENCES_KEY = "confidences"
def __init__(self, num_top_classes=None, label=None):
def __init__(self, num_top_classes=None, type="auto", label=None):
'''
Parameters:
num_top_classes (int): number of most confident classes to show.
type (str): Type of value to be passed to component. "value" expects a single out label, "confidences" expects a dictionary mapping labels to confidence scores, "auto" detects return type.
label (str): component name in interface.
'''
self.num_top_classes = num_top_classes
self.type = type
super().__init__(label)
def postprocess(self, y):
if isinstance(y, str) or isinstance(y, Number):
return {"label": str(y)}
elif isinstance(y, dict):
if self.type == "label" or (self.type == "auto" and (isinstance(y, str) or isinstance(y, Number))):
return {self.LABEL_KEY: str(y)}
elif self.type == "confidences" or (self.type == "auto" and isinstance(y, dict)):
sorted_pred = sorted(
y.items(),
key=operator.itemgetter(1),
@ -98,8 +103,6 @@ class Label(OutputComponent):
} for pred in sorted_pred
]
}
elif isinstance(y, int) or isinstance(y, float):
return {self.LABEL_KEY: str(y)}
else:
raise ValueError("The `Label` output interface expects one of: a string label, or an int label, a "
"float label, or a dictionary whose keys are labels and values are confidences.")
@ -123,10 +126,10 @@ class Image(OutputComponent):
Output type: Union[numpy.array, PIL.Image, str, matplotlib.pyplot]
'''
def __init__(self, type="numpy", plot=False, label=None):
def __init__(self, type="auto", plot=False, label=None):
'''
Parameters:
type (str): Type of value to be passed to component. "numpy" expects a numpy array with shape (width, height, 3), "pil" expects a PIL image object, "file" expects a file path to the saved image, "plot" expects a matplotlib.pyplot object.
type (str): Type of value to be passed to component. "numpy" expects a numpy array with shape (width, height, 3), "pil" expects a PIL image object, "file" expects a file path to the saved image, "plot" expects a matplotlib.pyplot object, "auto" detects return type.
plot (bool): DEPRECATED. Whether to expect a plot to be returned by the function.
label (str): component name in interface.
'''
@ -146,16 +149,27 @@ class Image(OutputComponent):
}
def postprocess(self, y):
if self.type in ["numpy", "pil"]:
if self.type == "pil":
if self.type == "auto":
if isinstance(y, np.ndarray):
dtype = "numpy"
elif isinstance(y, PIL.Image.Image):
dtype = "pil"
elif isinstance(y, str):
dtype = "file"
elif isinstance(y, ModuleType):
dtype = "plot"
else:
dtype = self.type
if dtype in ["numpy", "pil"]:
if dtype == "pil":
y = np.array(y)
return processing_utils.encode_array_to_base64(y)
elif self.type == "file":
elif dtype == "file":
return processing_utils.encode_file_to_base64(y)
elif self.type == "plot":
elif dtype == "plot":
return processing_utils.encode_plot_to_base64(y)
else:
raise ValueError("Unknown type: " + self.type + ". Please choose from: 'numpy', 'pil', 'file', 'plot'.")
raise ValueError("Unknown type: " + dtype + ". Please choose from: 'numpy', 'pil', 'file', 'plot'.")
def rebuild(self, dir, data):
"""
@ -235,10 +249,10 @@ class Audio(OutputComponent):
Output type: Union[Tuple[int, numpy.array], str]
'''
def __init__(self, type="numpy", label=None):
def __init__(self, type="auto", label=None):
'''
Parameters:
type (str): Type of value to be passed to component. "numpy" returns a 2-set tuple with an integer sample_rate and the data numpy.array of shape (samples, 2), "file" returns a temporary file path to the saved wav audio file.
type (str): Type of value to be passed to component. "numpy" returns a 2-set tuple with an integer sample_rate and the data numpy.array of shape (samples, 2), "file" returns a temporary file path to the saved wav audio file, "auto" detects return type.
label (str): component name in interface.
'''
self.type = type
@ -256,8 +270,8 @@ class Audio(OutputComponent):
}
def postprocess(self, y):
if self.type in ["numpy", "file"]:
if self.type == "numpy":
if self.type in ["numpy", "file", "auto"]:
if self.type == "numpy" or (self.type == "auto" and isinstance(y, tuple)):
file = tempfile.NamedTemporaryFile()
scipy.io.wavfile.write(file, y[0], y[1])
y = file.name
@ -348,11 +362,11 @@ class Dataframe(OutputComponent):
Output type: Union[pandas.DataFrame, numpy.array, List[Union[str, float]], List[List[Union[str, float]]]]
"""
def __init__(self, headers=None, type="pandas", label=None):
def __init__(self, headers=None, type="auto", label=None):
'''
Parameters:
headers (List[str]): Header names to dataframe.
type (str): Type of value to be passed to component. "pandas" for pandas dataframe, "numpy" for numpy array, or "array" for Python array.
type (str): Type of value to be passed to component. "pandas" for pandas dataframe, "numpy" for numpy array, or "array" for Python array, "auto" detects return type.
label (str): component name in interface.
'''
self.type = type
@ -369,17 +383,26 @@ class Dataframe(OutputComponent):
@classmethod
def get_shortcut_implementations(cls):
return {
"dataframe": {"type": "pandas"},
"dataframe": {},
"numpy": {"type": "numpy"},
"matrix": {"type": "array"},
"list": {"type": "array"},
}
def postprocess(self, y):
if self.type == "pandas":
if self.type == "auto":
if isinstance(y, pd.core.frame.DataFrame):
dtype = "pandas"
elif isinstance(y, np.ndarray):
dtype = "numpy"
elif isinstance(y, list):
dtype = "array"
else:
dtype = self.type
if dtype == "pandas":
return {"headers": list(y.columns), "data": y.values.tolist()}
elif self.type in ("numpy", "array"):
if self.type == "numpy":
elif dtype in ("numpy", "array"):
if dtype == "numpy":
y = y.tolist()
if len(y) == 0 or not isinstance(y[0], list):
y = [y]

