merge conflict on auto datatype

build/lib/gradio/inputs.py

@@ -15,17 +15,19 @@ import base64
 import numpy as np
 import PIL
 import scipy.io.wavfile
-from gradio import processing_utils
+from gradio import processing_utils, test_data
 import pandas as pd
 import math
 import tempfile
 
+
 class InputComponent(Component):
     """
     Input Component. All input components subclass this.
     """
     pass
 
+
 class Textbox(InputComponent):
     """
     Component creates a textbox for user to enter input. Provides a string (or number is `type` is "float") as an argument to the wrapped function.
@@ -33,7 +35,7 @@ class Textbox(InputComponent):
     """
 
     def __init__(self, lines=1, placeholder=None, default=None, numeric=False, type="str", label=None):
-        '''
+        """
         Parameters:
         lines (int): number of line rows to provide in textarea.
         placeholder (str): placeholder hint to provide behind textarea.
@@ -41,7 +43,7 @@ class Textbox(InputComponent):
         numeric (bool): DEPRECATED. Whether the input should be parsed as a number instead of a string.        
         type (str): Type of value to be returned by component. "str" returns a string, "number" returns a float value.
         label (str): component name in interface.
-        '''
+        """
         self.lines = lines
         self.placeholder = placeholder
         self.default = default
@@ -50,6 +52,13 @@ class Textbox(InputComponent):
             self.type = "number"
         else:
             self.type = type
+        if default is None:
+            self.test_input = {
+                "str": "the quick brown fox jumped over the lazy dog",
+                "number": 786.92,
+            }[type]
+        else:
+            self.test_input = default
         super().__init__(label)
 
     def get_template_context(self):
@@ -77,7 +86,6 @@ class Textbox(InputComponent):
             raise ValueError("Unknown type: " + self.type + ". Please choose from: 'str', 'number'.")
 
 
-
 class Slider(InputComponent):
     """
     Component creates a slider that ranges from `minimum` to `maximum`. Provides a number as an argument to the wrapped function.
@@ -101,6 +109,7 @@ class Slider(InputComponent):
             step = 10 ** power
         self.step = step
         self.default = minimum if default is None else default
+        self.test_input = self.default
         super().__init__(label)
 
     def get_template_context(self):
@@ -126,10 +135,11 @@ class Checkbox(InputComponent):
     """
 
     def __init__(self, label=None):
-        '''
+        """
         Parameters:
         label (str): component name in interface.
-        '''
+        """
+        self.test_input = True
         super().__init__(label)
 
     @classmethod
@@ -154,6 +164,7 @@ class CheckboxGroup(InputComponent):
         '''
         self.choices = choices
         self.type = type
+        self.test_input = self.choices
         super().__init__(label)
 
     def get_template_context(self):
@@ -186,6 +197,7 @@ class Radio(InputComponent):
         '''
         self.choices = choices
         self.type = type
+        self.test_input = self.choices[0]
         super().__init__(label)
 
     def get_template_context(self):
@@ -202,6 +214,7 @@ class Radio(InputComponent):
         else:
             raise ValueError("Unknown type: " + self.type + ". Please choose from: 'value', 'index'.")
 
+
 class Dropdown(InputComponent):
     """
     Component creates a dropdown of which only one can be selected. Provides string representing selected choice as an argument to the wrapped function.
@@ -217,6 +230,7 @@ class Dropdown(InputComponent):
         '''
         self.choices = choices
         self.type = type
+        self.test_input = self.choices[0]
         super().__init__(label)
 
     def get_template_context(self):
@@ -248,7 +262,7 @@ class Image(InputComponent):
         invert_colors (bool): whether to invert the image as a preprocessing step.
         source (str): Source of image. "upload" creates a box where user can drop an image file, "webcam" allows user to take snapshot from their webcam, "canvas" defaults to a white image that can be edited and drawn upon with tools.
         tool (str): Tools used for editing. "editor" allows a full screen editor, "select" provides a cropping and zoom tool.
-        type (str): Type of value to be returned by component. "numpy" returns a numpy array with shape (width, height, 3), "pil" returns a PIL image object, "file" returns a temporary file object whose path can be retrieved by file_obj.name.
+        type (str): Type of value to be returned by component. "numpy" returns a numpy array with shape (width, height, 3) and values from 0 to 255, "pil" returns a PIL image object, "file" returns a temporary file object whose path can be retrieved by file_obj.name.
         label (str): component name in interface.
         '''
         self.shape = shape
@@ -257,6 +271,7 @@ class Image(InputComponent):
         self.tool = tool
         self.type = type
         self.invert_colors = invert_colors
+        self.test_input = test_data.BASE64_IMAGE
         super().__init__(label)
 
