Merge branch 'master' of https://github.com/gradio-app/gradio

build/lib/gradio/inputs.py

@@ -66,7 +66,8 @@ class AbstractInput(ABC):
 
 
 class Sketchpad(AbstractInput):
-    def __init__(self, shape=(28, 28), invert_colors=True, flatten=False, scale=1/255, shift=0,
+    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):
         self.image_width = shape[0]
         self.image_height = shape[1]
@@ -272,8 +273,9 @@ class Checkbox(AbstractInput):
 
 
 class ImageIn(AbstractInput):
-    def __init__(self, shape=(224, 224, 3), image_mode='RGB',
+    def __init__(self, cast_to=None, shape=(224, 224, 3), image_mode='RGB',
                  scale=1/127.5, shift=-1, cropper_aspect_ratio=None, label=None):
+        self.cast_to = cast_to
         self.image_width = shape[0]
         self.image_height = shape[1]
         self.num_channels = shape[2]
@@ -298,10 +300,29 @@ class ImageIn(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")

build/lib/gradio/interface.py

@@ -82,8 +82,8 @@ class Interface:
         self.session = None
         self.server_name = server_name
 
-    def update_config_file(self, output_directory):
+    def get_config_file(self):
-        config = {
+        return {
             "input_interfaces": [
                 (iface.__class__.__name__.lower(), iface.get_template_context())
                 for iface in self.input_interfaces],
@@ -95,7 +95,38 @@ class Interface:
             "show_input": self.show_input,
             "show_output": self.show_output,            
         }
-        networking.set_config(config, output_directory)
+
+    def process(self, raw_input):
+        processed_input = [input_interface.preprocess(
+            raw_input[i]) for i, input_interface in enumerate(self.input_interfaces)]
+        predictions = []
+        for predict_fn in self.predict:
+            if self.context:
+                if self.capture_session:
+                    graph, sess = self.session
+                    with graph.as_default():
+                        with sess.as_default():
+                            prediction = predict_fn(*processed_input,
+                                                    self.context)
+                else:
+                    prediction = predict_fn(*processed_input,
+                                            self.context)
+            else:
+                if self.capture_session:
+                    graph, sess = self.session
+                    with graph.as_default():
+                        with sess.as_default():
+                            prediction = predict_fn(*processed_input)
+                else:
+                    prediction = predict_fn(*processed_input)
+            if len(self.output_interfaces) / \
+                    len(self.predict) == 1:
+                prediction = [prediction]
+            predictions.extend(prediction)
+        processed_output = [output_interface.postprocess(
+            predictions[i]) for i, output_interface in enumerate(self.output_interfaces)]
+        return processed_output
+
 
     def validate(self):
         if self.validate_flag:
@@ -181,8 +212,7 @@ class Interface:
         server_port, httpd = networking.start_simple_server(self, output_directory, self.server_name)
         path_to_local_server = "http://{}:{}/".format(self.server_name, server_port)
         networking.build_template(output_directory)
-
+        networking.set_config(self.get_config_file(), output_directory)
-        self.update_config_file(output_directory)
 
         self.status = "RUNNING"
         self.simple_server = httpd

build/lib/gradio/networking.py

@@ -43,7 +43,8 @@ def build_template(temp_dir):
     :param temp_dir: string with path to temp directory in which the html file should be built
     """
     dir_util.copy_tree(STATIC_TEMPLATE_LIB, temp_dir)
-    dir_util.copy_tree(STATIC_PATH_LIB, os.path.join(temp_dir, STATIC_PATH_TEMP))
+    dir_util.copy_tree(STATIC_PATH_LIB, os.path.join(
+        temp_dir, STATIC_PATH_TEMP))
 
     # Move association file to root of temporary directory.
     copyfile(os.path.join(temp_dir, ASSOCIATION_PATH_IN_STATIC),
@@ -81,6 +82,7 @@ def render_string_or_list_with_tags(old_lines, context):
         new_lines.append(line)
     return new_lines
 
