more changes

build/lib/gradio/inputs.py

@@ -311,9 +311,10 @@ class ImageIn(AbstractInput):
         im = np.array(im).flatten()
         im = im * self.scale + self.shift
         if self.num_channels is None:
-            array = im.reshape(self.image_width, self.image_height)
+            array = im.reshape(1, self.image_width, self.image_height)
         else:
-            array = im.reshape(self.image_width, self.image_height, self.num_channels)
+            array = im.reshape(1, self.image_width, self.image_height, \
+                                self.num_channels)
         return array
 
     def rebuild_flagged(self, dir, msg):

build/lib/gradio/interface.py

@@ -16,6 +16,7 @@ import requests
 import random
 import time
 from IPython import get_ipython
+import tensorflow as tf
 
 LOCALHOST_IP = "0.0.0.0"
 TRY_NUM_PORTS = 100
@@ -29,8 +30,9 @@ class Interface:
     """
 
     def __init__(self, fn, inputs, outputs, saliency=None, verbose=False,
-                            live=False, show_input=True, show_output=True,
-                            load_fn=None, server_name=LOCALHOST_IP):
+                 live=False, show_input=True, show_output=True,
+                 load_fn=None, capture_session=False,
+                 server_name=LOCALHOST_IP):
         """
         :param fn: a function that will process the input panel data from the interface and return the output panel data.
         :param inputs: a string or `AbstractInput` representing the input interface.
@@ -42,7 +44,9 @@ class Interface:
             elif isinstance(iface, gradio.inputs.AbstractInput):
                 return iface
             else:
-                raise ValueError("Input interface must be of type `str` or `AbstractInput`")
+                raise ValueError("Input interface must be of type `str` or "
+                                 "`AbstractInput`")
+
         def get_output_instance(iface):
             if isinstance(iface, str):
                 return gradio.outputs.shortcuts[iface]
@@ -50,7 +54,8 @@ class Interface:
                 return iface
             else:
                 raise ValueError(
-                    "Output interface must be of type `str` or `AbstractOutput`"
+                    "Output interface must be of type `str` or "
+                    "`AbstractOutput`"
                 )
         if isinstance(inputs, list):
             self.input_interfaces = [get_input_instance(i) for i in inputs]
@@ -73,6 +78,8 @@ class Interface:
         self.show_input = show_input
         self.show_output = show_output
         self.flag_hash = random.getrandbits(32)
+        self.capture_session = capture_session
+        self.session = None
         self.server_name = server_name
 
     def update_config_file(self, output_directory):
@@ -155,6 +162,10 @@ class Interface:
         context = self.load_fn() if self.load_fn else None
         self.context = context
 
+        if self.capture_session:
+            self.session = tf.get_default_graph(), \
+                          tf.keras.backend.get_session()
+
         # If an existing interface is running with this instance, close it.
         if self.status == "RUNNING":
             if self.verbose:
@@ -241,7 +252,8 @@ class Interface:
 
             if (
                 is_colab
-            ):  # Embed the remote interface page if on google colab; otherwise, embed the local page.
+            ):  # Embed the remote interface page if on google colab;
+                # otherwise, embed the local page.
                 display(IFrame(share_url, width=1000, height=500))
             else:
                 display(IFrame(path_to_local_server, width=1000, height=500))

build/lib/gradio/networking.py

@@ -140,11 +140,25 @@ def serve_files_in_background(interface, port, directory_to_serve=None, server_n
                 predictions = []
                 for predict_fn in interface.predict:
                     if interface.context:
-                        prediction = predict_fn(*processed_input,
-                                                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:
-                        prediction = predict_fn(*processed_input)
-                    if len(interface.output_interfaces) / len(interface.predict) == 1:
+                        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)]

build/lib/gradio/static/js/gradio.js

@@ -19,7 +19,7 @@ function gradio(config, fn, target) {
         </div>          
       </div>
     </div>`);
-    let io_master = Object.create(io_master_template);
+    io_master = Object.create(io_master_template);
     io_master.fn = fn
     io_master.target = target;
     io_master.config = config;
@@ -68,9 +68,9 @@ function gradio(config, fn, target) {
       `);
       input_interface.target = target.find(`.input_interface[interface_id=${_id}]`);
       set_interface_id(input_interface, _id);
+      input_interface.io_master = io_master;
       input_interface.init(input_interface_data[1]);
       input_interfaces.push(input_interface);
-      input_interface.io_master = io_master;
       _id++;
     }
     for (let i = 0; i < config["output_interfaces"].length; i++) {
@@ -92,9 +92,9 @@ function gradio(config, fn, target) {
       `);
       output_interface.target = target.find(`.output_interface[interface_id=${_id}]`);
       set_interface_id(output_interface, _id);
+      output_interface.io_master = io_master;
       output_interface.init(output_interface_data[1]);
       output_interfaces.push(output_interface);
-      output_interface.io_master = io_master;
       _id++;
     }
     io_master.input_interfaces = input_interfaces;