     @classmethod
@@ -293,6 +308,8 @@ class Image(InputComponent):
             file_obj = tempfile.NamedTemporaryFile()
             im.save(file_obj.name)
             return file_obj
+        else:
+            raise ValueError("Unknown type: " + self.type + ". Please choose from: 'numpy', 'pil', 'file'.")
 
     def rebuild(self, dir, data):
         """
@@ -312,14 +329,15 @@ class Audio(InputComponent):
     """
 
     def __init__(self, source="upload", type="numpy", label=None):
-        '''
+        """
         Parameters:
         source (str): Source of audio. "upload" creates a box where user can drop an audio file, "microphone" creates a microphone input.
         type (str): Type of value to be returned by 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 object whose path can be retrieved by file_obj.name, "mfcc" returns the mfcc coefficients of the input audio.
         label (str): component name in interface.
-        '''
+        """
         self.source = source
         self.type = type
+        self.test_input = test_data.BASE64_AUDIO
         super().__init__(label)
 
     def get_template_context(self):
@@ -361,6 +379,7 @@ class File(InputComponent):
         label (str): component name in interface.
         '''
         self.type = type
+        self.test_input = None
         super().__init__(label)
 
     @classmethod
@@ -385,7 +404,7 @@ class Dataframe(InputComponent):
     """
 
     def __init__(self, headers=None, row_count=3, col_count=3, datatype="str", type="pandas", label=None):
-        '''
+        """
         Parameters:
         headers (List[str]): Header names to dataframe.
         row_count (int): Limit number of rows for input.
@@ -393,14 +412,17 @@ class Dataframe(InputComponent):
         datatype (Union[str, List[str]]): Datatype of values in sheet. Can be provided per column as a list of strings, or for the entire sheet as a single string. Valid datatypes are "str", "number", "bool", and "date".
         type (str): Type of value to be returned by component. "pandas" for pandas dataframe, "numpy" for numpy array, or "array" for a Python array.
         label (str): component name in interface.
-        '''
+        """
         self.headers = headers
         self.datatype = datatype
         self.row_count = row_count
         self.col_count = len(headers) if headers else col_count
         self.type = type
-        super().__init__(label)
+        sample_values = {"str": "abc", "number": 786, "bool": True, "date": "02/08/1993"}
+        column_dtypes = [datatype]*self.col_count if isinstance(datatype, str) else datatype
+        self.test_input = [[sample_values[c] for c in column_dtypes] for _ in range(row_count)]
 
+        super().__init__(label)
 
     def get_template_context(self):
         return {

build/lib/gradio/interface.py

@@ -193,19 +193,19 @@ class Interface:
 
         return config
 
-    def process(self, raw_input):
+    def process(self, raw_input, predict_fn=None):
         """
         :param raw_input: a list of raw inputs to process and apply the
         prediction(s) on.
+        :param predict_fn: which function to process. If not provided, all of the model functions are used.
         :return:
         processed output: a list of processed  outputs to return as the
         prediction(s).
         duration: a list of time deltas measuring inference time for each
         prediction fn.
         """
-        processed_input = [input_interface.preprocess(
+        processed_input = [input_interface.preprocess(raw_input[i])
-            raw_input[i]) for i, input_interface in
+                           for i, input_interface in enumerate(self.input_interfaces)]
-            enumerate(self.input_interfaces)]
         predictions = []
         durations = []
         for predict_fn in self.predict:
@@ -253,6 +253,22 @@ class Interface:
             thread.keep_running = False
             networking.url_ok(path_to_local_server)
 
