Merge pull request #60 from IndicoDataSolutions/development

Development

CHANGES.txt

@@ -22,4 +22,5 @@ v0.5.0, Friday Feb 27 -- Updated to support private cloud, allows for indicorc f
 v0.5.1, Friday Feb 27 -- More README updates, fixed rst formatting issue, added classifiers
 v0.5.2, Tue March 7 -- Required API keys, configuration settings
 v0.5.3, Wed Apr 15 -- Added scipy to requirements, edited Readme to not break pypi page
-v0.6.0, Thu May 29 -- Remove numpy / scipy dependency in favor of Pillow
+v0.6.0, Thu May 29 -- Remove numpy / scipy dependency in favor of Pillow
+v0.7.0, Tue Jun 9 -- Added support for calling multiple APIs in a single function and accepting filenames as image API inputs

README.md

@@ -15,7 +15,7 @@ pip install indicoio
 
 From source:
 ```bash
-git clone https://github.com/IndicoDataSolutions/IndicoIo-python.git 
+git clone https://github.com/IndicoDataSolutions/IndicoIo-python.git
 python setup.py install
 ```
 
@@ -62,7 +62,7 @@ Examples
 >>> text_tags(test_text, top_n=1) # return only keys with top_n values
 {u'startups_and_entrepreneurship': 0.21888586688354486}
 
->>> import numpy as np 
+>>> import numpy as np
 
 >>> test_face = np.linspace(0,50,48*48).reshape(48,48)
 
@@ -85,3 +85,32 @@ Each `indicoio` function has a corresponding batch function for analyzing many e
 >>> batch_sentiment(['Best day ever', 'Worst day ever'])
 [0.9899001220871786, 0.005709885173415242]
 ```
+
+
+Calling multiple APIs with a single function
+---------
+There are two multiple API functions `predict_text` and `predict_image`. These functions are similar to the existing api functions, but take in an additional `apis` argument as a list of strings of API names (defaults to all existing apis). `predict_text` accepts a list of existing text APIs and vice versa for `predict_image`. These functions also support batch as the other functions do.
+
+Accepted text API names: `text_tags, political, sentiment, language`
+
+Accepted image API names: `fer, facial_features, image_features`
+
+```python
+>>> from indicoio import predict_text, predict_image, batch_predict_text, batch_predict_image
+
+>>> predict_text('Best day ever', apis=["sentiment", "language"])
+{'sentiment': 0.9899001220871786, 'language': {u'Swedish': 0.0022464881013042294, u'Vietnamese': 9.887170914498351e-05, ...}}
+
+>>> batch_predict_text(['Best day ever', 'Worst day ever'], apis=["sentiment", "language"])
+{'sentiment': [0.9899001220871786, 0.005709885173415242], 'language': [{u'Swedish': 0.0022464881013042294, u'Vietnamese': 9.887170914498351e-05, u'Romanian': 0.00010661175919993216, ...}, {u'Swedish': 0.4924352805804646, u'Vietnamese': 0.028574824174911372, u'Romanian': 0.004185623723173551, u'Dutch': 0.000717033819689362, u'Korean': 0.0030093489153785826, ...}]}
+
+>>> import numpy as np
+
+>>> test_face = np.linspace(0,50,48*48).reshape(48,48).tolist()
+
+>>> predict_image(test_face, apis=["fer", "facial_features"])
+{'facial_features': [0.0, -0.026176479280200796, 0.20707644777495776, ...], 'fer': {u'Angry': 0.08877494466353497, u'Sad': 0.3933999409104264, u'Neutral': 0.1910612654566151, u'Surprise': 0.0346146405941845, u'Fear': 0.17682159820518667, u'Happy': 0.11532761017005204}}
+
+>>> batch_predict_image([test_face, test_face], apis=["fer", "facial_features"])
+{'facial_features': [[0.0, -0.026176479280200796, 0.20707644777495776, ...], [0.0, -0.026176479280200796, 0.20707644777495776, ...]], 'fer': [{u'Angry': 0.08877494466353497, u'Sad': 0.3933999409104264, u'Neutral': 0.1910612654566151, u'Surprise': 0.0346146405941845, u'Fear': 0.17682159820518667, u'Happy': 0.11532761017005204}, { u'Angry': 0.08877494466353497, u'Sad': 0.3933999409104264, u'Neutral': 0.1910612654566151, u'Surprise': 0.0346146405941845, u'Fear': 0.17682159820518667, u'Happy': 0.11532761017005204}]}
+```

README.rst

@@ -18,7 +18,7 @@ From source:
 
 .. code:: bash
 
-    git clone https://github.com/IndicoDataSolutions/IndicoIo-python.git 
+    git clone https://github.com/IndicoDataSolutions/IndicoIo-python.git
     python setup.py install
 
 API Keys + Setup
@@ -54,8 +54,7 @@ Examples
     >>> indicoio.config.api_key = "YOUR_API_KEY"
 
     >>> political("Guns don't kill people. People kill people.")