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

@@ -6,40 +6,21 @@ const sketchpad_input = {
       <div id="brush_3" size="24" class="brush"></div>
     </div>
     <div class="view_holders">
-      <div class="saliency_holder hide">
-        <canvas class="saliency"></canvas>
-      </div>
       <div class="canvas_holder">
         <canvas class="sketch"></canvas>
       </div>
     </div>`,
-    disabled_html: `
-      <div class="view_holders">
-        <div class="saliency_holder hide">
-          <canvas class="saliency"></canvas>
-        </div>
-        <div class="canvas_holder">
-          <canvas></canvas>
-        </div>
-      </div>`,
   init: function() {
     var io = this;
     var dimension = Math.min(this.target.find(".canvas_holder").width(),
         this.target.find(".canvas_holder").height()) - 2 // dimension - border
     var id = this.id;
-    if (this.io_master.config.disabled) {
-      this.target.find('.canvas_holder canvas')
-        .attr("width", dimension).attr("height", dimension);
-    } else {
-      this.sketchpad = new Sketchpad({
-        element: '.interface[interface_id=' + id + '] .sketch',
-        width: dimension,
-        height: dimension
-      });
-      this.sketchpad.penSize = this.target.find(".brush.selected").attr("size");
-    }
-    this.target.find(".saliency")
-      .attr("width", dimension+"px").attr("height", dimension+"px");
+    this.sketchpad = new Sketchpad({
+      element: '.interface[interface_id=' + id + '] .sketch',
+      width: dimension,
+      height: dimension
+    });
+    this.sketchpad.penSize = this.target.find(".brush.selected").attr("size");
     this.canvas = this.target.find('.canvas_holder canvas')[0];
     this.context = this.canvas.getContext("2d");
     this.target.find(".brush").click(function (e) {
@@ -52,17 +33,9 @@ const sketchpad_input = {
     var dataURL = this.canvas.toDataURL("image/png");
     this.io_master.input(this.id, dataURL);
   },
-  output: function(data) {
-    this.target.find(".saliency_holder").removeClass("hide");
-    let ctx = this.target.find(".saliency")[0].getContext('2d');
-    let dimension = this.target.find(".saliency").width();
-    ctx.clearRect(0,0,dimension,dimension);
-    paintSaliency(data, dimension, dimension, ctx);
-  },
   clear: function() {
     this.context.clearRect(0, 0, this.context.canvas.width, this.context.
         canvas.height);
-    this.target.find(".saliency_holder").addClass("hide");
   },
   renderFeatured: function(data) {
     return `<img src=${data}>`;

build/lib/gradio/templates/index.html

@@ -84,8 +84,8 @@
             $.ajax({type: "POST",
               url: "/api/predict/",
               data: JSON.stringify({"data": data}),
-              success: (data) => {console.log("y"); resolve(data)},
-              error: (data) => {console.log("n"); reject()},
+              success: resolve,
+              error: reject,
             });
           });              
         }, "#interface_target");

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:
@@ -133,10 +135,12 @@ 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)]
+                processed_input = [input_interface.preprocess(
+                    raw_input[i]) for i, input_interface in enumerate(interface.input_interfaces)]
                 predictions = []
                 for predict_fn in interface.predict:
                     if interface.context:
@@ -161,7 +165,8 @@ def serve_files_in_background(interface, port, directory_to_serve=None, server_n
                             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)]
+                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)
@@ -182,34 +187,37 @@ 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))
                 os.makedirs(flag_dir, exist_ok=True)
                 output = {'inputs': [interface.input_interfaces[
                     i].rebuild_flagged(
-                                  flag_dir, msg['data']['input_data']) for i
+                    flag_dir, msg['data']['input_data']) for i
                     in range(len(interface.input_interfaces))],
-                          'outputs': [interface.output_interfaces[
-                              i].rebuild_flagged(
-                                  flag_dir, msg['data']['output_data']) for i
+                    'outputs': [interface.output_interfaces[
+                        i].rebuild_flagged(
+                        flag_dir, msg['data']['output_data']) for i
                     in range(len(interface.output_interfaces))],
-                          'message': msg['data']['message']}
+                    'message': msg['data']['message']}
 
                 with open(os.path.join(flag_dir, FLAGGING_FILENAME), 'a+') as f:
                     f.write(json.dumps(output))
                     f.write("\n")
 
