got the class probs to be working!

gradio/inputs.py

@@ -117,7 +117,10 @@ class ImageUpload(AbstractInput):
         image_encoded = content.split(',')[1]
         im = Image.open(BytesIO(base64.b64decode(image_encoded))).convert(self.image_mode)
         im = preprocessing_utils.resize_and_crop(im, (self.image_width, self.image_height))
-        array = np.array(im).flatten().reshape(1, self.image_width, self.image_height, self.num_channels)
+        if self.num_channels is None:
+            array = np.array(im).flatten().reshape(1, self.image_width, self.image_height)
+        else:
+            array = np.array(im).flatten().reshape(1, self.image_width, self.image_height, self.num_channels)
         return array
 
 

gradio/interface.py

@@ -102,7 +102,6 @@ class Interface:
         while True:
             try:
                 msg = await websocket.recv()
-                print('>>>>>>>>>msg', msg)
                 processed_input = self.input_interface.preprocess(msg)
                 prediction = self.predict(processed_input)
                 processed_output = self.output_interface.postprocess(prediction)

gradio/outputs.py

@@ -6,7 +6,7 @@ automatically added to a registry, which allows them to be easily referenced in
 
 from abc import ABC, abstractmethod
 import numpy as np
-
+import json
 
 class AbstractOutput(ABC):
     """
@@ -60,20 +60,21 @@ class Label(AbstractOutput):
             if prediction.size == 1:  # if it's single value
                 response[Label.LABEL_KEY] = np.asscalar(prediction)
             elif len(prediction.shape) == 1:  # if a 1D
-                response[Label.LABEL_KEY] = prediction.argmax()
+                response[Label.LABEL_KEY] = int(prediction.argmax())
                 if self.show_confidences:
                     response[Label.CONFIDENCES_KEY] = []
                     for i in range(self.num_top_classes):
                         response[Label.CONFIDENCES_KEY].append({
-                            Label.LABEL_KEY: prediction.argmax(),
-                            Label.CONFIDENCE_KEY: prediction.max(),
+                            Label.LABEL_KEY: int(prediction.argmax()),
+                            Label.CONFIDENCE_KEY: float(prediction.max()),
                         })
                         prediction[prediction.argmax()] = 0
         elif isinstance(prediction, str):
             response[Label.LABEL_KEY] = prediction
         else:
             raise ValueError("Unable to post-process model prediction.")
-        return response
+        print(response)
+        return json.dumps(response)
 
 
 class Textbox(AbstractOutput):

gradio/static/js/class-output.js

@@ -26,7 +26,7 @@ try {
   ws.onmessage = function (event) {
     sleep(300).then(() => {
 //      $(".output_class").text(event.data);
-      var data = event.data
+      var data = JSON.parse(event.data)
       $(".output_class").text(data["label"])
       $(".confidence_intervals").empty()
       if ("confidences" in data) {

test/test_outputs.py

@@ -2,6 +2,7 @@ import numpy as np
 import unittest
 import os
 from gradio import outputs
+import json
 
 PACKAGE_NAME = 'gradio'