5
build/lib/gradio/static/css/interfaces/input/textbox.css
View File

@ -1,4 +1,4 @@
.input_text {
textarea.input_text, input.input_text {
resize: none;
width: 100%;
font-size: 18px;
@ -11,6 +11,3 @@
padding: 4px;
font-family: monospace;
}
.input_text_saliency {
display: none;
}

6
build/lib/gradio/static/css/style.css
View File

@ -7,17 +7,13 @@ body#lib {
button, input[type="submit"], input[type="reset"], input[type="text"], input[type="button"], select[type="submit"] {
border: none;
font: inherit;
cursor: pointer;
outline: inherit;
-webkit-appearance: none;
}
select {
font: inherit;
}
label, input[type=radio], input[type=checkbox], select, input[type=range] {
cursor: pointer;
}
button, input[type="submit"], input[type="reset"], input[type="button"], select[type="submit"] {
label, input[type=radio], input[type=checkbox], select, input[type=range], button, input[type="submit"], input[type="reset"], input[type="button"], select[type="submit"] {
cursor: pointer;
}

7
build/lib/gradio/static/js/gradio.js
View File

@ -1,4 +1,4 @@
function gradio(config, fn, target) {
function gradio(config, fn, target, example_file_path) {
target = $(target);
target.html(`
<div class="share invisible">
@ -42,6 +42,7 @@ function gradio(config, fn, target) {
io_master.fn = fn
io_master.target = target;
io_master.config = config;
io_master.example_file_path = example_file_path;
let input_to_object_map = {
"csv" : {},
@ -243,7 +244,7 @@ function gradio(config, fn, target) {
return io_master;
}
function gradio_url(config, url, target) {
function gradio_url(config, url, target, example_file_path) {
return gradio(config, function(data) {
return new Promise((resolve, reject) => {
$.ajax({type: "POST",
@ -253,7 +254,7 @@ function gradio_url(config, url, target) {
error: reject,
});
});
}, target);
}, target, example_file_path);
}
function saveAs(uri, filename) {
var link = document.createElement('a');