-    {u'Libertarian': 0.47740164630834825, u'Green': 0.08454409540443657, 
+    {u'Libertarian': 0.47740164630834825, u'Green': 0.08454409540443657, u'Liberal': 0.16617097211030055, u'Conservative': 0.2718832861769146}
-        u'Liberal': 0.16617097211030055, u'Conservative': 0.2718832861769146}
 
     >>> sentiment('Worst movie ever.')
     0.07062467665597527
@@ -71,23 +70,21 @@ Examples
     >>> text_tags(test_text, top_n=1) # return only keys with top_n values
     {u'startups_and_entrepreneurship': 0.21888586688354486}
 
-    >>> import numpy as np 
+    >>> import numpy as np
 
     >>> test_face = np.linspace(0,50,48*48).reshape(48,48).tolist()
 
     >>> fer(test_face)
-    {u'Angry': 0.08843749137458341, u'Sad': 0.39091163159204684, u'Neutral': 0.1947947999669361,
+    {u'Angry': 0.08843749137458341, u'Sad': 0.39091163159204684, u'Neutral': 0.1947947999669361, u'Surprise': 0.03443785859010413, u'Fear': 0.17574534848440568, u'Happy': 0.11567286999192382}
-         u'Surprise': 0.03443785859010413, u'Fear': 0.17574534848440568, u'Happy': 0.11567286999192382}
 
     >>> facial_features(test_face)
     [0.0, -0.02568680526917187, 0.21645604230056517, ..., 3.0342637531932777]
 
     >>> language('Quis custodiet ipsos custodes')
-    {u'Swedish': 0.00033330636691921914, u'Lithuanian': 0.007328693814717631, 
+    {u'Swedish': 0.00033330636691921914, u'Lithuanian': 0.007328693814717631, u'Vietnamese': 0.0002686116137658802, u'Romanian': 8.133913804076592e-06, ...}
-        u'Vietnamese': 0.0002686116137658802, u'Romanian': 8.133913804076592e-06, ...}
 
-Batch API Access
+Batch API
-----------------
+---------
 
 Each ``indicoio`` function has a corresponding batch function for
 analyzing many examples with a single request. Simply pass in a list of
@@ -100,3 +97,37 @@ inputs and receive a list of results in return.
     >>> batch_sentiment(['Best day ever', 'Worst day ever'])
     [0.9899001220871786, 0.005709885173415242]
 
+Calling multiple APIs with a single function
+--------------------------------------------
+
+There are two multiple API functions ``predict_text`` and
+``predict_image``. These functions are similar to the existing api
+functions, but take in an additional ``apis`` argument as a list of
+strings of API names (defaults to all existing apis). ``predict_text``
+accepts a list of existing text APIs and vice versa for
+``predict_image``. These functions also support batch as the other
+functions do.
+
+Accepted text API names: ``text_tags, political, sentiment, language``
+
+Accepted image API names: ``fer, facial_features, image_features``
+
+.. code:: python
+
+    >>> from indicoio import predict_text, predict_image, batch_predict_text, batch_predict_image
+
+    >>> predict_text('Best day ever', apis=["sentiment", "language"])
+    {'sentiment': 0.9899001220871786, 'language': {u'Swedish': 0.0022464881013042294, u'Vietnamese': 9.887170914498351e-05, ...}}
+
+    >>> batch_predict_text(['Best day ever', 'Worst day ever'], apis=["sentiment", "language"])
+    {'sentiment': [0.9899001220871786, 0.005709885173415242], 'language': [{u'Swedish': 0.0022464881013042294, u'Vietnamese': 9.887170914498351e-05, u'Romanian': 0.00010661175919993216, ...}, {u'Swedish': 0.4924352805804646, u'Vietnamese': 0.028574824174911372, u'Romanian': 0.004185623723173551, u'Dutch': 0.000717033819689362, u'Korean': 0.0030093489153785826, ...}]}
+
+    >>> import numpy as np
+
+    >>> test_face = np.linspace(0,50,48*48).reshape(48,48).tolist()
+
+    >>> predict_image(test_face, apis=["fer", "facial_features"])
+    {'facial_features': [0.0, -0.026176479280200796, 0.20707644777495776, ...], 'fer': {u'Angry': 0.08877494466353497, u'Sad': 0.3933999409104264, u'Neutral': 0.1910612654566151, u'Surprise': 0.0346146405941845, u'Fear': 0.17682159820518667, u'Happy': 0.11532761017005204}}
+
+    >>> batch_predict_image([test_face, test_face], apis=["fer", "facial_features"])
+    {'facial_features': [[0.0, -0.026176479280200796, 0.20707644777495776, ...], [0.0, -0.026176479280200796, 0.20707644777495776, ...]], 'fer': [{u'Angry': 0.08877494466353497, u'Sad': 0.3933999409104264, u'Neutral': 0.1910612654566151, u'Surprise': 0.0346146405941845, u'Fear': 0.17682159820518667, u'Happy': 0.11532761017005204}, { u'Angry': 0.08877494466353497, u'Sad': 0.3933999409104264, u'Neutral': 0.1910612654566151, u'Surprise': 0.0346146405941845, u'Fear': 0.17682159820518667, u'Happy': 0.11532761017005204}]}

indicoio/__init__.py

@@ -1,14 +1,14 @@
 from functools import partial
 
+Version, version, __version__, VERSION = ('0.7.0',) * 4
+
 JSON_HEADERS = {
     'Content-type': 'application/json',
     'Accept': 'application/json',
     'client-lib': 'python',
-    'version-number': '0.6.0'
+    'version-number': VERSION