+    def test_launch(self):
+        for predict_fn in self.predict:
+            print("Test launching: {}()...".format(predict_fn.__name__), end=' ')
+
+            raw_input = []
+            for input_interface in self.input_interfaces:
+                if input_interface.test_input is None:  # If no test input is defined for that input interface
+                    print("SKIPPED")
+                    break
+                else:  # If a test input is defined for each interface object
+                    raw_input.append(input_interface.test_input)
+            else:
+                self.process(raw_input)
+                print("PASSED")
+                continue
+
     def launch(self, inline=None, inbrowser=None, share=False, debug=False):
         """
         Parameters
@@ -326,6 +342,8 @@ class Interface:
                 if self.verbose:
                     print(strings.en["COLAB_NO_LOCAL"])
             else:  # If it's not a colab notebook and share=False, print a message telling them about the share option.
+                print("To get a public link for a hosted model, "
+                      "set Share=True")
                 if self.verbose:
                     print(strings.en["PUBLIC_SHARE_TRUE"])
                 share_url = None
@@ -345,10 +363,10 @@ class Interface:
             # with the interface.
         if inline:
             from IPython.display import IFrame, display
-            if (is_colab):
+            # Embed the remote interface page if on google colab;
-                # Embed the remote interface page if on google colab;
+            # otherwise, embed the local page.
-                # otherwise, embed the local page.
+            print("Interface loading below...")
-                print("Interface loading below...")
+            if share:
                 while not networking.url_ok(share_url):
                     time.sleep(1)
                 display(IFrame(share_url, width=1000, height=500))

build/lib/gradio/processing_utils.py

@@ -32,7 +32,6 @@ def encode_plot_to_base64(plt):
     with BytesIO() as output_bytes:
         plt.savefig(output_bytes, format="png")
         bytes_data = output_bytes.getvalue()
-    plt.close()
     base64_str = str(base64.b64encode(bytes_data), 'utf-8')
     return "data:image/png;base64," + base64_str
 

demo/diff_texts.py

@@ -3,13 +3,15 @@
 import gradio as gr
 from difflib import Differ
 
+
 def diff_texts(text1, text2):
     d = Differ()
     return [
         (token[2:], token[0]) for token in d.compare(text1, text2)
     ]
-    
+
-gr.Interface(
+
+io = gr.Interface(
     diff_texts,
     [
         gr.inputs.Textbox(lines=3, default="The quick brown fox jumped over the lazy dogs."),
@@ -20,4 +22,7 @@ gr.Interface(
         "-": "pink",
         " ": "none",
     })
-).launch()
+)
+
+io.test_launch()
+io.launch()

demo/digit_classifier.py

@@ -2,24 +2,26 @@
 
 import tensorflow as tf
 import gradio
-import os
-from tensorflow.keras.layers import *
 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(image):
     image = image.reshape(1, -1)
     prediction = model.predict(image).tolist()[0]
     return {str(i): prediction[i] for i in range(10)}
 
 
-gr.Interface(
+io = gr.Interface(
     recognize_digit, 
     "sketchpad", 
     gradio.outputs.Label(num_top_classes=3),
     live=True,
     capture_session=True,
-).launch()
+)
+
+io.test_launch()
+io.launch()

demo/filter_records.py

@@ -7,11 +7,14 @@ import random
 def filter_records(records, gender):
     return records[records['gender'] == gender]
 
-gr.Interface(filter_records, 
+io = gr.Interface(filter_records,
   [
     gr.inputs.Dataframe(headers=["name", "age", "gender"], datatype=["str", "number", "str"], row_count=5), 
     gr.inputs.Dropdown(["M", "F", "O"])
   ],
   "dataframe",
   description="Enter gender as 'M', 'F', or 'O' for other."
-).launch()
+)
+
+io.test_launch()
+io.launch()