39
build/lib/gradio/static/js/interfaces/input/image.js
View File

@ -215,25 +215,28 @@ const image_input = {
}
},
load_example_preview: function(data) {
return "<img src="+data+" height=100>"
return "<img src='"+this.io_master.example_file_path+data+"' height=100>"
},
load_example: function(data) {
load_example: function(example_data) {
example_data = this.io_master.example_file_path + example_data;
let io = this;
if (this.source == "canvas") {
this.clear();
let ctx = this.context;
var img = new Image;
let dimension = this.target.find(".canvas_holder canvas").width();
img.onload = function(){
ctx.clearRect(0,0,dimension,dimension);
ctx.drawImage(img,0,0,dimension,dimension);
};
img.src = data;
} else {
io.target.find(".upload_zone").hide();
io.target.find(".image_display").removeClass("hide");
io.set_image_data(data, /*update_editor=*/true);
io.state = "IMAGE_LOADED";
}
toDataURL(example_data, function(data) {
if (io.source == "canvas") {
io.clear();
let ctx = this.context;
var img = new Image;
let dimension = io.target.find(".canvas_holder canvas").width();
img.onload = function(){
ctx.clearRect(0,0,dimension,dimension);
ctx.drawImage(img,0,0,dimension,dimension);
};
img.src = data;
} else {
io.target.find(".upload_zone").hide();
io.target.find(".image_display").removeClass("hide");
io.set_image_data(data, /*update_editor=*/true);
io.state = "IMAGE_LOADED";
}
})
}
}

22
build/lib/gradio/static/js/interfaces/input/textbox.js
View File

@ -1,9 +1,11 @@
const textbox_input = {
html: `<textarea class="input_text"></textarea>
<div class='input_text_saliency'></div>`,
html: `<textarea class="input_text"></textarea>`,
one_line_html: `<input type="text" class="input_text">`,
init: function(opts) {
if (opts.lines) {
if (opts.lines > 1) {
this.target.find(".input_text").attr("rows", opts.lines).css("height", "auto");
} else {
this.target.html(this.one_line_html);
}
if (opts.placeholder) {
this.target.find(".input_text").attr("placeholder", opts.placeholder)
@ -16,22 +18,8 @@ const textbox_input = {
text = this.target.find(".input_text").val();
this.io_master.input(this.id, text);
},
output: function(data) {
this.target.find(".input_text").hide();
this.target.find(".input_text_saliency").show();
this.target.find(".input_text_saliency").empty();
let html = '';
let text = this.target.find(".input_text").val();
let index = 0;
data.forEach(function(value, index) {
html += `<span style='background-color:rgba(75,150,255,${value})'>${text.charAt(index)}</span>`;
})
$(".input_text_saliency").html(html);
},
clear: function() {
this.target.find(".input_text").val("");
this.target.find(".input_text_saliency").hide();
this.target.find(".input_text").show();
},
load_example: function(data) {
this.target.find(".input_text").val(data);

14
build/lib/gradio/static/js/utils.js
View File

@ -6,6 +6,20 @@ String.prototype.format = function() {
return a
}
function toDataURL(url, callback) {
var xhr = new XMLHttpRequest();
xhr.onload = function() {
var reader = new FileReader();
reader.onloadend = function() {
callback(reader.result);
}
reader.readAsDataURL(xhr.response);
};
xhr.open('GET', url);
xhr.responseType = 'blob';
xhr.send();
}
function resizeImage(base64Str, max_width, max_height, callback) {
var img = new Image();
img.src = base64Str;

2
build/lib/gradio/templates/index.html
View File

@ -118,7 +118,7 @@
<script src="/static/js/gradio.js"></script>
<script>
$.getJSON("static/config.json", function(config) {
io = gradio_url(config, "/api/predict/", "#interface_target");
io = gradio_url(config, "/api/predict/", "#interface_target", "/file/");
});
const copyToClipboard = str => {
const el = document.createElement('textarea');

3
demo/image_mod.py
View File

@ -11,11 +11,10 @@ def image_mod(image):
gr.Interface(image_mod,
gr.inputs.Image(type="pil"),
gr.outputs.Image(type="pil"),
"image",
examples=[
["images/cheetah1.jpg"],
["images/cheetah2.jpg"],
["images/lion.jpg"],
],
live=True,
).launch(share=True)

6
gradio/inputs.py
View File

@ -294,12 +294,6 @@ class Image(InputComponent):
im.save(file_obj.name)
return file_obj
def process_example(self, example):
if os.path.exists(example):
return processing_utils.encode_file_to_base64(example)
else:
return example
def rebuild(self, dir, data):
"""
Default rebuild method to decode a base64 image