 }
 
-Version, version, __version__, VERSION = ('0.6.0',) * 4
-
 from indicoio.text.sentiment import political, posneg
 from indicoio.text.sentiment import posneg as sentiment
 from indicoio.text.lang import language
@@ -16,18 +16,11 @@ 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
+from indicoio.utils.multi import predict_image, predict_text
 
-apis = [
+from indicoio.config import API_NAMES
-    'political',
+
-    'posneg',
+apis = dict((api, globals().get(api)) for api in API_NAMES)
-    'sentiment',
-    'language',
-    'fer',
-    'facial_features',
-    'image_features',
-    'text_tags'
-]
-apis = dict((api, globals().get(api)) for api in apis)
 
 for api in apis:
     globals()[api] = partial(apis[api])

indicoio/config.py

@@ -45,11 +45,26 @@ class Settings(ConfigParser.ConfigParser):
             None
         )
 
+TEXT_APIS = [
+    'text_tags',
+    'political',
+    'sentiment',
+    'language'
+]
+
+IMAGE_APIS = [
+    'fer',
+    'facial_features',
+    'image_features'
+]
+
+API_NAMES = IMAGE_APIS + TEXT_APIS + ["predict_text", "predict_image"]
 
 SETTINGS = Settings(files=[
     os.path.expanduser("~/.indicorc"),
     os.path.join(os.getcwd(), '.indicorc')
 ])
+
 api_key = SETTINGS.api_key()
 cloud = SETTINGS.cloud()
 PUBLIC_API_HOST = 'apiv2.indico.io'

indicoio/images/features.py

@@ -1,6 +1,7 @@
 import requests
 
-from indicoio.utils import image_preprocess, api_handler
+from indicoio.utils.image import image_preprocess
+from indicoio.utils.api import api_handler
 
 def facial_features(image, cloud=None, batch=False, api_key=None, **kwargs):
     """
@@ -25,7 +26,7 @@ def facial_features(image, cloud=None, batch=False, api_key=None, **kwargs):
     :rtype: List containing feature responses
     """
     image = image_preprocess(image, batch=batch)
-    return api_handler(image, cloud=cloud, api="facialfeatures", batch=batch, api_key=api_key, **kwargs)
+    return api_handler(image, cloud=cloud, api="facialfeatures", url_params={"batch":batch, "api_key":api_key}, **kwargs)
 
 def image_features(image, cloud=None, batch=False, api_key=None, **kwargs):
     """
@@ -58,4 +59,4 @@ def image_features(image, cloud=None, batch=False, api_key=None, **kwargs):
     :rtype: List containing features
     """
     image = image_preprocess(image, batch=batch, size=(64,64))
-    return api_handler(image, cloud=cloud, api="imagefeatures", batch=batch, api_key=api_key, **kwargs)
+    return api_handler(image, cloud=cloud, api="imagefeatures", url_params={"batch":batch, "api_key":api_key}, **kwargs)

indicoio/images/fer.py

@@ -1,6 +1,7 @@
 import requests
 
-from indicoio.utils import api_handler, image_preprocess
+from indicoio.utils.api import api_handler
+from indicoio.utils.image import image_preprocess
 import indicoio.config as config
 
 def fer(image, cloud=None, batch=False, api_key=None, **kwargs):
@@ -27,4 +28,4 @@ def fer(image, cloud=None, batch=False, api_key=None, **kwargs):
     :rtype: Dictionary containing emotion probability pairs
     """
     image = image_preprocess(image, batch=batch)
-    return api_handler(image, cloud=cloud, api="fer", batch=batch, api_key=api_key, **kwargs)
+    return api_handler(image, cloud=cloud, api="fer", url_params={"batch":batch, "api_key":api_key}, **kwargs)

indicoio/text/lang.py

@@ -1,4 +1,4 @@
-from indicoio.utils import api_handler
+from indicoio.utils.api import api_handler
 import indicoio.config as config
 
 def language(text, cloud=None, batch=False, api_key=None, **kwargs):
@@ -24,4 +24,4 @@ def language(text, cloud=None, batch=False, api_key=None, **kwargs):
     :rtype: Dictionary of language probability pairs
     """
 
-    return api_handler(text, cloud=cloud, api="language", batch=batch, api_key=api_key, **kwargs)
+    return api_handler(text, cloud=cloud, api="language", url_params={"batch":batch, "api_key":api_key}, **kwargs)

indicoio/text/sentiment.py

@@ -1,4 +1,4 @@
-from indicoio.utils import api_handler
+from indicoio.utils.api import api_handler
 
 def political(text, cloud=None, batch=False, api_key=None, **kwargs):
     """
@@ -26,7 +26,7 @@ def political(text, cloud=None, batch=False, api_key=None, **kwargs):
     :rtype: Dictionary of party probability pairs
     """
 
-    return api_handler(text, cloud=cloud, api="political", batch=batch, api_key=api_key, **kwargs)
+    return api_handler(text, cloud=cloud, api="political", url_params={"batch":batch, "api_key":api_key}, **kwargs)
 
 def posneg(text, cloud=None, batch=False, api_key=None, **kwargs):
     """
@@ -49,4 +49,4 @@ def posneg(text, cloud=None, batch=False, api_key=None, **kwargs):
     :rtype: Float
     """
 
-    return api_handler(text, cloud=cloud, api="sentiment", batch=batch, api_key=api_key, **kwargs)
