sample-inputs for sketchpad

gradio.egg-info/requires.txt

@@ -6,3 +6,4 @@ psutil
 paramiko
 scipy
 IPython
+scikit-image

gradio/inputs.py

@@ -53,6 +53,12 @@ class AbstractInput(ABC):
         """
         return {}
 
+    def sample_inputs(self):
+        """
+        An interface can optionally implement a method that sends a list of sample inputs for inference.
+        """
+        return []
+
     @abstractmethod
     def get_name(self):
         """
@@ -77,7 +83,7 @@ class AbstractInput(ABC):
 
 class Sketchpad(AbstractInput):
     def __init__(self, preprocessing_fn=None, shape=(28, 28), invert_colors=True, flatten=False, scale=1/255, shift=0,
-                 dtype='float64'):
+                 dtype='float64', sample_inputs=None):
         self.image_width = shape[0]
         self.image_height = shape[1]
         self.invert_colors = invert_colors
@@ -85,8 +91,10 @@ class Sketchpad(AbstractInput):
         self.scale = scale
         self.shift = shift
         self.dtype = dtype
+        self.sample_inputs = sample_inputs
         super().__init__(preprocessing_fn=preprocessing_fn)
 
+
     def get_name(self):
         return 'sketchpad'
 
@@ -121,6 +129,13 @@ class Sketchpad(AbstractInput):
         im.save(f'{dir}/{filename}', 'PNG')
         return filename
 
+    def get_sample_inputs(self):
+        encoded_images = []
+        if self.sample_inputs is not None:
+            for input in self.sample_inputs:
+                encoded_images.append(preprocessing_utils.encode_array_to_base64(input))
+        return encoded_images
+
 
 class Webcam(AbstractInput):
     def __init__(self, preprocessing_fn=None, image_width=224, image_height=224, num_channels=3):

gradio/interface.py

@@ -130,6 +130,22 @@ class Interface:
 
         raise ValueError("model_type could not be inferred, please specify parameter `model_type`")
 
+
+    @staticmethod
+    def update_config_file(self, output_directory):
+        networking.set_interface_types_in_config_file(
+            output_directory,
+            self.input_interface.__class__.__name__.lower(),
+            self.output_interface.__class__.__name__.lower(),
+        )
+
+        if self.input_interface.__class__.__name__.lower() == "sketchpad" or self.input_interface.__class__.__name__.lower() == "textbox":
+            networking.set_sample_data_in_config_file(
+                output_directory,
+                self.input_interface.get_sample_inputs()
+            )
+
+
     def predict(self, preprocessed_input):
         """
         Method that calls the relevant method of the model object to make a prediction.
@@ -235,11 +251,8 @@ class Interface:
             output_directory, self.input_interface, self.output_interface
         )
 
-        networking.set_interface_types_in_config_file(
-            output_directory,
-            self.input_interface.__class__.__name__.lower(),
-            self.output_interface.__class__.__name__.lower(),
-        )
+        self.update_config_file(self, output_directory)
+
         self.status = self.STATUS_TYPES["RUNNING"]
         self.simple_server = httpd
 

gradio/networking.py

@@ -117,6 +117,16 @@ def set_share_url_in_config_file(temp_dir, share_url):
     )
 
 
+def set_sample_data_in_config_file(temp_dir, sample_inputs):
+    config_file = os.path.join(temp_dir, CONFIG_FILE)
+    render_template_with_tags(
+        config_file,
+        {
+            "sample_inputs": sample_inputs
+        },
+    )
+
+
 def get_first_available_port(initial, final):
     """
     Gets the first open port in a specified range of port numbers

gradio/preprocessing_utils.py

@@ -5,6 +5,7 @@ import tempfile
 import scipy.io.wavfile
 from scipy.fftpack import dct
 import numpy as np
+import skimage
 
 
 #########################
@@ -16,6 +17,16 @@ def decode_base64_to_image(encoding):
     return Image.open(BytesIO(base64.b64decode(image_encoded)))
 
 
+def encode_array_to_base64(image_array):
+    with BytesIO() as output_bytes:
+        PIL_image = Image.fromarray(skimage.img_as_ubyte(image_array))
+        PIL_image.save(output_bytes, 'PNG')
+        bytes_data = output_bytes.getvalue()
+
+    base64_str = str(base64.b64encode(bytes_data), 'utf-8')
+    return base64_str
+
+
 def resize_and_crop(img, size, crop_type='top'):
     """
     Resize and crop an image to fit the specified size.
@@ -24,8 +35,8 @@ def resize_and_crop(img, size, crop_type='top'):
         modified_path: path to store the modified image.
         size: `(width, height)` tuple.
         crop_type: can be 'top', 'middle' or 'bottom', depending on this
-            value, the image will cropped getting the 'top/left', 'midle' or
-            'bottom/rigth' of the image to fit the size.
+            value, the image will cropped getting the 'top/left', 'middle' or
+            'bottom/right' of the image to fit the size.
     raises:
         Exception: if can not open the file in img_path of there is problems
             to save the image.

gradio/static/config.json

@@ -1,5 +1,6 @@
 {
   "input_interface_type": "{{input_interface_type}}",
   "output_interface_type": "{{output_interface_type}}",
-  "share_url": "{{share_url}}"
+  "share_url": "{{share_url}}",
+  "sample_inputs": "{{sample_inputs}}"
 }