11
gradio/networking.py
View File

@ -197,6 +197,17 @@ def serve_files_in_background(interface, port, directory_to_serve=None, server_n
else:
self.send_error(404, 'Path not found: {}'.format(self.path))
def do_GET(self):
if self.path.startswith("/file/"):
self.send_response(200)
self.end_headers()
with open(self.path[6:], "rb") as f:
self.wfile.write(f.read())
else:
super().do_GET()
class HTTPServer(BaseHTTPServer):
"""The main server, you pass in base_path which is the path you want to serve requests from"""

75
gradio/outputs.py
View File

@ -15,6 +15,9 @@ import warnings
import tempfile
import scipy
import os
import pandas as pd
import PIL
from types import ModuleType
class OutputComponent(Component):
"""
@ -28,10 +31,10 @@ class Textbox(OutputComponent):
Output type: Union[str, float, int]
'''
def __init__(self, type="str", label=None):
def __init__(self, type="auto", label=None):
'''
Parameters:
type (str): Type of value to be passed to component. "str" expects a string, "number" expects a float value.
type (str): Type of value to be passed to component. "str" expects a string, "number" expects a float value, "auto" detects return type.
label (str): component name in interface.
'''
self.type = type
@ -53,7 +56,7 @@ class Textbox(OutputComponent):
def postprocess(self, y):
if self.type == "str":
return y
elif self.type == "number":
elif self.type == "number" or self.type == "auto":
return str(y)
else:
raise ValueError("Unknown type: " + self.type + ". Please choose from: 'str', 'number'")
@ -69,19 +72,21 @@ class Label(OutputComponent):
CONFIDENCE_KEY = "confidence"
CONFIDENCES_KEY = "confidences"
def __init__(self, num_top_classes=None, label=None):
def __init__(self, num_top_classes=None, type="auto", label=None):
'''
Parameters:
num_top_classes (int): number of most confident classes to show.
type (str): Type of value to be passed to component. "value" expects a single out label, "confidences" expects a dictionary mapping labels to confidence scores, "auto" detects return type.
label (str): component name in interface.
'''
self.num_top_classes = num_top_classes
self.type = type
super().__init__(label)
def postprocess(self, y):
if isinstance(y, str) or isinstance(y, Number):
return {"label": str(y)}
elif isinstance(y, dict):
if self.type == "label" or (self.type == "auto" and (isinstance(y, str) or isinstance(y, Number))):
return {self.LABEL_KEY: str(y)}
elif self.type == "confidences" or (self.type == "auto" and isinstance(y, dict)):
sorted_pred = sorted(
y.items(),
key=operator.itemgetter(1),
@ -98,8 +103,6 @@ class Label(OutputComponent):
} for pred in sorted_pred
]
}
elif isinstance(y, int) or isinstance(y, float):
return {self.LABEL_KEY: str(y)}
else:
raise ValueError("The `Label` output interface expects one of: a string label, or an int label, a "
"float label, or a dictionary whose keys are labels and values are confidences.")
@ -123,10 +126,10 @@ class Image(OutputComponent):
Output type: Union[numpy.array, PIL.Image, str, matplotlib.pyplot]
'''
def __init__(self, type="numpy", plot=False, label=None):
def __init__(self, type="auto", plot=False, label=None):
'''
Parameters:
type (str): Type of value to be passed to component. "numpy" expects a numpy array with shape (width, height, 3), "pil" expects a PIL image object, "file" expects a file path to the saved image, "plot" expects a matplotlib.pyplot object.
type (str): Type of value to be passed to component. "numpy" expects a numpy array with shape (width, height, 3), "pil" expects a PIL image object, "file" expects a file path to the saved image, "plot" expects a matplotlib.pyplot object, "auto" detects return type.
plot (bool): DEPRECATED. Whether to expect a plot to be returned by the function.
label (str): component name in interface.
'''
@ -146,16 +149,27 @@ class Image(OutputComponent):
}
def postprocess(self, y):
if self.type in ["numpy", "pil"]:
if self.type == "pil":
if self.type == "auto":
if isinstance(y, np.ndarray):
dtype = "numpy"
elif isinstance(y, PIL.Image.Image):
dtype = "pil"
elif isinstance(y, str):
dtype = "file"
elif isinstance(y, ModuleType):
dtype = "plot"
else:
dtype = self.type
if dtype in ["numpy", "pil"]:
if dtype == "pil":
y = np.array(y)
return processing_utils.encode_array_to_base64(y)
elif self.type == "file":
elif dtype == "file":
return processing_utils.encode_file_to_base64(y)
elif self.type == "plot":
elif dtype == "plot":
return processing_utils.encode_plot_to_base64(y)
else:
raise ValueError("Unknown type: " + self.type + ". Please choose from: 'numpy', 'pil', 'file', 'plot'.")
raise ValueError("Unknown type: " + dtype + ". Please choose from: 'numpy', 'pil', 'file', 'plot'.")
def rebuild(self, dir, data):
"""
@ -235,10 +249,10 @@ class Audio(OutputComponent):
Output type: Union[Tuple[int, numpy.array], str]
'''
def __init__(self, type="numpy", label=None):
def __init__(self, type="auto", label=None):
'''
Parameters:
type (str): Type of value to be passed to component. "numpy" returns a 2-set tuple with an integer sample_rate and the data numpy.array of shape (samples, 2), "file" returns a temporary file path to the saved wav audio file.
type (str): Type of value to be passed to component. "numpy" returns a 2-set tuple with an integer sample_rate and the data numpy.array of shape (samples, 2), "file" returns a temporary file path to the saved wav audio file, "auto" detects return type.
label (str): component name in interface.
'''
self.type = type
@ -256,8 +270,8 @@ class Audio(OutputComponent):
}
def postprocess(self, y):
if self.type in ["numpy", "file"]:
if self.type == "numpy":
if self.type in ["numpy", "file", "auto"]:
if self.type == "numpy" or (self.type == "auto" and isinstance(y, tuple)):
file = tempfile.NamedTemporaryFile()
scipy.io.wavfile.write(file, y[0], y[1])
y = file.name
@ -348,11 +362,11 @@ class Dataframe(OutputComponent):
Output type: Union[pandas.DataFrame, numpy.array, List[Union[str, float]], List[List[Union[str, float]]]]
"""
def __init__(self, headers=None, type="pandas", label=None):
def __init__(self, headers=None, type="auto", label=None):
'''
Parameters:
headers (List[str]): Header names to dataframe.
type (str): Type of value to be passed to component. "pandas" for pandas dataframe, "numpy" for numpy array, or "array" for Python array.
type (str): Type of value to be passed to component. "pandas" for pandas dataframe, "numpy" for numpy array, or "array" for Python array, "auto" detects return type.
label (str): component name in interface.
'''
self.type = type
@ -369,17 +383,26 @@ class Dataframe(OutputComponent):
@classmethod
def get_shortcut_implementations(cls):
return {
"dataframe": {"type": "pandas"},
"dataframe": {},
"numpy": {"type": "numpy"},
"matrix": {"type": "array"},
"list": {"type": "array"},
}
def postprocess(self, y):
if self.type == "pandas":
if self.type == "auto":
if isinstance(y, pd.core.frame.DataFrame):
dtype = "pandas"
elif isinstance(y, np.ndarray):
dtype = "numpy"
elif isinstance(y, list):
dtype = "array"
else:
dtype = self.type
if dtype == "pandas":
return {"headers": list(y.columns), "data": y.values.tolist()}
elif self.type in ("numpy", "array"):
if self.type == "numpy":
elif dtype in ("numpy", "array"):
if dtype == "numpy":
y = y.tolist()
if len(y) == 0 or not isinstance(y[0], list):
y = [y]