+    return api_handler(text, cloud=cloud, api="sentiment", url_params={"batch":batch, "api_key":api_key}, **kwargs)

indicoio/text/tagging.py

@@ -1,4 +1,4 @@
-from indicoio.utils import api_handler
+from indicoio.utils.api import api_handler
 import indicoio.config as config
 
 def text_tags(text, cloud=None, batch=False, api_key=None, **kwargs):
@@ -23,4 +23,4 @@ def text_tags(text, cloud=None, batch=False, api_key=None, **kwargs):
     :rtype: Dictionary of class probability pairs
     """
 
-    return api_handler(text, cloud=cloud, api="texttags", batch=batch, api_key=api_key, **kwargs)
+    return api_handler(text, cloud=cloud, api="texttags", url_params={"batch":batch, "api_key":api_key}, **kwargs)

indicoio/utils/__init__.py

@@ -1,45 +1,9 @@
-import inspect, json, getpass, os.path, base64, StringIO, re, warnings
+"""
-import requests
+Basic utility classes and functions
-from PIL import Image
+"""
-
+import inspect
-from indicoio import JSON_HEADERS
-from indicoio import config
-
-B64_PATTERN = re.compile("^([A-Za-z0-9+/]{4})*([A-Za-z0-9+/]{4}|[A-Za-z0-9+/]{3}=|[A-Za-z0-9+/]{2}==)")
-
-
-def api_handler(arg, cloud, api, batch=False, api_key=None, **kwargs):
-    data = {'data': arg}
-    data.update(**kwargs)
-    json_data = json.dumps(data)
-    if not cloud:
-        cloud=config.cloud
-
-    if cloud:
-        host = "%s.indico.domains" % cloud
-    else:
-        # default to indico public cloud
-        host = config.PUBLIC_API_HOST
-
-    if not api_key:
-        api_key = config.api_key
-
-    url = config.url_protocol + "//%s/%s" % (host, api)
-    url = url + "/batch" if batch else url
-    url += "?key=%s" % api_key
-
-
-    response = requests.post(url, data=json_data, headers=JSON_HEADERS)
-    if response.status_code == 503 and cloud != None:
-        raise Exception("Private cloud '%s' does not include api '%s'" % (cloud, api))
-
-    json_results = response.json()
-    results = json_results.get('results', False)
-    if results is False:
-        error = json_results.get('error')
-        raise ValueError(error)
-    return results
 
+from indicoio.utils.errors import DataStructureException
 
 class TypeCheck(object):
     """
@@ -68,118 +32,6 @@ class TypeCheck(object):
         return check_args
 
 
-class DataStructureException(Exception):
-    """
-    If a non-accepted datastructure is passed, throws an exception
-    """
-    def __init__(self, callback, passed_structure, accepted_structures):
-        self.callback = callback.__name__
-        self.structure = str(type(passed_structure))
-        self.accepted = [str(structure) for structure in accepted_structures]
-
-    def __str__(self):
-        return """
-        function %s does not accept %s, accepted types are: %s
-        """ % (self.callback, self.structure, str(self.accepted))
-
-
-def image_preprocess(image, size=(48,48), batch=False):
-    """
-    Takes an image and prepares it for sending to the api including
-    resizing and image data/structure standardizing.
-    """
-    if batch:
-        return [image_preprocess(img, batch=False) for img in image]
-
-    if isinstance(image, basestring):
-        b64_str = re.sub('^data:image/.+;base64,', '', image)
-        if os.path.isfile(image):
-            # check type of element
-            outImage = Image.open(image)
-        elif B64_PATTERN.match(b64_str) is not None:
-            return b64_str
-        else:
-            raise ValueError("Snose tring provided must be a valid filepath or base64 encoded string")
-
-    elif isinstance(image, list): # image passed in is a list and not np.array
-        warnings.warn(
-            "Input as lists of pixels will be deprecated in the next major update",
-            DeprecationWarning
-        )
-        outImage = process_list_image(image)
-    elif type(image).__name__ == "ndarray": # image is from numpy/scipy
-        out_image = Image.fromarray(image)
-    else:
-        raise ValueError("Image must be a filepath, base64 encoded string, or a numpy array")
-
-    # image resizing
-    outImage = outImage.resize(size)
-
-    # convert to base64
-    temp_output = StringIO.StringIO()
-    outImage.save(temp_output, format='PNG')
-    temp_output.seek(0)
-    output_s = temp_output.read()
-
-    return base64.b64encode(output_s)
-
-
-def get_list_dimensions(_list):
-    """
-    Takes a nested list and returns the size of each dimension followed
-    by the element type in the list
-    """
-    if isinstance(_list, list) or isinstance(_list, tuple):
-        return [len(_list)] + get_list_dimensions(_list[0])
-    return []
-
-
-def get_element_type(_list, dimens):
-    """
-    Given the dimensions of a nested list and the list, returns the type of the
-    elements in the inner list.