demo/form_graph.py

@@ -0,0 +1,35 @@
+import gradio as gr
+import random
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+def plot_forecast(final_year, companies, noise, show_legend, point_style):
+    start_year = 2020
+    x = np.arange(start_year, final_year + 1)
+    year_count = x.shape[0]
+    plt_format = ({"cross": "X", "line": "-", "circle": "o--"})[point_style]
+    fig = plt.figure()
+    ax = fig.add_subplot(1)
+    for i, company in enumerate(companies):
+        series = np.arange(0, year_count, dtype=float)
+        series = series ** 2 * (i + 1)
+        series += np.random.rand(year_count) * noise
+        ax.plot(x, series, plt_format)
+    if show_legend:
+        plt.legend(companies)
+    return fig
+
+
+gr.Interface(plot_forecast,
+             [
+                 gr.inputs.Radio([2025, 2030, 2035, 2040],
+                                 label="Project to:"),
+                 gr.inputs.CheckboxGroup(
+                     ["Google", "Microsoft", "Gradio"], label="Company Selection"),
+                 gr.inputs.Slider(1, 100, label="Noise Level"),
+                 gr.inputs.Checkbox(label="Show Legend"),
+                 gr.inputs.Dropdown(["cross", "line", "circle"], label="Style"),
+             ],
+             gr.outputs.Image(plot=True, label="forecast") 
+             ).launch()

demo/generate_tone.py

@@ -2,10 +2,10 @@
 
 import gradio as gr
 import numpy as np
-import random
 
 notes = ["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"]
 
+
 def generate_tone(note, octave, duration):
     sr = 48000
     a4_freq, tones_from_a4 = 440, 12 * (octave - 4) + (note - 9)
@@ -13,13 +13,16 @@ def generate_tone(note, octave, duration):
     duration = int(duration)
     audio = np.linspace(0, duration, duration * sr)
     audio = (20000 * np.sin(audio * (2 * np.pi * frequency))).astype(np.int16)
-    return (sr, audio)
+    return sr, audio
 
 
-gr.Interface(
+io = gr.Interface(
     generate_tone, 
     [
         gr.inputs.Dropdown(notes, type="index"),
         gr.inputs.Slider(4, 6, step=1),
         gr.inputs.Textbox(type="number", default=1, label="Duration in seconds")
-    ], "audio").launch()
+    ], "audio")
+
+io.test_launch()
+io.launch()

demo/image_mod.py

@@ -1,15 +1,13 @@
 # Demo: (Image) -> (Image)
 
 import gradio as gr
-from time import time
-from PIL import Image
 
 
 def image_mod(image):
     return image.rotate(45)
 
-
+  
-gr.Interface(image_mod, 
+io = gr.Interface(image_mod, 
              gr.inputs.Image(type="pil"), 
              "image", 
              examples=[
@@ -17,4 +15,8 @@ gr.Interface(image_mod,
                  ["images/cheetah2.jpg"],
                  ["images/lion.jpg"],
              ],
-             ).launch(share=True)
+             live=True,
+             )
+
+io.test_launch()
+io.launch()

demo/matrix_transpose.py

@@ -1,13 +1,17 @@
 # Demo: (Dataframe) -> (Dataframe)
 
 import gradio as gr
-import numpy as np
+
-import random
 
 def transpose(matrix):
     return matrix.T
 
-gr.Interface(transpose, 
+
-  gr.inputs.Dataframe(type="numpy", datatype="number", row_count=5, col_count=3), 
+io = gr.Interface(
-  "numpy"
+    transpose,
-).launch()
+    gr.inputs.Dataframe(type="numpy", datatype="number", row_count=5, col_count=3),
+    "numpy"
+)
+
+io.test_launch()
+io.launch()

demo/reverse_audio.py

@@ -2,10 +2,14 @@
 
 import gradio as gr
 import numpy as np
-import random
+
 
 def reverse_audio(audio):
     sr, data = audio
     return (sr, np.flipud(data))
 
-gr.Interface(reverse_audio, "microphone", "audio").launch()
+
+io = gr.Interface(reverse_audio, "microphone", "audio")
+
+io.test_launch()
+io.launch()

demo/sentence_builder.py

@@ -1,25 +1,28 @@
 # Demo: (Slider, Dropdown, Radio, CheckboxGroup, Checkbox) -> (Textbox)
 
 import gradio as gr
-import numpy as np
+
 
 def sentence_builder(quantity, animal, place, activity_list, morning):
     return f"""The {quantity} {animal}s went to the {place} where they {" and ".join(activity_list)} until the {"morning" if morning else "night"}"""
 