+
 def set_config(config, temp_dir):
     config_file = os.path.join(temp_dir, CONFIG_FILE)
     with open(config_file, "w") as output:
@@ -133,36 +135,11 @@ def serve_files_in_background(interface, port, directory_to_serve=None, server_n
             if self.path == "/api/predict/":
                 # Make the prediction.
                 self._set_headers()
-                data_string = self.rfile.read(int(self.headers["Content-Length"]))
+                data_string = self.rfile.read(
+                    int(self.headers["Content-Length"]))
                 msg = json.loads(data_string)
                 raw_input = msg["data"]
-                processed_input = [input_interface.preprocess(raw_input[i]) for i, input_interface in enumerate(interface.input_interfaces)]
+                output = {"action": "output", "data": interface.process(raw_input)}
-                predictions = []
-                for predict_fn in interface.predict:
-                    if interface.context:
-                        if interface.capture_session:
-                            graph, sess = interface.session
-                            with graph.as_default():
-                                with sess.as_default():
-                                    prediction = predict_fn(*processed_input,
-                                                            interface.context)
-                        else:
-                            prediction = predict_fn(*processed_input,
-                                                    interface.context)
-                    else:
-                        if interface.capture_session:
-                            graph, sess = interface.session
-                            with graph.as_default():
-                                with sess.as_default():
-                                    prediction = predict_fn(*processed_input)
-                        else:
-                            prediction = predict_fn(*processed_input)
-                    if len(interface.output_interfaces) / \
-                            len(interface.predict) == 1:
-                        prediction = [prediction]
-                    predictions.extend(prediction)
-                processed_output = [output_interface.postprocess(predictions[i]) for i, output_interface in enumerate(interface.output_interfaces)]
-                output = {"action": "output", "data": processed_output}
                 if interface.saliency is not None:
                     saliency = interface.saliency(raw_input, prediction)
                     output['saliency'] = saliency.tolist()
@@ -182,7 +159,8 @@ def serve_files_in_background(interface, port, directory_to_serve=None, server_n
 
             elif self.path == "/api/flag/":
                 self._set_headers()
-                data_string = self.rfile.read(int(self.headers["Content-Length"]))
+                data_string = self.rfile.read(
+                    int(self.headers["Content-Length"]))
                 msg = json.loads(data_string)
                 flag_dir = os.path.join(FLAGGING_DIRECTORY,
                                         str(interface.flag_hash))
@@ -207,9 +185,11 @@ def serve_files_in_background(interface, port, directory_to_serve=None, server_n
                 import numpy as np
 
                 self._set_headers()
-                data_string = self.rfile.read(int(self.headers["Content-Length"]))
+                data_string = self.rfile.read(
+                    int(self.headers["Content-Length"]))
                 msg = json.loads(data_string)
-                img_orig = preprocessing_utils.decode_base64_to_image(msg["data"])
+                img_orig = preprocessing_utils.decode_base64_to_image(
+                    msg["data"])
                 img_orig = img_orig.convert('RGB')
                 img_orig = img_orig.resize((224, 224))
 
@@ -219,8 +199,10 @@ def serve_files_in_background(interface, port, directory_to_serve=None, server_n
                 for deg in range(-180, 180+45, 45):
                     img = img_orig.rotate(deg)
                     img_array = np.array(img) / 127.5 - 1
-                    prediction = interface.predict(np.expand_dims(img_array, axis=0))
+                    prediction = interface.predict(
-                    processed_output = interface.output_interface.postprocess(prediction)
+                        np.expand_dims(img_array, axis=0))
+                    processed_output = interface.output_interface.postprocess(
+                        prediction)
                     output = {'input': interface.input_interface.save_to_file(flag_dir, img),
                               'output': interface.output_interface.rebuild_flagged(
                                   flag_dir, {'data': {'output': processed_output}}),
@@ -240,9 +222,11 @@ def serve_files_in_background(interface, port, directory_to_serve=None, server_n
                 from PIL import ImageEnhance
 
                 self._set_headers()
-                data_string = self.rfile.read(int(self.headers["Content-Length"]))
+                data_string = self.rfile.read(
+                    int(self.headers["Content-Length"]))
                 msg = json.loads(data_string)
-                img_orig = preprocessing_utils.decode_base64_to_image(msg["data"])
+                img_orig = preprocessing_utils.decode_base64_to_image(
+                    msg["data"])
                 img_orig = img_orig.convert('RGB')
                 img_orig = img_orig.resize((224, 224))
                 enhancer = ImageEnhance.Brightness(img_orig)
@@ -253,8 +237,10 @@ def serve_files_in_background(interface, port, directory_to_serve=None, server_n
                 for i in range(9):
                     img = enhancer.enhance(i/4)
                     img_array = np.array(img) / 127.5 - 1
-                    prediction = interface.predict(np.expand_dims(img_array, axis=0))
+                    prediction = interface.predict(
-                    processed_output = interface.output_interface.postprocess(prediction)
+                        np.expand_dims(img_array, axis=0))
+                    processed_output = interface.output_interface.postprocess(
+                        prediction)
                     output = {'input': interface.input_interface.save_to_file(flag_dir, img),
                               'output': interface.output_interface.rebuild_flagged(
                                   flag_dir, {'data': {'output': processed_output}}),
@@ -299,7 +285,8 @@ 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
 