-    """
-    elem = _list
-    for _ in xrange(len(dimens)):
-        elem = elem[0]
-    return type(elem)
-
-
-def process_list_image(_list):
-    """
-    Processes list to be [[(int, int, int), ...]]
-    """
-    # Check if list is empty
-    if not _list:
-        return _list
-
-    dimens = get_list_dimensions(_list)
-    data_type = get_element_type(_list, dimens)
-
-    seq_obj = []
-
-    outImage = Image.new("RGB", (dimens[0], dimens[1]))
-    for i in xrange(dimens[0]):
-        for j in xrange(dimens[1]):
-            elem = _list[i][j]
-            if len(dimens) >= 3:
-                #RGB(A)
-                if data_type == float:
-                    seq_obj.append((int(elem[0] * 255), int(elem[1] * 255), int(elem[2] * 255)))
-                else:
-                    seq_obj.append(elem[0:3])
-            elif data_type == float:
-                #Grayscale 0 - 1.0f
-                seq_obj.append((int(elem * 255), ) * 3)
-            else:
-                #Grayscale 0 - 255
-                seq_obj.append((elem, ) * 3)
-
-    #Needs to be 0 - 255 in flattened list of (R, G, B)
-    outImage.putdata(data = seq_obj)
-
-    return outImage
-
 
 def is_url(data, batch=False):
     if batch and isinstance(data[0], basestring):

indicoio/utils/api.py

@@ -0,0 +1,39 @@
+"""
+Handles making requests to the IndicoApi Server
+"""
+
+import json, requests
+
+from indicoio.utils.errors import IndicoError, DataStructureException
+from indicoio import JSON_HEADERS
+from indicoio import config
+
+def api_handler(arg, cloud, api, url_params = {"batch":False, "api_key":None}, **kwargs):
+    data = {'data': arg}
+    data.update(**kwargs)
+    json_data = json.dumps(data)
+    if not cloud:
+        cloud=config.cloud
+
+    if cloud:
+        host = "%s.indico.domains" % cloud
+    else:
+        # default to indico public cloud
+        host = config.PUBLIC_API_HOST
+
+    url = config.url_protocol + "//%s/%s" % (host, api)
+    url = url + "/batch" if url_params.get("batch", False) else url
+    url += "?key=%s" % (url_params.get("api_key", None) or config.api_key)
+    if "apis" in url_params:
+        url += "&apis=%s" % ",".join(url_params["apis"])
+
+    response = requests.post(url, data=json_data, headers=JSON_HEADERS)
+    if response.status_code == 503 and cloud != None:
+        raise IndicoError("Private cloud '%s' does not include api '%s'" % (cloud, api))
+
+    json_results = response.json()
+    results = json_results.get('results', False)
+    if results is False:
+        error = json_results.get('error')
+        raise IndicoError(error)
+    return results

indicoio/utils/errors.py

@@ -0,0 +1,20 @@
+"""
+Contains Indico Custom Errors
+"""
+
+class IndicoError(ValueError):
+    pass
+
+class DataStructureException(Exception):
+    """
+    If a non-accepted datastructure is passed, throws an exception
+    """
+    def __init__(self, callback, passed_structure, accepted_structures):
+        self.callback = callback.__name__
+        self.structure = str(type(passed_structure))
+        self.accepted = [str(structure) for structure in accepted_structures]
+
+    def __str__(self):
+        return """
+        function %s does not accept %s, accepted types are: %s
+        """ % (self.callback, self.structure, str(self.accepted))

indicoio/utils/image.py

@@ -0,0 +1,110 @@
+"""
+Image Utils
+Handles preprocessing images before they are sent to the server
+"""
+import os.path, base64, StringIO, re, warnings
+
+from PIL import Image
+
+from indicoio.utils.errors import IndicoError, DataStructureException
+
+B64_PATTERN = re.compile("^([A-Za-z0-9+/]{4})*([A-Za-z0-9+/]{4}|[A-Za-z0-9+/]{3}=|[A-Za-z0-9+/]{2}==)")
+
+def image_preprocess(image, size=(48,48), batch=False):
+    """
+    Takes an image and prepares it for sending to the api including
+    resizing and image data/structure standardizing.
