Batch API support

indicoio/__init__.py

@@ -13,12 +13,13 @@ from indicoio.images.fer import fer
 from indicoio.images.features import facial_features
 from indicoio.images.features import image_features
 
-political = partial(political, config.api_root)
+apis = ['political', 'posneg', 'sentiment', 'language', 'fer', 
-posneg = partial(posneg, config.api_root)
+        'facial_features', 'image_features', 'text_tags']
-sentiment = partial(sentiment, config.api_root)
+apis = dict((api, globals().get(api)) for api in apis)
-posneg = partial(sentiment, config.api_root)
+local = {}
-language = partial(language, config.api_root)
+
-fer = partial(fer, config.api_root)
+for api in apis:
-facial_features = partial(facial_features, config.api_root)
+	globals()[api] = partial(apis[api], config.api_root)
-image_features = partial(image_features, config.api_root)
+	globals()['batch_' + api] = partial(apis[api], config.api_root, batch=True)
-text_tags = partial(text_tags, config.api_root)
+	local[api] = partial(apis[api], config.local_api_root)
+	local[api] = partial(apis[api], config.local_api_root, batch=True)

indicoio/images/features.py

@@ -5,7 +5,7 @@ import numpy as np
 
 from indicoio.utils import image_preprocess, api_handler
 
-def facial_features(api_root, image):
+def facial_features(api_root, image, batch=False):
     """
     Given an grayscale input image of a face, returns a 48 dimensional feature vector explaining that face.
     Useful as a form of feature engineering for face oriented tasks.
@@ -27,9 +27,9 @@ def facial_features(api_root, image):
     :type image: list of lists
     :rtype: List containing feature responses
     """
-    return api_handler(image, api_root + "facialfeatures")
+    return api_handler(image, api_root + "facialfeatures", batch=batch)
 
-def image_features(api_root, image):
+def image_features(api_root, image, batch=False):
     """
     Given an input image, returns a 2048 dimensional sparse feature vector explaining that image. 
     Useful as a form of feature engineering for image oriented tasks.
@@ -60,4 +60,4 @@ def image_features(api_root, image):
     :rtype: List containing features
     """
     image = image_preprocess(image)
-    return api_handler(image, api_root + "imagefeatures")
+    return api_handler(image, api_root + "imagefeatures", batch=batch)

indicoio/images/fer.py

@@ -4,7 +4,7 @@ import requests
 import numpy as np
 from indicoio.utils import api_handler
 
-def fer(api_root, image):
+def fer(api_root, image, batch=False):
     """
     Given a grayscale input image of a face, returns a probability distribution over emotional state.
     Input should be in a list of list format, resizing will be attempted internally but for best 
@@ -28,4 +28,4 @@ def fer(api_root, image):
     :rtype: Dictionary containing emotion probability pairs
     """
     
-    return api_handler(image, api_root + "fer")
+    return api_handler(image, api_root + "fer", batch=batch)

indicoio/local/__init__.py

@@ -1,22 +0,0 @@
-from functools import partial
-import indicoio.config as config
-
-JSON_HEADERS = {'Content-type': 'application/json', 'Accept': 'text/plain'}
-
-from indicoio.text.sentiment import political, posneg
-from indicoio.text.sentiment import posneg as sentiment
-from indicoio.text.lang import language
-from indicoio.text.tagging import text_tags
-from indicoio.images.fer import fer
-from indicoio.images.features import facial_features
-from indicoio.images.features import image_features
-
-political = partial(political, config.local_api_root)
-posneg = partial(posneg, config.local_api_root)
-sentiment = partial(sentiment, config.local_api_root)
-posneg = partial(sentiment, config.local_api_root)
-language = partial(language, config.local_api_root)
-fer = partial(fer, config.local_api_root)
-facial_features = partial(facial_features, config.local_api_root)
-image_features = partial(image_features, config.local_api_root)
-text_tags = partial(text_tags, config.local_api_root)

indicoio/text/lang.py

@@ -1,6 +1,6 @@
 from indicoio.utils import api_handler
 
-def language(api_root, text):
+def language(api_root, text, batch=False):
     """
     Given input text, returns a probability distribution over 33 possible 
     languages of what language the text was written in.
@@ -23,4 +23,4 @@ def language(api_root, text):
     :rtype: Dictionary of language probability pairs
     """
     
-    return api_handler(text, api_root + "language")
+    return api_handler(text, api_root + "language", batch=batch)

indicoio/text/sentiment.py

@@ -1,7 +1,7 @@
 from indicoio import JSON_HEADERS
 from indicoio.utils import api_handler
 
-def political(api_root, text):
+def political(api_root, text, batch=False):
     """
     Given input text, returns a probability distribution over the political alignment of the speaker.
 
@@ -27,9 +27,9 @@ def political(api_root, text):
     :rtype: Dictionary of party probability pairs
     """
 
-    return api_handler(text, api_root + "political")
+    return api_handler(text, api_root + "political", batch=batch)
 
-def posneg(api_root, text):
+def posneg(api_root, text, batch=False):
     """
     Given input text, returns a scalar estimate of the sentiment of that text.
     Values are roughly in the range 0 to 1 with 0.5 indicating neutral sentiment.
@@ -50,4 +50,4 @@ def posneg(api_root, text):
     :rtype: Float
     """
     
-    return api_handler(text, api_root + "sentiment")
+    return api_handler(text, api_root + "sentiment", batch=batch)

indicoio/text/tagging.py

@@ -1,6 +1,6 @@
 from indicoio.utils import api_handler
 
-def text_tags(api_root, text):
+def text_tags(api_root, text, batch=False):
     """
     Given input text, returns a probability distribution over 100 document categories
 
@@ -22,4 +22,4 @@ def text_tags(api_root, text):
     :rtype: Dictionary of class probability pairs
     """
     
-    return api_handler(text, api_root + "texttags")
+    return api_handler(text, api_root + "texttags", batch=batch)

indicoio/utils/__init__.py

@@ -4,8 +4,10 @@ from skimage.transform import resize
 
 from indicoio import JSON_HEADERS
 
-def api_handler(arg, url):
+def api_handler(arg, url, batch=False):
     data_dict = json.dumps({'data': arg})
+    if batch:
+        url += "/batch"
     response = requests.post(url, data=data_dict, headers=JSON_HEADERS).json()
     results = response.get('results', False)
     if not results:

setup.py

@@ -14,7 +14,6 @@ setup(
         "indicoio.text",
         "indicoio.images",
         "indicoio.utils",
-        "indicoio.local",
         "tests",
     ],
     description="""