 
-gr.Interface(sentence_builder, 
+io = gr.Interface(
-            [
+    sentence_builder,
-                gr.inputs.Slider(2, 20),
+    [
-                gr.inputs.Dropdown(["cat", "dog", "bird"]),
+        gr.inputs.Slider(2, 20),
-                gr.inputs.Radio(["park", "zoo", "road"]),
+        gr.inputs.Dropdown(["cat", "dog", "bird"]),
-                gr.inputs.CheckboxGroup(["ran", "swam", "ate", "slept"]),
+        gr.inputs.Radio(["park", "zoo", "road"]),
-                gr.inputs.Checkbox(label="Is it the morning?"),
+        gr.inputs.CheckboxGroup(["ran", "swam", "ate", "slept"]),
-            ], 
+        gr.inputs.Checkbox(label="Is it the morning?"),
-            "text",
+    ],
-            examples=[
+    "text",
-                [2, "cat", "park", ["ran", "swam"], True],
+    examples=[
-                [4, "dog", "zoo", ["ate", "swam"], False],
+        [2, "cat", "park", ["ran", "swam"], True],
-                [10, "bird", "road", ["ran"], False],
+        [4, "dog", "zoo", ["ate", "swam"], False],
-                [8, "cat", "zoo", ["ate"], True],
+        [10, "bird", "road", ["ran"], False],
-            ],
+        [8, "cat", "zoo", ["ate"], True],
-            ).launch()
+    ])
+
+io.test_launch()
+io.launch()

demo/spectogram.py

@@ -4,7 +4,6 @@ import gradio as gr
 import matplotlib.pyplot as plt
 import numpy as np
 from scipy import signal
-from scipy.io import wavfile
 
 
 def spectrogram(audio):
@@ -15,4 +14,7 @@ def spectrogram(audio):
     return plt
 
 
-gr.Interface(spectrogram, "audio", "plot").launch()
+io = gr.Interface(spectrogram, "audio", "plot
+
+io.test_launch()
+io.launch()

demo/stock_forecast.py

@@ -1,7 +1,6 @@
 # Demo: (Radio, CheckboxGroup, Slider, Checkbox, Dropdown) -> (Image)
 
 import gradio as gr
-import random
 import matplotlib.pyplot as plt
 import numpy as np
 
@@ -21,15 +20,15 @@ def stock_forecast(final_year, companies, noise, show_legend, point_style):
     return plt
 
 
-gr.Interface(stock_forecast,
+io = gr.Interface(
-             [
+    stock_forecast,
-                 gr.inputs.Radio([2025, 2030, 2035, 2040],
+    [
-                                 label="Project to:"),
+        gr.inputs.Radio([2025, 2030, 2035, 2040], label="Project to:"),
-                 gr.inputs.CheckboxGroup(
+        gr.inputs.CheckboxGroup(["Google", "Microsoft", "Gradio"]),
-                     ["Google", "Microsoft", "Gradio"]),
+        gr.inputs.Slider(1, 100),
-                 gr.inputs.Slider(1, 100),
+        "checkbox",
-                 "checkbox",
+        gr.inputs.Dropdown(["cross", "line", "circle"], label="Style")],
-                 gr.inputs.Dropdown(["cross", "line", "circle"], label="Style"),
+    gr.outputs.Image(plot=True, label="forecast"))
-             ],
+
-             gr.outputs.Image(plot=True, label="forecast") 
+io.test_launch()
-             ).launch()
+io.launch()

demo/text_analysis.py

@@ -6,6 +6,7 @@ import gradio as gr
 
 nlp = spacy.load("en_core_web_sm")
 
+
 def text_analysis(text):
     doc = nlp(text)
     html = displacy.render(doc, style="dep", page=True)
@@ -21,10 +22,14 @@ def text_analysis(text):
     
     return pos_tokens, pos_count, html
 
-gr.Interface(
+
+io = gr.Interface(
     text_analysis,
     gr.inputs.Textbox(placeholder="Enter sentence here..."),
     [
         "highlight", "key_values", "html"
     ]
-).launch()
+)
+
+io.test_launch()
+io.launch()

demo/webcam.py

@@ -1,9 +1,13 @@
 # Demo: (Image) -> (Image)
 
 import gradio as gr
-import numpy as np
+
 
 def snap(image):
     return np.flipud(image)
 