+    """
+    if batch:
+        return [image_preprocess(img, batch=False) for img in image]
+
+    if isinstance(image, basestring):
+        b64_str = re.sub('^data:image/.+;base64,', '', image)
+        if os.path.isfile(image):
+            # check type of element
+            outImage = Image.open(image)
+        elif B64_PATTERN.match(b64_str) is not None:
+            return b64_str
+        else:
+            raise IndicoError("Snose tring provided must be a valid filepath or base64 encoded string")
+
+    elif isinstance(image, list): # image passed in is a list and not np.array
+        warnings.warn(
+            "Input as lists of pixels will be deprecated in the next major update",
+            DeprecationWarning
+        )
+        outImage = process_list_image(image)
+    elif isinstance(image, Image.Image):
+        outImage = image
+    elif type(image).__name__ == "ndarray": # image is from numpy/scipy
+        out_image = Image.fromarray(image)
+    else:
+        raise IndicoError("Image must be a filepath, base64 encoded string, or a numpy array")
+
+    # image resizing
+    outImage = outImage.resize(size)
+
+    # convert to base64
+    temp_output = StringIO.StringIO()
+    outImage.save(temp_output, format='PNG')
+    temp_output.seek(0)
+    output_s = temp_output.read()
+
+    return base64.b64encode(output_s)
+
+
+def get_list_dimensions(_list):
+    """
+    Takes a nested list and returns the size of each dimension followed
+    by the element type in the list
+    """
+    if isinstance(_list, list) or isinstance(_list, tuple):
+        return [len(_list)] + get_list_dimensions(_list[0])
+    return []
+
+
+def get_element_type(_list, dimens):
+    """
+    Given the dimensions of a nested list and the list, returns the type of the
+    elements in the inner list.
+    """
+    elem = _list
+    for _ in xrange(len(dimens)):
+        elem = elem[0]
+    return type(elem)
+
+
+def process_list_image(_list):
+    """
+    Processes list to be [[(int, int, int), ...]]
+    """
+    # Check if list is empty
+    if not _list:
+        return _list
+
+    dimens = get_list_dimensions(_list)
+    data_type = get_element_type(_list, dimens)
+
+    seq_obj = []
+
+    outImage = Image.new("RGB", (dimens[0], dimens[1]))
+    for i in xrange(dimens[0]):
+        for j in xrange(dimens[1]):
+            elem = _list[i][j]
+            if len(dimens) >= 3:
+                #RGB(A)
+                if data_type == float:
+                    seq_obj.append((int(elem[0] * 255), int(elem[1] * 255), int(elem[2] * 255)))
+                else:
+                    seq_obj.append(elem[0:3])
+            elif data_type == float:
+                #Grayscale 0 - 1.0f
+                seq_obj.append((int(elem * 255), ) * 3)
+            else:
+                #Grayscale 0 - 255
+                seq_obj.append((elem, ) * 3)
+
+    #Needs to be 0 - 255 in flattened list of (R, G, B)
+    outImage.putdata(data = seq_obj)
+
+    return outImage

indicoio/utils/multi.py

@@ -0,0 +1,116 @@
+from indicoio.config import TEXT_APIS, IMAGE_APIS, API_NAMES
+from indicoio.utils.api import api_handler
+from indicoio.utils.image import image_preprocess
+from indicoio.utils.errors import IndicoError
+
+
+CLIENT_SERVER_MAP = dict((api, api.strip().replace("_", "").lower()) for api in API_NAMES)
+SERVER_CLIENT_MAP = dict((v, k) for k, v in CLIENT_SERVER_MAP.iteritems())
+
+def multi(data, type, apis, available, batch=False, **kwargs):
+    """
+    Helper to make multi requests of different types.
+
+    :param data: data to be sent in JSON.
+    :param type: String type of API request
+    :param apis: List of apis to use.
+    :param apis: List of apis available for use.
+    :type data: str or image
+    :type type: str or unicode
+    :type apis: list of str
+    :type available: list of str
+    :rtype: Dictionary of api responses
+    """
+    # Client side api name checking - strictly only accept func name api
+    invalid_apis = [api for api in apis if api not in available]
+    if invalid_apis:
+        raise IndicoError("%s are not valid %s APIs. Please reference the available APIs below:\n%s"
+                            % (", ".join(invalid_apis), type, ", ".join(available))
+                        )
+    # Convert client api names to server names before sending request
+    apis = map(CLIENT_SERVER_MAP.get, apis)
+    result = api_handler(data, url_params = {"apis":apis, "batch":batch}, **kwargs)
+    return handle_response(result)
+
+def handle_response(result):
+    # Parse out the results to a dicionary of api: result
+    return dict((SERVER_CLIENT_MAP[api], parsed_response(api, res))
+        for api, res in result.iteritems())
+
+
+def predict_text(input_text, apis=TEXT_APIS, cloud=None, batch=False, api_key=None, **kwargs):
+    """
+    Given input text, returns the results of specified text apis. Possible apis
+    include: [ 'text_tags', 'political', 'sentiment', 'language' ]
+
+    Example usage:
+
+    .. code-block:: python
+
+       >>> import indicoio
+       >>> text = 'Monday: Delightful with mostly sunny skies. Highs in the low 70s.'
+       >>> results = indicoio.text(data = text, apis = ["language", "sentiment"])
+       >>> language_results = results["langauge"]
+       >>> sentiment_results = results["sentiment"]
+
+    :param text: The text to be analyzed.