5
gradio/static/css/interfaces/input/textbox.css
View File

@ -1,4 +1,4 @@
.input_text {
textarea.input_text, input.input_text {
resize: none;
width: 100%;
font-size: 18px;
@ -11,6 +11,3 @@
padding: 4px;
font-family: monospace;
}
.input_text_saliency {
display: none;
}

7
gradio/static/js/gradio.js
View File

@ -1,4 +1,4 @@
function gradio(config, fn, target) {
function gradio(config, fn, target, example_file_path) {
target = $(target);
target.html(`
<div class="share invisible">
@ -42,6 +42,7 @@ function gradio(config, fn, target) {
io_master.fn = fn
io_master.target = target;
io_master.config = config;
io_master.example_file_path = example_file_path;
let input_to_object_map = {
"csv" : {},
@ -243,7 +244,7 @@ function gradio(config, fn, target) {
return io_master;
}
function gradio_url(config, url, target) {
function gradio_url(config, url, target, example_file_path) {
return gradio(config, function(data) {
return new Promise((resolve, reject) => {
$.ajax({type: "POST",
@ -253,7 +254,7 @@ function gradio_url(config, url, target) {
error: reject,
});
});
}, target);
}, target, example_file_path);
}
function saveAs(uri, filename) {
var link = document.createElement('a');