-gr.Interface(snap, gr.inputs.Image(shape=(100,100), image_mode="L", source="webcam"), "image").launch()
+
+io = gr.Interface(snap, gr.inputs.Image(shape=(100,100), image_mode="L", source="webcam"), "image")
+
+io.test_launch()
+io.launch()

demo/zip_to_json.py

@@ -3,6 +3,7 @@
 import gradio as gr
 from zipfile import ZipFile
 
+
 def zip_to_json(file_obj):
     files = []
     with ZipFile(file_obj.name) as zfile:
@@ -14,4 +15,8 @@ def zip_to_json(file_obj):
             })
     return files
 
-gr.Interface(zip_to_json, "file", "json").launch()
+
+io = gr.Interface(zip_to_json, "file", "json")
+
+io.test_launch()
+io.launch()

demo/zip_two_files.py

@@ -3,10 +3,15 @@
 import gradio as gr
 from zipfile import ZipFile
 
+
 def zip_two_files(file1, file2):
     with ZipFile('tmp.zip', 'w') as zipObj:
         zipObj.write(file1.name, "file1")
         zipObj.write(file2.name, "file2")
     return "tmp.zip"
 
-gr.Interface(zip_two_files, ["file", "file"], "file").launch()
+
+io = gr.Interface(zip_two_files, ["file", "file"], "file")
+
+io.test_launch()
+io.launch()

gradio.egg-info/PKG-INFO

@@ -1,6 +1,6 @@
 Metadata-Version: 1.0
 Name: gradio
-Version: 1.1.7
+Version: 1.1.9
 Summary: Python library for easily interacting with trained machine learning models
 Home-page: https://github.com/gradio-app/gradio-UI
 Author: Abubakar Abid

gradio.egg-info/SOURCES.txt

@@ -9,6 +9,7 @@ gradio/networking.py
 gradio/outputs.py
 gradio/processing_utils.py
 gradio/strings.py
+gradio/test_data.py
 gradio/tunneling.py
 gradio/utils.py
 gradio.egg-info/PKG-INFO

gradio/inputs.py

@@ -15,17 +15,19 @@ import base64
 import numpy as np
 import PIL
 import scipy.io.wavfile
-from gradio import processing_utils
+from gradio import processing_utils, test_data
 import pandas as pd
 import math
 import tempfile
 
+
 class InputComponent(Component):
     """
     Input Component. All input components subclass this.
     """
     pass
 
+
 class Textbox(InputComponent):
     """
     Component creates a textbox for user to enter input. Provides a string (or number is `type` is "float") as an argument to the wrapped function.
@@ -33,7 +35,7 @@ class Textbox(InputComponent):
     """
 
     def __init__(self, lines=1, placeholder=None, default=None, numeric=False, type="str", label=None):
-        '''
+        """
         Parameters:
         lines (int): number of line rows to provide in textarea.
         placeholder (str): placeholder hint to provide behind textarea.
@@ -41,7 +43,7 @@ class Textbox(InputComponent):
         numeric (bool): DEPRECATED. Whether the input should be parsed as a number instead of a string.        
         type (str): Type of value to be returned by component. "str" returns a string, "number" returns a float value.
         label (str): component name in interface.
-        '''
+        """
         self.lines = lines
         self.placeholder = placeholder
         self.default = default
@@ -50,6 +52,13 @@ class Textbox(InputComponent):
             self.type = "number"
         else:
             self.type = type
+        if default is None:
+            self.test_input = {
+                "str": "the quick brown fox jumped over the lazy dog",
+                "number": 786.92,
+            }[type]
+        else:
+            self.test_input = default
         super().__init__(label)
 
     def get_template_context(self):
@@ -77,7 +86,6 @@ class Textbox(InputComponent):
             raise ValueError("Unknown type: " + self.type + ". Please choose from: 'str', 'number'.")
 