+    :param apis: List of apis to use.
+    :type text: str or unicode
+    :type apis: list of str
+    :rtype: Dictionary of api responses
+    """
+
+    return multi(
+        api="apis",
+        data=input_text,
+        type="text",
+        available = TEXT_APIS,
+        cloud=cloud,
+        batch=batch,
+        api_key=api_key,
+        apis=apis,
+        **kwargs)
+
+
+def predict_image(image, apis=IMAGE_APIS, cloud=None, batch=False, api_key=None, **kwargs):
+    """
+    Given input image, returns the results of specified image apis. Possible apis
+    include: ['fer', 'facial_features', 'image_features']
+
+    Example usage:
+
+    .. code-block:: python
+
+       >>> import indicoio
+       >>> import numpy as np
+       >>> face = np.zeros((48,48)).tolist()
+       >>> results = indicoio.image(image = face, apis = ["fer", "facial_features"])
+       >>> fer = results["fer"]
+       >>> facial_features = results["facial_features"]
+
+    :param text: The text to be analyzed.
+    :param apis: List of apis to use.
+    :type text: str or unicode
+    :type apis: list of str
+    :rtype: Dictionary of api responses
+    """
+
+    return multi(
+        api="apis",
+        data=image_preprocess(image, batch=batch),
+        type="image",
+        available=IMAGE_APIS,
+        cloud=cloud,
+        batch=batch,
+        api_key=api_key,
+        apis=apis,
+        **kwargs)
+
+def parsed_response(api, response):
+    result = response.get('results', False)
+    if result:
+        return result
+    raise IndicoError(
+        "Sorry, the %s API returned an unexpected response.\n\t%s"
+        % (api, response.get('error', ""))
+    )

setup.py

@@ -1,6 +1,8 @@
 """
 Setup for indico apis
 """
+from indicoio import VERSION
+
 try:
     from setuptools import setup
 except ImportError:
@@ -8,7 +10,7 @@ except ImportError:
 
 setup(
     name="IndicoIo",
-    version='0.6.0',
+    version=VERSION,
     packages=[
         "indicoio",
         "indicoio.text",

tests/test_remote.py

@@ -9,6 +9,8 @@ from indicoio import config
 from indicoio import political, sentiment, fer, facial_features, language, image_features, text_tags
 from indicoio import batch_political, batch_sentiment, batch_fer, batch_facial_features
 from indicoio import batch_language, batch_image_features, batch_text_tags
+from indicoio import predict_image, predict_text, batch_predict_image, batch_predict_text
+from indicoio.utils.errors import IndicoError
 
 DIR = os.path.dirname(os.path.realpath(__file__))
 
@@ -48,7 +50,7 @@ class BatchAPIRun(unittest.TestCase):
 
     def test_batch_fer_bad_b64(self):
         test_data = ["$bad#FI jeaf9(#0"]
-        self.assertRaises(ValueError, batch_fer, test_data, api_key=self.api_key)
+        self.assertRaises(IndicoError, batch_fer, test_data, api_key=self.api_key)
 
     def test_batch_fer_good_b64(self):
         test_data = ["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"]
@@ -62,9 +64,15 @@ class BatchAPIRun(unittest.TestCase):
         self.assertTrue(isinstance(response, list))
         self.assertTrue(isinstance(response[0], dict))
 
+    def test_batch_fer_pil_image(self):
+        test_data = [Image.open(os.path.normpath(os.path.join(DIR, "data/fear.png")))]
+        response = batch_fer(test_data, api_key=self.api_key)
+        self.assertTrue(isinstance(response, list))
+        self.assertTrue(isinstance(response[0], dict))
+
     def test_batch_fer_nonexistant_filepath(self):
         test_data = ["data/unhappy.png"]
-        self.assertRaises(ValueError, batch_fer, test_data, api_key=self.api_key)
+        self.assertRaises(IndicoError, batch_fer, test_data, api_key=self.api_key)
 
 
     def test_batch_facial_features(self):
@@ -107,6 +115,45 @@ class BatchAPIRun(unittest.TestCase):
         self.assertTrue(isinstance(response, list))
         self.assertTrue(response[0]['English'] > 0.25)
 
+    def test_batch_multi_api_image(self):
+        test_data = [generate_array((48,48)), generate_int_array((48,48))]
+        response = batch_predict_image(test_data, apis=config.IMAGE_APIS, api_key=self.api_key)
+
+        self.assertTrue(isinstance(response, dict))
+        self.assertTrue(set(response.keys()) == set(config.IMAGE_APIS))
+        self.assertTrue(isinstance(response["fer"], list))
+
+    def test_batch_multi_api_text(self):
+        test_data = ['clearly an english sentence']
+        response = batch_predict_text(test_data, apis=config.TEXT_APIS, api_key=self.api_key)
+
+        self.assertTrue(isinstance(response, dict))
+        self.assertTrue(set(response.keys()) == set(config.TEXT_APIS))
+
+    def test_default_multi_api_text(self):
+        test_data = ['clearly an english sentence']
+        response = batch_predict_text(test_data, api_key=self.api_key)
+
+        self.assertTrue(isinstance(response, dict))