 

build/lib/gradio/static/css/interfaces/input/checkbox_group.css

@@ -3,7 +3,7 @@
   align-items: center;
   font-size: 18px;
 }
-.checkbox_group input {
+.checkbox_group input, .checkbox {
   margin: 0px 4px 0px 0px;
 }
 .checkbox_group label {

build/lib/gradio/static/css/style.css

@@ -1,6 +1,5 @@
 body {
   font-family: 'Open Sans', sans-serif;
-  font-size: 12px;
   margin: 0;
 }
 button, input[type="submit"], input[type="reset"], input[type="text"], input[type="button"], select[type="submit"] {

build/lib/gradio/static/js/interfaces/input/checkbox.js

@@ -1,5 +1,5 @@
 const checkbox = {
-  html: `<input type="checkbox">`,
+  html: `<input class="checkbox" type="checkbox">`,
   init: function(opts) {
     this.target.css("height", "auto");
   },

gradio/interface.py

@@ -82,8 +82,8 @@ class Interface:
         self.session = None
         self.server_name = server_name
 
-    def update_config_file(self, output_directory):
+    def get_config_file(self):
-        config = {
+        return {
             "input_interfaces": [
                 (iface.__class__.__name__.lower(), iface.get_template_context())
                 for iface in self.input_interfaces],
@@ -95,7 +95,38 @@ class Interface:
             "show_input": self.show_input,
             "show_output": self.show_output,            
         }
-        networking.set_config(config, output_directory)
+
+    def process(self, raw_input):
+        processed_input = [input_interface.preprocess(
+            raw_input[i]) for i, input_interface in enumerate(self.input_interfaces)]
+        predictions = []
+        for predict_fn in self.predict:
+            if self.context:
+                if self.capture_session:
+                    graph, sess = self.session
+                    with graph.as_default():
+                        with sess.as_default():
+                            prediction = predict_fn(*processed_input,
+                                                    self.context)
+                else:
+                    prediction = predict_fn(*processed_input,
+                                            self.context)
+            else:
+                if self.capture_session:
+                    graph, sess = self.session
+                    with graph.as_default():
+                        with sess.as_default():
+                            prediction = predict_fn(*processed_input)
+                else:
+                    prediction = predict_fn(*processed_input)
+            if len(self.output_interfaces) / \
+                    len(self.predict) == 1:
+                prediction = [prediction]
+            predictions.extend(prediction)
+        processed_output = [output_interface.postprocess(
+            predictions[i]) for i, output_interface in enumerate(self.output_interfaces)]
+        return processed_output
+
 
     def validate(self):
         if self.validate_flag:
@@ -181,8 +212,7 @@ class Interface:
         server_port, httpd = networking.start_simple_server(self, output_directory, self.server_name)
         path_to_local_server = "http://{}:{}/".format(self.server_name, server_port)
         networking.build_template(output_directory)
-
+        networking.set_config(self.get_config_file(), output_directory)
-        self.update_config_file(output_directory)
 
         self.status = "RUNNING"
         self.simple_server = httpd

gradio/networking.py

@@ -139,35 +139,7 @@ def serve_files_in_background(interface, port, directory_to_serve=None, server_n
                     int(self.headers["Content-Length"]))
                 msg = json.loads(data_string)
                 raw_input = msg["data"]
-                processed_input = [input_interface.preprocess(
+                output = {"action": "output", "data": interface.process(raw_input)}
-                    raw_input[i]) for i, input_interface in enumerate(interface.input_interfaces)]
-                predictions = []
-                for predict_fn in interface.predict:
-                    if interface.context:
-                        if interface.capture_session:
-                            graph, sess = interface.session
-                            with graph.as_default():
-                                with sess.as_default():
-                                    prediction = predict_fn(*processed_input,
-                                                            interface.context)
-                        else:
-                            prediction = predict_fn(*processed_input,
-                                                    interface.context)
-                    else:
-                        if interface.capture_session:
-                            graph, sess = interface.session
-                            with graph.as_default():
-                                with sess.as_default():
-                                    prediction = predict_fn(*processed_input)
-                        else:
-                            prediction = predict_fn(*processed_input)
-                    if len(interface.output_interfaces) / \
-                            len(interface.predict) == 1:
-                        prediction = [prediction]
-                    predictions.extend(prediction)
-                processed_output = [output_interface.postprocess(
-                    predictions[i]) for i, output_interface in enumerate(interface.output_interfaces)]
-                output = {"action": "output", "data": processed_output}
                 if interface.saliency is not None:
                     saliency = interface.saliency(raw_input, prediction)
                     output['saliency'] = saliency.tolist()