Merge remote-tracking branch 'origin/master' into aliabd/components-tests

README.md

@@ -10,11 +10,9 @@ Quickly create a GUI around your machine learning model, API, or function. Gradi
 
 Gradio is useful for:
 
-* Creating demos of your machine learning code for clients / collaborators / users
-
-* Getting feedback on model performance from testers
-
-* Debugging your model interactively during development
+* **Demoing** your machine learning models for clients / collaborators / users / students
+* **Deploying** your models quickly with automatic shareable links and getting feedback on model performance
+* **Debugging** your model interactively during development using built-in interpretation visualizations for any model
 
 
 ## Getting Started

gradio.egg-info/PKG-INFO

@@ -1,6 +1,6 @@
 Metadata-Version: 1.0
 Name: gradio
-Version: 2.4.1
+Version: 2.4.2
 Summary: Python library for easily interacting with trained machine learning models
 Home-page: https://github.com/gradio-app/gradio-UI
 Author: Abubakar Abid

gradio/interface.py

@@ -161,6 +161,8 @@ class Interface:
             self.css = css
         if examples is None or isinstance(examples, str) or (isinstance(examples, list) and (len(examples) == 0 or isinstance(examples[0], list))):
             self.examples = examples
+        elif isinstance(examples, list) and len(self.input_components) == 1:  # If there is only one input component, examples can be provided as a regular list instead of a list of lists 
+            self.examples = [[e] for e in examples]
         else:
             raise ValueError(
                 "Examples argument must either be a directory or a nested list, where each sublist represents a set of inputs.")

gradio/networking.py

@@ -405,14 +405,11 @@ def queue_thread(path_to_local_server, test_mode=False):
     while True:
         try:
             next_job = queue.pop()
-            print(next_job)
             if next_job is not None:
                 _, hash, input_data, task_type = next_job
-                print(hash)
                 queue.start_job(hash)
                 response = requests.post(
                     path_to_local_server + "/api/" + task_type + "/", json=input_data)
-                print('response', response)
                 if response.status_code == 200:
                     queue.pass_job(hash, response.json())
                 else:

gradio/outputs.py

@@ -134,9 +134,9 @@ class Label(OutputComponent):
                              "float label, or a dictionary whose keys are labels and values are confidences.")
 
     def deserialize(self, y):
-        # 4 cases: (1): {'label': 'lion'}, {'label': 'lion', 'confidences':...}, {'lion': 0.46, ...}, 'lion'
-        if self.type == "label" or (self.type == "auto" and (isinstance(y, str) or ('label' in y and not('confidences' in y.keys())))):
-            if isinstance(y, str):
+        # 5 cases: (1): {'label': 'lion'}, {'label': 'lion', 'confidences':...}, {'lion': 0.46, ...}, 'lion', '0.46'
+        if self.type == "label" or (self.type == "auto" and (isinstance(y, str) or isinstance(y, int) or isinstance(y, float) or ('label' in y and not('confidences' in y.keys())))):
+            if isinstance(y, str) or isinstance(y, int) or isinstance(y, float):
                 return y
             else:
                 return y['label']

gradio/templates/frontend/asset-manifest.json

@@ -1,18 +1,18 @@
 {
   "files": {
-    "main.css": "/static/css/main.ccb63765.css",
+    "main.css": "/static/css/main.fdac3282.css",
     "main.js": "/static/bundle.js",
     "index.html": "/index.html",
-    "static/media/arrow-left.794a4706.svg": "/static/media/arrow-left.794a4706.svg",
-    "static/media/arrow-right.5a7d4ada.svg": "/static/media/arrow-right.5a7d4ada.svg",
-    "static/media/clear.85cf6de8.svg": "/static/media/clear.85cf6de8.svg",
-    "static/media/edit.c6b7d6f7.svg": "/static/media/edit.c6b7d6f7.svg",
-    "static/media/logo.36a8f455.svg": "/static/media/logo.36a8f455.svg",
+    "static/media/arrow-left.e497f657.svg": "/static/media/arrow-left.e497f657.svg",
+    "static/media/arrow-right.ea6059fd.svg": "/static/media/arrow-right.ea6059fd.svg",
+    "static/media/clear.33f9b5f3.svg": "/static/media/clear.33f9b5f3.svg",
+    "static/media/edit.44bd4fe1.svg": "/static/media/edit.44bd4fe1.svg",
+    "static/media/logo.411acfd1.svg": "/static/media/logo.411acfd1.svg",
     "static/media/logo_loading.e93acd82.jpg": "/static/media/logo_loading.e93acd82.jpg"
   },
   "entrypoints": [
     "static/bundle.css",
-    "static/css/main.ccb63765.css",
+    "static/css/main.fdac3282.css",
     "static/bundle.js"
   ]
 }

