Passing in a root_url variable and pulling config from environment variables

indicoio/__init__.py

@@ -1,5 +1,4 @@
 from functools import partial
-import indicoio.config as config 
 
 JSON_HEADERS = {'Content-type': 'application/json', 'Accept': 'text/plain'}
 
@@ -13,14 +12,10 @@ from indicoio.images.fer import fer
 from indicoio.images.features import facial_features
 from indicoio.images.features import image_features
 
-apis = ['political', 'posneg', 'sentiment', 'language', 'fer', 
+apis = ['political', 'posneg', 'sentiment', 'language', 'fer',
         'facial_features', 'image_features', 'text_tags']
 apis = dict((api, globals().get(api)) for api in apis)
-class Namespace(object): pass
-local = Namespace()
 
 for api in apis:
-	globals()[api] = partial(apis[api], config.api_root)
-	globals()['batch_' + api] = partial(apis[api], config.api_root, batch=True)
-	setattr(local, api, partial(apis[api], config.local_api_root))
-	setattr(local, 'batch_' + api, partial(apis[api], config.local_api_root, batch=True))
+    globals()[api] = partial(apis[api])
+    globals()['batch_' + api] = partial(apis[api], batch=True)

indicoio/config.py

@@ -1,2 +1,3 @@
-local_api_root = "http://localhost:9438/"
-api_root = "http://apiv1.indico.io/"
+import os
+
+api_root = os.getenv("INDICO_PRIVATE_CLOUD_URL") or "http://apiv1.indico.io/"

indicoio/images/features.py

@@ -4,12 +4,13 @@ import requests
 import numpy as np
 
 from indicoio.utils import image_preprocess, api_handler
+import indicoio.config as config
 
-def facial_features(api_root, image, batch=False, auth=None, **kwargs):
+def facial_features(image, url_root=config.api_root, batch=False, auth=None, **kwargs):
     """
     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.
-    Input should be in a list of list format, resizing will be attempted internally but for best 
+    Input should be in a list of list format, resizing will be attempted internally but for best
     performance, images should be already sized at 48x48 pixels.
 
     Example usage:
@@ -27,18 +28,18 @@ def facial_features(api_root, image, batch=False, auth=None, **kwargs):
     :type image: list of lists
     :rtype: List containing feature responses
     """
-    return api_handler(image, api_root + "facialfeatures", batch=batch, auth=auth, **kwargs)
+    return api_handler(image, url_root + "facialfeatures", batch=batch, auth=auth, **kwargs)
 
-def image_features(api_root, image, batch=False, auth=None, **kwargs):
+def image_features(image, url_root=config.api_root, batch=False, auth=None, **kwargs):
     """
-    Given an input image, returns a 2048 dimensional sparse feature vector explaining that image. 
+    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.
 
     * Input can be either grayscale or rgb color and should either be a numpy array or nested list format.
     * Input data should be either uint8 0-255 range values or floating point between 0 and 1.
     * Large images (i.e. 1024x768+) are much bigger than needed, resizing will be done internally to 64x64 if needed.
     * For ideal performance, images should be square aspect ratio but non-square aspect ratios are supported as well.
-    
+
     Example usage:
 
     .. code-block:: python
@@ -60,4 +61,4 @@ def image_features(api_root, image, batch=False, auth=None, **kwargs):
     :rtype: List containing features
     """
     image = image_preprocess(image, batch=batch)
-    return api_handler(image, api_root + "imagefeatures", batch=batch, auth=auth, **kwargs)
+    return api_handler(image, url_root + "imagefeatures", batch=batch, auth=auth, **kwargs)

indicoio/images/fer.py

@@ -2,12 +2,14 @@ import json
 
 import requests
 import numpy as np
-from indicoio.utils import api_handler
 
-def fer(api_root, image, batch=False, auth=None, **kwargs):
+from indicoio.utils import api_handler
+import indicoio.config as config
+
+def fer(image, url_root=config.api_root, batch=False, auth=None, **kwargs):
     """
     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 
+    Input should be in a list of list format, resizing will be attempted internally but for best
     performance, images should be already sized at 48x48 pixels..
 
