fixed bugs & added custom embedding fn

demo/digit_classifier.py

@@ -15,7 +15,7 @@ def recognize_digit(image):
     prediction = model.predict(image).tolist()[0]
     return {str(i): prediction[i] for i in range(10)}
 
-im = gradio.inputs.Image(shape=(28, 28), image_mode='L', invert_colors=True)
+im = gradio.inputs.Image(shape=(28, 28), image_mode='L', invert_colors=False)
 
 io = gr.Interface(
     recognize_digit, 

gradio/interface.py

@@ -123,6 +123,7 @@ class Interface:
         self.analytics_enabled=analytics_enabled
         self.save_to = None
         self.share = None
+        self.embedding_fn = embedding_fn
 
         data = {'fn': fn,
                 'inputs': inputs,
@@ -243,14 +244,12 @@ class Interface:
             predictions[i]) for i, output_interface in enumerate(self.output_interfaces)]
         return processed_output, durations
     
-    def embedding_fn(self, raw_input):
-        if self.interpretation == "default":
-            processed_input = [input_interface.preprocess(raw_input[i])
-                            for i, input_interface in enumerate(self.input_interfaces)]
+    def embed(self, processed_input):
+        if self.embedding_fn == "default":
             embedding = np.concatenate([input_interface.embed(processed_input[i])
                             for i, input_interface in enumerate(self.input_interfaces)])
         else:
-            raise NotImplementedError("Only default embedding is currently supported")
+            embedding = self.embedding_fn(processed_input)
         return embedding
 
     def interpret(self, raw_input):

gradio/networking.py

@@ -124,12 +124,14 @@ def predict():
 @app.route("/api/score_similarity/", methods=["POST"])
 def score_similarity():
     raw_input = request.json["data"]
-    input_embedding = app.interface.embedding_fn(raw_input)
+    preprocessed_input = [input_interface.preprocess(raw_input[i])
+                    for i, input_interface in enumerate(app.interface.input_interfaces)]
+    input_embedding = app.interface.embed(preprocessed_input)
     scores = list()
     for example in app.interface.examples:
-        preprocessed_example = [iface.preprocess_example(example)
+        preprocessed_example = [iface.preprocess(iface.preprocess_example(example))
             for iface, example in zip(app.interface.input_interfaces, example)]
-        example_embedding = app.interface.embedding_fn(preprocessed_example)
+        example_embedding = app.interface.embed(preprocessed_example)
         scores.append(calculate_similarity(input_embedding, example_embedding))
     return jsonify({"data": scores})
 

gradio/static/js/all_io.js

@@ -47,7 +47,7 @@ var io_master_template = {
       console.log(output.data)
       this.target.find(".loading").addClass("invisible");
       this.target.find(".output_interfaces").css("opacity", 1);
-      let html = "<th>DIFFS</th>"
+      let html = "<th>SIMILARITY</th>"
       this.target.find(".examples > table > thead > tr").append(html); 
       for (let i = 0; i < output["data"].length; i++) {
         let html = "<td>" + output["data"][i] + "</td>"

gradio/static/js/gradio.js

@@ -318,7 +318,7 @@ function gradio(config, fn, target, example_file_path) {
     }
   })
   target.find(".interpret").click(function() {
-    target.find(".interpretation_explained").removeClass("invisible");
+    // target.find(".interpretation_explained").removeClass("invisible");
     if (io_master.last_output) {
       io_master.score_similarity();
       // io_master.interpret(); // TODO(UNDO)