gradio/templates/frontend/index.html

@@ -8,4 +8,4 @@
       window.config = {{ config|tojson }};
     } catch (e) {
       window.config = {};
-    }</script><script src="https://cdnjs.cloudflare.com/ajax/libs/iframe-resizer/4.3.1/iframeResizer.contentWindow.min.js"></script><title>Gradio</title><link href="static/bundle.css" rel="stylesheet"><link href="static/css/main.ccb63765.css" rel="stylesheet"></head><body style="height:100%"><div id="root" style="height:100%"></div><script src="static/bundle.js"></script></body></html>
+    }</script><script src="https://cdnjs.cloudflare.com/ajax/libs/iframe-resizer/4.3.1/iframeResizer.contentWindow.min.js"></script><title>Gradio</title><link href="static/bundle.css" rel="stylesheet"><link href="static/css/main.fdac3282.css" rel="stylesheet"></head><body style="height:100%"><div id="root" style="height:100%"></div><script src="static/bundle.js"></script></body></html>

gradio/version.txt

@@ -1 +1 @@
-2.4.1
+2.4.2

setup.py

@@ -5,7 +5,7 @@ except ImportError:
 
 setup(
     name='gradio',
-    version='2.4.1',
+    version='2.4.2',
     include_package_data=True,
     description='Python library for easily interacting with trained machine learning models',
     author='Abubakar Abid',

test/test_interfaces.py

@@ -1,4 +1,63 @@
+from gradio.interface import *
 import unittest
+import unittest.mock as mock
+import requests
+
+
+class TestInterface(unittest.TestCase):
+    
+    # send_error_analytics should probably actually be a method of Interface
+    # (so it doesn't have to take the 'enabled' argument)
+    # and since it's specific to the launch method, it should probably be
+    # renamed to send_launch_error_analytics.
+    # these tests test its current behavior
+    @mock.patch("requests.post")
+    def test_error_analytics_doesnt_crash_on_connection_error(self, mock_post):
+        mock_post.side_effect = requests.ConnectionError()
+        send_error_analytics(True)
+        mock_post.assert_called()
+        
+    @mock.patch("requests.post")
+    def test_error_analytics_doesnt_post_if_not_enabled(self, mock_post):
+        send_error_analytics(False)
+        mock_post.assert_not_called()
+     
+    @mock.patch("requests.post")   
+    def test_error_analytics_successful(self, mock_post):
+        send_error_analytics(True)
+        mock_post.assert_called()
+                
+    # as above, send_launch_analytics should probably be a method of Interface
+    @mock.patch("requests.post")
+    def test_launch_analytics_doesnt_crash_on_connection_error(self, mock_post):
+        mock_post.side_effect = requests.ConnectionError()
+        send_launch_analytics(analytics_enabled=True,
+                              inbrowser=True, is_colab="is_colab",
+	                              share="share", share_url="share_url")
+        mock_post.assert_called()
+        
+    @mock.patch("requests.post")
+    def test_launch_analytics_doesnt_post_if_not_enabled(self, mock_post):
+        send_launch_analytics(analytics_enabled=False,
+                              inbrowser=True, is_colab="is_colab",
+	                              share="share", share_url="share_url")
+        mock_post.assert_not_called()
+     
+    def test_reset_all(self):
+        interface = Interface(lambda input: None, "textbox", "label")
+        interface.close = mock.MagicMock()
+        reset_all()
+        interface.close.assert_called()
+        
+    def test_examples_invalid_input(self):
+        with self.assertRaises(ValueError):
+            Interface(lambda x: x, examples=1234)
+        
+    def test_examples_not_valid_path(self):
+        with self.assertRaises(FileNotFoundError):
+            interface = Interface(lambda x: x, "textbox", "label", examples='invalid-path')
+            interface.launch()
+
 
 if __name__ == '__main__':
     unittest.main()

test/test_processing_utils.py

@@ -27,10 +27,10 @@ class ImagePreprocessing(unittest.TestCase):
             "/images/test_image.png")
         self.assertEqual(output_base64, gr.test_data.BASE64_IMAGE)
 
-    def test_encode_plot_to_base64(self):
-        plt.plot([1, 2, 3, 4])
-        output_base64 = gr.processing_utils.encode_plot_to_base64(plt)
-        self.assertEqual(output_base64, gr.test_data.BASE64_PLT_IMG)
+    # def test_encode_plot_to_base64(self):  # Commented out because this is throwing errors on Windows. Possibly due to different matplotlib behavior on Windows?
+    #     plt.plot([1, 2, 3, 4])
+    #     output_base64 = gr.processing_utils.encode_plot_to_base64(plt)
+    #     self.assertEqual(output_base64, gr.test_data.BASE64_PLT_IMG)
 
     def test_encode_array_to_base64(self):
         img = Image.open("test/images/test_image.png")

test/test_utils.py

@@ -53,10 +53,12 @@ class TestUtils(unittest.TestCase):
 
         mock_post.side_effect = requests.ConnectionError()
         error_analytics("placeholder")
+        mock_post.assert_called()
      
     @mock.patch("requests.post")   
     def test_error_analytics_successful(self, mock_post):
         error_analytics("placeholder")
+        mock_post.assert_called()
         
             
     @mock.patch("IPython.get_ipython")