+        self.assertTrue(set(response.keys()) == set(config.TEXT_APIS))
+
+    def test_multi_api_bad_api(self):
+        self.assertRaises(IndicoError,
+                          batch_predict_text,
+                          "this shouldn't work",
+                          apis=["sentiment", "somethingbad"])
+
+    def test_multi_bad_mixed_api(self):
+        self.assertRaises(IndicoError,
+                            predict_text,
+                            "this shouldn't work",
+                            apis=["fer", "sentiment", "facial_features"])
+    def test_batch_multi_bad_mixed_api(self):
+        self.assertRaises(IndicoError,
+                            batch_predict_text,
+                            ["this shouldn't work"],
+                            apis=["fer", "sentiment", "facial_features"])
+
     def test_batch_set_cloud(self):
         test_data = ['clearly an english sentence']
         self.assertRaises(ConnectionError,
@@ -118,6 +165,9 @@ class BatchAPIRun(unittest.TestCase):
 
 class FullAPIRun(unittest.TestCase):
 
+    def setUp(self):
+        self.api_key = config.api_key
+
     def load_image(self, relpath, as_grey=False):
         im = Image.open(os.path.normpath(os.path.join(DIR, relpath))).convert('L');
         pixels = list(im.getdata())
@@ -178,12 +228,26 @@ class FullAPIRun(unittest.TestCase):
         self.assertTrue(isinstance(response, dict))
         self.assertEqual(fer_set, set(response.keys()))
 
+    def test_good_int_array_fer(self):
+        fer_set = set(['Angry', 'Sad', 'Neutral', 'Surprise', 'Fear', 'Happy'])
+        test_face = generate_int_array((48,48))
+        response = fer(test_face)
+
+        self.assertTrue(isinstance(response, dict))
+        self.assertEqual(fer_set, set(response.keys()))
+
     def test_happy_fer(self):
         test_face = self.load_image("data/happy.png", as_grey=True)
         response = fer(test_face)
         self.assertTrue(isinstance(response, dict))
         self.assertTrue(response['Happy'] > 0.5)
 
+    def test_happy_fer_pil(self):
+        test_face = Image.open(os.path.normpath(os.path.join(DIR, "data/happy.png"))).convert('L');
+        response = fer(test_face)
+        self.assertTrue(isinstance(response, dict))
+        self.assertTrue(response['Happy'] > 0.5)
+
     def test_fear_fer(self):
         test_face = self.load_image("data/fear.png", as_grey=True)
         response = fer(test_face)
@@ -206,6 +270,15 @@ class FullAPIRun(unittest.TestCase):
         self.assertEqual(len(response), 48)
         self.check_range(response)
 
+    def test_good_int_array_facial_features(self):
+        fer_set = set(['Angry', 'Sad', 'Neutral', 'Surprise', 'Fear', 'Happy'])
+        test_face = generate_int_array((48,48))
+        response = facial_features(test_face)
+
+        self.assertTrue(isinstance(response, list))
+        self.assertEqual(len(response), 48)
+        self.check_range(response)
+
     # TODO: uncomment this test once the remote server is updated to
     # deal with image_urls
     # def test_image_url(self):
@@ -232,6 +305,21 @@ class FullAPIRun(unittest.TestCase):
         self.assertEqual(len(response), 2048)
         self.check_range(response)
 
+    def test_multi_api_image(self):
+        test_data = generate_array((48,48))
+        response = predict_image(test_data, apis=config.IMAGE_APIS, api_key=self.api_key)
+
+        self.assertTrue(isinstance(response, dict))
+        self.assertTrue(set(response.keys()) == set(config.IMAGE_APIS))
+
+    def test_multi_api_text(self):
+        test_data = 'clearly an english sentence'
+        response = predict_text(test_data, apis=config.TEXT_APIS, api_key=self.api_key)
+
+        self.assertTrue(isinstance(response, dict))
+        self.assertTrue(set(response.keys()) == set(config.TEXT_APIS))
+
+
     def test_language(self):
         language_set = set([
             'English',
@@ -297,7 +385,7 @@ class FullAPIRun(unittest.TestCase):
 
     def test_set_api_key(self):
         test_data = 'clearly an english sentence'
-        self.assertRaises(ValueError,
+        self.assertRaises(IndicoError,
                           language,
                           test_data,
                           api_key ='invalid_api_key')
@@ -306,7 +394,7 @@ class FullAPIRun(unittest.TestCase):
         config.api_key = 'invalid_api_key'
 
         self.assertEqual(config.api_key, 'invalid_api_key')
-        self.assertRaises(ValueError,
+        self.assertRaises(IndicoError,
                           language,
                           test_data)
 
@@ -323,6 +411,9 @@ def flatten(container):
 def generate_array(size):
     return [[random.random() for _ in xrange(size[0])] for _ in xrange(size[1])]
 
+def generate_int_array(size):
+    return [[random.randint(0, 50) for _ in xrange(size[0])] for _ in xrange(size[1])]
+
 
 if __name__ == "__main__":
     unittest.main()