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

.gitignore

@@ -9,3 +9,4 @@ models/*
 .models/*
 gradio_files/*
 ngrok*
+examples/ngrok*

Test Notebook.ipynb

@@ -2,9 +2,18 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2\n",
@@ -16,26 +25,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# model = tf.keras.applications.inception_v3.InceptionV3()"
+    "model = tf.keras.applications.inception_v3.InceptionV3()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
-    "inp = gradio.inputs.ImageUpload(image_width=299, image_height=299)\n",
-    "out = gradio.outputs.Label(label_names='imagenet1000', max_label_length=8, num_top_classes=8)\n",
+    "inp = gradio.inputs.ImageUpload(image_width=299, image_height=299, num_channels=3)\n",
+    "out = gradio.outputs.Label(label_names='imagenet1000', max_label_length=12, num_top_classes=5)\n",
     "\n",
     "iface = gradio.Interface(inputs=inp, \n",
     "                         outputs=out,\n",
-    "                         model=lambda x: np.array(1), \n",
-    "                         model_type='function')\n",
+    "                         model=model, \n",
+    "                         model_type='keras')\n",
     "\n",
     "# iface = gradio.Interface(inputs=inp, \n",
     "#                          outputs=out,\n",
@@ -45,7 +54,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validating samples: 6/6  [======]\n",
+      "\n",
+      "Validation passed successfully!\n"
+     ]
+    }
+   ],
+   "source": [
+    "iface.validate()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
    "metadata": {
     "scrolled": false
    },
@@ -55,7 +83,7 @@
      "output_type": "stream",
      "text": [
       "NOTE: Gradio is in beta stage, please report all bugs to: a12d@stanford.edu\n",
-      "Model is running locally at: http://localhost:7860/interface.html\n",
+      "Model is running locally at: http://localhost:7866/interface.html\n",
       "To create a public link, set `share=True` in the argument to `launch()`\n"
      ]
     },
@@ -66,14 +94,14 @@
        "        <iframe\n",
        "            width=\"1000\"\n",
        "            height=\"500\"\n",
-       "            src=\"http://localhost:7860/interface.html\"\n",
+       "            src=\"http://localhost:7866/interface.html\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
        "        ></iframe>\n",
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.IFrame at 0x26c5900fba8>"
+       "<IPython.lib.display.IFrame at 0x1b08a51f748>"
       ]
      },
      "metadata": {},

gradio/inputs.py

@@ -6,11 +6,12 @@ automatically added to a registry, which allows them to be easily referenced in
 
 from abc import ABC, abstractmethod
 import base64
-from gradio import preprocessing_utils
+from gradio import preprocessing_utils, validation_data
 from io import BytesIO
 import numpy as np
 from PIL import Image, ImageOps
 
+
 class AbstractInput(ABC):
     """
     An abstract class for defining the methods that all gradio inputs should have.
@@ -27,6 +28,9 @@ class AbstractInput(ABC):
             self.preprocess = preprocessing_fn
         super().__init__()
 
+    def get_validation_inputs(self):
+        return []
+
     @abstractmethod
     def get_template_path(self):
         """
@@ -74,6 +78,9 @@ class Webcam(AbstractInput):
         self.num_channels = num_channels
         super().__init__(preprocessing_fn=preprocessing_fn)
 
+    def get_validation_inputs(self):
+        return validation_data.BASE64_COLOR_IMAGES
+
     def get_template_path(self):
         return 'templates/input/webcam.html'
 
@@ -90,6 +97,8 @@ class Webcam(AbstractInput):
 
 
 class Textbox(AbstractInput):
+    def get_validation_inputs(self):
+        return validation_data.ENGLISH_TEXTS
 
     def get_template_path(self):
         return 'templates/input/textbox.html'
@@ -112,6 +121,9 @@ class ImageUpload(AbstractInput):
         self.shift = shift
         super().__init__(preprocessing_fn=preprocessing_fn)
 
+    def get_validation_inputs(self):
+        return validation_data.BASE64_COLOR_IMAGES
+
     def get_template_path(self):
         return 'templates/input/image_upload.html'
 

gradio/interface.py

@@ -12,6 +12,7 @@ import gradio.outputs
 from gradio import networking
 import tempfile
 import threading
+import traceback
 
 nest_asyncio.apply()
 
@@ -63,6 +64,8 @@ class Interface:
             ValueError('model_type must be one of: {}'.format(self.VALID_MODEL_TYPES))
         self.model_type = model_type
         self.verbose = verbose
+        self.launch_flag = False
+        self.validate_flag = False
 
     @staticmethod
     def _infer_model_type(model):
@@ -133,11 +136,55 @@ class Interface:
         else:
             ValueError('model_type must be one of: {}'.format(self.VALID_MODEL_TYPES))
 
-    def launch(self, inline=None, browser=None, share=False):
+    def validate(self):
+        if self.validate_flag:
+            if self.verbose:
+                print("Interface already validated")
+            return
+        validation_inputs = self.input_interface.get_validation_inputs()
+        n = len(validation_inputs)
+        if n == 0:
+            self.validate_flag = True
+            if self.verbose:
+                print("No validation samples for this interface... skipping validation.")
+            return
+        for m, msg in enumerate(validation_inputs):
+            if self.verbose:
+                print(f"Validating samples: {m+1}/{n}  [" + "="*(m+1) + "."*(n-m-1) + "]", end='\r')
+            try:
+                processed_input = self.input_interface.preprocess(msg)
+                prediction = self.predict(processed_input)
+            except Exception as e:
+                if self.verbose:
+                    print("\n----------")
+                    print("Validation failed, likely due to incompatible pre-processing and model input. See below:\n")
+                    print(traceback.format_exc())
+                break
+            try:
+                _ = self.output_interface.postprocess(prediction)
+            except Exception as e:
+                if self.verbose:
+                    print("\n----------")
+                    print("Validation failed, likely due to incompatible model output and post-processing."
+                          "See below:\n")
+                    print(traceback.format_exc())
+                break
+        else:  # This means if a break was not explicitly called
+            self.validate_flag = True
+            if self.verbose:
+                print("\n\nValidation passed successfully!")
+            return
+        raise RuntimeError("Validation did not pass")
+
+    def launch(self, inline=None, browser=None, share=False, validate=True):
         """
         Standard method shared by interfaces that creates the interface and sets up a websocket to communicate with it.
         :param share: boolean. If True, then a share link is generated using ngrok is displayed to the user.
         """
+        if validate and not self.validate_flag:
+            self.validate()
+
+        self.launch_flag = True
         output_directory = tempfile.mkdtemp()
 
         # Set up a port to serve the directory containing the static files with interface.