     Example usage:
@@ -19,13 +21,13 @@ def fer(api_root, image, batch=False, auth=None, **kwargs):
        >>> face = np.zeros((48,48)).tolist()
        >>> emotions = fer(face)
        >>> emotions
-	   {u'Angry': 0.6340586827229989, u'Sad': 0.1764309536057839, 
-	   u'Neutral': 0.05582989039191157, u'Surprise': 0.0072685938275375344, 
+	   {u'Angry': 0.6340586827229989, u'Sad': 0.1764309536057839,
+	   u'Neutral': 0.05582989039191157, u'Surprise': 0.0072685938275375344,
 	   u'Fear': 0.08523385724298838, u'Happy': 0.04117802220878012}
 
     :param image: The image to be analyzed.
     :type image: list of lists
     :rtype: Dictionary containing emotion probability pairs
     """
-    
-    return api_handler(image, api_root + "fer", batch=batch, auth=auth, **kwargs)
+
+    return api_handler(image, url_root + "fer", batch=batch, auth=auth, **kwargs)

indicoio/text/lang.py

@@ -1,8 +1,9 @@
 from indicoio.utils import api_handler
+import indicoio.config as config
 
-def language(api_root, text, batch=False, auth=None, **kwargs):
+def language(text, url_root=config.api_root, batch=False, auth=None, **kwargs):
     """
-    Given input text, returns a probability distribution over 33 possible 
+    Given input text, returns a probability distribution over 33 possible
     languages of what language the text was written in.
 
     Example usage:
@@ -22,5 +23,5 @@ def language(api_root, text, batch=False, auth=None, **kwargs):
     :type text: str or unicode
     :rtype: Dictionary of language probability pairs
     """
-    
-    return api_handler(text, api_root + "language", batch=batch, auth=auth, **kwargs)
+
+    return api_handler(text, url_root + "language", batch=batch, auth=auth, **kwargs)

indicoio/text/sentiment.py

@@ -1,7 +1,8 @@
 from indicoio import JSON_HEADERS
 from indicoio.utils import api_handler
+import indicoio.config as config
 
-def political(api_root, text, batch=False, auth=None, **kwargs):
+def political(text, url_root=config.api_root, batch=False, auth=None, **kwargs):
     """
     Given input text, returns a probability distribution over the political alignment of the speaker.
 
@@ -15,7 +16,7 @@ def political(api_root, text, batch=False, auth=None, **kwargs):
        Hopefully, driverless cars will chance economics from ownership to fee for service.'
        >>> affiliation = political(text)
        >>> affiliation
-       {u'Libertarian': 0.4923755446986322, u'Green': 0.2974443102818122, 
+       {u'Libertarian': 0.4923755446986322, u'Green': 0.2974443102818122,
        u'Liberal': 0.13730032938784784, u'Conservative': 0.07287981563170784}
        >>> least_like = affiliation.keys()[np.argmin(affiliation.values())]
        >>> most_like = affiliation.keys()[np.argmax(affiliation.values())]
@@ -27,9 +28,9 @@ def political(api_root, text, batch=False, auth=None, **kwargs):
     :rtype: Dictionary of party probability pairs
     """
 
-    return api_handler(text, api_root + "political", batch=batch, auth=auth, **kwargs)
+    return api_handler(text, url_root + "political", batch=batch, auth=auth, **kwargs)
 
-def posneg(api_root, text, batch=False, auth=None, **kwargs):
+def posneg(text, url_root=config.api_root, batch=False, auth=None, **kwargs):
     """
     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.
@@ -49,5 +50,5 @@ def posneg(api_root, text, batch=False, auth=None, **kwargs):
     :type text: str or unicode
     :rtype: Float
     """
-    
-    return api_handler(text, api_root + "sentiment", batch=batch, auth=auth, **kwargs)
+
+    return api_handler(text, url_root + "sentiment", batch=batch, auth=auth, **kwargs)

indicoio/text/tagging.py

@@ -1,6 +1,7 @@
 from indicoio.utils import api_handler
+import indicoio.config as config
 
-def text_tags(api_root, text, batch=False, auth=None, **kwargs):
+def text_tags(text, url_root=config.api_root, batch=False, auth=None, **kwargs):
     """
     Given input text, returns a probability distribution over 100 document categories
 
@@ -21,5 +22,5 @@ def text_tags(api_root, text, batch=False, auth=None, **kwargs):
     :type text: str or unicode
     :rtype: Dictionary of class probability pairs
     """
-    
-    return api_handler(text, api_root + "texttags", batch=batch, auth=auth, **kwargs)
+
+    return api_handler(text, url_root + "texttags", batch=batch, auth=auth, **kwargs)