39
gradio/static/js/interfaces/input/image.js
View File

@ -215,25 +215,28 @@ const image_input = {
}
},
load_example_preview: function(data) {
return "<img src="+data+" height=100>"
return "<img src='"+this.io_master.example_file_path+data+"' height=100>"
},
load_example: function(data) {
load_example: function(example_data) {
example_data = this.io_master.example_file_path + example_data;
let io = this;
if (this.source == "canvas") {
this.clear();
let ctx = this.context;
var img = new Image;
let dimension = this.target.find(".canvas_holder canvas").width();
img.onload = function(){
ctx.clearRect(0,0,dimension,dimension);
ctx.drawImage(img,0,0,dimension,dimension);
};
img.src = data;
} else {
io.target.find(".upload_zone").hide();
io.target.find(".image_display").removeClass("hide");
io.set_image_data(data, /*update_editor=*/true);
io.state = "IMAGE_LOADED";
}
toDataURL(example_data, function(data) {
if (io.source == "canvas") {
io.clear();
let ctx = this.context;
var img = new Image;
let dimension = io.target.find(".canvas_holder canvas").width();
img.onload = function(){
ctx.clearRect(0,0,dimension,dimension);
ctx.drawImage(img,0,0,dimension,dimension);
};
img.src = data;
} else {
io.target.find(".upload_zone").hide();
io.target.find(".image_display").removeClass("hide");
io.set_image_data(data, /*update_editor=*/true);
io.state = "IMAGE_LOADED";
}
})
}
}

22
gradio/static/js/interfaces/input/textbox.js
View File

@ -1,9 +1,11 @@
const textbox_input = {
html: `<textarea class="input_text"></textarea>
<div class='input_text_saliency'></div>`,
html: `<textarea class="input_text"></textarea>`,
one_line_html: `<input type="text" class="input_text">`,
init: function(opts) {
if (opts.lines) {
if (opts.lines > 1) {
this.target.find(".input_text").attr("rows", opts.lines).css("height", "auto");
} else {
this.target.html(this.one_line_html);
}
if (opts.placeholder) {
this.target.find(".input_text").attr("placeholder", opts.placeholder)
@ -16,22 +18,8 @@ const textbox_input = {
text = this.target.find(".input_text").val();
this.io_master.input(this.id, text);
},
output: function(data) {
this.target.find(".input_text").hide();
this.target.find(".input_text_saliency").show();
this.target.find(".input_text_saliency").empty();
let html = '';
let text = this.target.find(".input_text").val();
let index = 0;
data.forEach(function(value, index) {
html += `<span style='background-color:rgba(75,150,255,${value})'>${text.charAt(index)}</span>`;
})
$(".input_text_saliency").html(html);
},
clear: function() {
this.target.find(".input_text").val("");
this.target.find(".input_text_saliency").hide();
this.target.find(".input_text").show();
},
load_example: function(data) {
this.target.find(".input_text").val(data);

14
gradio/static/js/utils.js
View File

@ -6,6 +6,20 @@ String.prototype.format = function() {
return a
}
function toDataURL(url, callback) {
var xhr = new XMLHttpRequest();
xhr.onload = function() {
var reader = new FileReader();
reader.onloadend = function() {
callback(reader.result);
}
reader.readAsDataURL(xhr.response);
};
xhr.open('GET', url);
xhr.responseType = 'blob';
xhr.send();
}
function resizeImage(base64Str, max_width, max_height, callback) {
var img = new Image();
img.src = base64Str;

2
gradio/templates/index.html
View File

@ -118,7 +118,7 @@
<script src="/static/js/gradio.js"></script>
<script>
$.getJSON("static/config.json", function(config) {
io = gradio_url(config, "/api/predict/", "#interface_target");
io = gradio_url(config, "/api/predict/", "#interface_target", "/file/");
});
const copyToClipboard = str => {
const el = document.createElement('textarea');