-            #TODO(abidlabs): clean this up
+            # TODO(abidlabs): clean this up
             elif self.path == "/api/auto/rotation":
                 from gradio import validation_data, preprocessing_utils
                 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 +227,10 @@ 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))
-                    processed_output = interface.output_interface.postprocess(prediction)
+                    prediction = interface.predict(
+                        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 +250,11 @@ 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 +265,10 @@ 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))
-                    processed_output = interface.output_interface.postprocess(prediction)
+                    prediction = interface.predict(
+                        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 +313,8 @@ 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
 
 

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 {

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"] {

gradio/static/js/gradio.js

@@ -19,7 +19,7 @@ function gradio(config, fn, target) {
         </div>          
       </div>
     </div>`);
-    let io_master = Object.create(io_master_template);
+    io_master = Object.create(io_master_template);
     io_master.fn = fn
     io_master.target = target;
     io_master.config = config;
@@ -68,9 +68,9 @@ function gradio(config, fn, target) {
       `);
       input_interface.target = target.find(`.input_interface[interface_id=${_id}]`);
       set_interface_id(input_interface, _id);
+      input_interface.io_master = io_master;
       input_interface.init(input_interface_data[1]);
       input_interfaces.push(input_interface);
-      input_interface.io_master = io_master;
       _id++;
     }
     for (let i = 0; i < config["output_interfaces"].length; i++) {
@@ -92,9 +92,9 @@ function gradio(config, fn, target) {
       `);
       output_interface.target = target.find(`.output_interface[interface_id=${_id}]`);
       set_interface_id(output_interface, _id);
+      output_interface.io_master = io_master;
       output_interface.init(output_interface_data[1]);
       output_interfaces.push(output_interface);
-      output_interface.io_master = io_master;
       _id++;
     }
     io_master.input_interfaces = input_interfaces;

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/static/js/interfaces/input/sketchpad.js

@@ -6,40 +6,21 @@ const sketchpad_input = {
       <div id="brush_3" size="24" class="brush"></div>
     </div>
     <div class="view_holders">
-      <div class="saliency_holder hide">
-        <canvas class="saliency"></canvas>
-      </div>
       <div class="canvas_holder">
         <canvas class="sketch"></canvas>
       </div>
     </div>`,
-    disabled_html: `
-      <div class="view_holders">
-        <div class="saliency_holder hide">
-          <canvas class="saliency"></canvas>
-        </div>
-        <div class="canvas_holder">
-          <canvas></canvas>
-        </div>
-      </div>`,
   init: function() {
     var io = this;
     var dimension = Math.min(this.target.find(".canvas_holder").width(),
         this.target.find(".canvas_holder").height()) - 2 // dimension - border
     var id = this.id;
-    if (this.io_master.config.disabled) {
-      this.target.find('.canvas_holder canvas')
-        .attr("width", dimension).attr("height", dimension);
-    } else {
-      this.sketchpad = new Sketchpad({
-        element: '.interface[interface_id=' + id + '] .sketch',
-        width: dimension,
-        height: dimension
-      });
-      this.sketchpad.penSize = this.target.find(".brush.selected").attr("size");
-    }
-    this.target.find(".saliency")
-      .attr("width", dimension+"px").attr("height", dimension+"px");
+    this.sketchpad = new Sketchpad({
+      element: '.interface[interface_id=' + id + '] .sketch',
+      width: dimension,
+      height: dimension
+    });
+    this.sketchpad.penSize = this.target.find(".brush.selected").attr("size");
     this.canvas = this.target.find('.canvas_holder canvas')[0];
     this.context = this.canvas.getContext("2d");
     this.target.find(".brush").click(function (e) {
@@ -52,17 +33,9 @@ const sketchpad_input = {
     var dataURL = this.canvas.toDataURL("image/png");
     this.io_master.input(this.id, dataURL);
   },
-  output: function(data) {
-    this.target.find(".saliency_holder").removeClass("hide");
-    let ctx = this.target.find(".saliency")[0].getContext('2d');
-    let dimension = this.target.find(".saliency").width();
-    ctx.clearRect(0,0,dimension,dimension);
-    paintSaliency(data, dimension, dimension, ctx);
-  },
   clear: function() {
     this.context.clearRect(0, 0, this.context.canvas.width, this.context.
         canvas.height);
-    this.target.find(".saliency_holder").addClass("hide");
   },
   renderFeatured: function(data) {
     return `<img src=${data}>`;

gradio/templates/index.html

@@ -84,8 +84,8 @@
             $.ajax({type: "POST",
               url: "/api/predict/",
               data: JSON.stringify({"data": data}),
-              success: (data) => {console.log("y"); resolve(data)},
-              error: (data) => {console.log("n"); reject()},
+              success: resolve,
+              error: reject,
             });
           });              
         }, "#interface_target");