 
-
 class Slider(InputComponent):
     """
     Component creates a slider that ranges from `minimum` to `maximum`. Provides a number as an argument to the wrapped function.
@@ -101,6 +109,7 @@ class Slider(InputComponent):
             step = 10 ** power
         self.step = step
         self.default = minimum if default is None else default
+        self.test_input = self.default
         super().__init__(label)
 
     def get_template_context(self):
@@ -126,10 +135,11 @@ class Checkbox(InputComponent):
     """
 
     def __init__(self, label=None):
-        '''
+        """
         Parameters:
         label (str): component name in interface.
-        '''
+        """
+        self.test_input = True
         super().__init__(label)
 
     @classmethod
@@ -154,6 +164,7 @@ class CheckboxGroup(InputComponent):
         '''
         self.choices = choices
         self.type = type
+        self.test_input = self.choices
         super().__init__(label)
 
     def get_template_context(self):
@@ -186,6 +197,7 @@ class Radio(InputComponent):
         '''
         self.choices = choices
         self.type = type
+        self.test_input = self.choices[0]
         super().__init__(label)
 
     def get_template_context(self):
@@ -202,6 +214,7 @@ class Radio(InputComponent):
         else:
             raise ValueError("Unknown type: " + self.type + ". Please choose from: 'value', 'index'.")
 
+
 class Dropdown(InputComponent):
     """
     Component creates a dropdown of which only one can be selected. Provides string representing selected choice as an argument to the wrapped function.
@@ -217,6 +230,7 @@ class Dropdown(InputComponent):
         '''
         self.choices = choices
         self.type = type
+        self.test_input = self.choices[0]
         super().__init__(label)
 
     def get_template_context(self):
@@ -248,7 +262,7 @@ class Image(InputComponent):
         invert_colors (bool): whether to invert the image as a preprocessing step.
         source (str): Source of image. "upload" creates a box where user can drop an image file, "webcam" allows user to take snapshot from their webcam, "canvas" defaults to a white image that can be edited and drawn upon with tools.
         tool (str): Tools used for editing. "editor" allows a full screen editor, "select" provides a cropping and zoom tool.
-        type (str): Type of value to be returned by component. "numpy" returns a numpy array with shape (width, height, 3), "pil" returns a PIL image object, "file" returns a temporary file object whose path can be retrieved by file_obj.name.
+        type (str): Type of value to be returned by component. "numpy" returns a numpy array with shape (width, height, 3) and values from 0 to 255, "pil" returns a PIL image object, "file" returns a temporary file object whose path can be retrieved by file_obj.name.
         label (str): component name in interface.
         '''
         self.shape = shape
@@ -257,6 +271,7 @@ class Image(InputComponent):
         self.tool = tool
         self.type = type
         self.invert_colors = invert_colors
+        self.test_input = test_data.BASE64_IMAGE
         super().__init__(label)
 
     @classmethod
@@ -293,6 +308,8 @@ class Image(InputComponent):
             file_obj = tempfile.NamedTemporaryFile()
             im.save(file_obj.name)
             return file_obj
+        else:
+            raise ValueError("Unknown type: " + self.type + ". Please choose from: 'numpy', 'pil', 'file'.")
 
     def rebuild(self, dir, data):
         """
@@ -312,14 +329,15 @@ class Audio(InputComponent):
     """
 
     def __init__(self, source="upload", type="numpy", label=None):
-        '''
+        """
         Parameters:
         source (str): Source of audio. "upload" creates a box where user can drop an audio file, "microphone" creates a microphone input.
         type (str): Type of value to be returned by 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 object whose path can be retrieved by file_obj.name, "mfcc" returns the mfcc coefficients of the input audio.
         label (str): component name in interface.
-        '''
+        """
         self.source = source
         self.type = type
+        self.test_input = test_data.BASE64_AUDIO
         super().__init__(label)
 
     def get_template_context(self):
@@ -361,6 +379,7 @@ class File(InputComponent):
         label (str): component name in interface.
         '''
         self.type = type
+        self.test_input = None
         super().__init__(label)
 
     @classmethod
@@ -385,7 +404,7 @@ class Dataframe(InputComponent):
     """
 
     def __init__(self, headers=None, row_count=3, col_count=3, datatype="str", type="pandas", label=None):
-        '''
+        """
         Parameters:
         headers (List[str]): Header names to dataframe.
         row_count (int): Limit number of rows for input.
@@ -393,14 +412,17 @@ class Dataframe(InputComponent):
         datatype (Union[str, List[str]]): Datatype of values in sheet. Can be provided per column as a list of strings, or for the entire sheet as a single string. Valid datatypes are "str", "number", "bool", and "date".
         type (str): Type of value to be returned by component. "pandas" for pandas dataframe, "numpy" for numpy array, or "array" for a Python array.
         label (str): component name in interface.
-        '''
+        """
         self.headers = headers
         self.datatype = datatype
         self.row_count = row_count
         self.col_count = len(headers) if headers else col_count
         self.type = type
-        super().__init__(label)
+        sample_values = {"str": "abc", "number": 786, "bool": True, "date": "02/08/1993"}
+        column_dtypes = [datatype]*self.col_count if isinstance(datatype, str) else datatype
+        self.test_input = [[sample_values[c] for c in column_dtypes] for _ in range(row_count)]
 
+        super().__init__(label)
 
     def get_template_context(self):
         return {

gradio/interface.py

@@ -193,19 +193,19 @@ class Interface:
 
         return config
 
-    def process(self, raw_input):
+    def process(self, raw_input, predict_fn=None):
         """
         :param raw_input: a list of raw inputs to process and apply the
         prediction(s) on.
+        :param predict_fn: which function to process. If not provided, all of the model functions are used.
         :return:
         processed output: a list of processed  outputs to return as the
         prediction(s).
         duration: a list of time deltas measuring inference time for each
         prediction fn.
         """
-        processed_input = [input_interface.preprocess(
+        processed_input = [input_interface.preprocess(raw_input[i])
-            raw_input[i]) for i, input_interface in
+                           for i, input_interface in enumerate(self.input_interfaces)]
-            enumerate(self.input_interfaces)]
         predictions = []
         durations = []
         for predict_fn in self.predict:
@@ -253,6 +253,22 @@ class Interface:
             thread.keep_running = False
             networking.url_ok(path_to_local_server)
 
+    def test_launch(self):
+        for predict_fn in self.predict:
+            print("Test launching: {}()...".format(predict_fn.__name__), end=' ')
+
+            raw_input = []
+            for input_interface in self.input_interfaces:
+                if input_interface.test_input is None:  # If no test input is defined for that input interface
+                    print("SKIPPED")
+                    break
+                else:  # If a test input is defined for each interface object
+                    raw_input.append(input_interface.test_input)
+            else:
+                self.process(raw_input)
+                print("PASSED")
+                continue
+
     def launch(self, inline=None, inbrowser=None, share=False, debug=False):
         """
         Parameters
@@ -326,6 +342,8 @@ class Interface:
                 if self.verbose:
                     print(strings.en["COLAB_NO_LOCAL"])
             else:  # If it's not a colab notebook and share=False, print a message telling them about the share option.
+                print("To get a public link for a hosted model, "
+                      "set Share=True")
                 if self.verbose:
                     print(strings.en["PUBLIC_SHARE_TRUE"])
                 share_url = None
@@ -345,10 +363,10 @@ class Interface:
             # with the interface.
         if inline:
             from IPython.display import IFrame, display
-            if (is_colab):
+            # Embed the remote interface page if on google colab;
-                # Embed the remote interface page if on google colab;
+            # otherwise, embed the local page.
-                # otherwise, embed the local page.
+            print("Interface loading below...")
-                print("Interface loading below...")
+            if share:
                 while not networking.url_ok(share_url):
                     time.sleep(1)
                 display(IFrame(share_url, width=1000, height=500))

gradio/processing_utils.py

@@ -32,7 +32,6 @@ def encode_plot_to_base64(plt):
     with BytesIO() as output_bytes:
         plt.savefig(output_bytes, format="png")
         bytes_data = output_bytes.getvalue()
-    plt.close()
     base64_str = str(base64.b64encode(bytes_data), 'utf-8')
     return "data:image/png;base64," + base64_str
 

setup.py

@@ -5,7 +5,7 @@ except ImportError:
 
 setup(
     name='gradio',
-    version='1.1.7',
+    version='1.1.9',
     include_package_data=True,
     description='Python library for easily interacting with trained machine learning models',
     author='Abubakar Abid',