Merge pull request #4 from IndicoDataSolutions/local-api

Local api

indicoio/__init__.py

@@ -1,10 +1,22 @@
+from functools import partial
+from utils import config
+
 JSON_HEADERS = {'Content-type': 'application/json', 'Accept': 'text/plain'}
 
-Version, version, __version__, VERSION = ('0.4.4',) * 4
+Version, version, __version__, VERSION = ('0.4.5',) * 4
 
 from text.sentiment import political, posneg
 from text.sentiment import posneg as sentiment
 from text.lang import language
 from images.fer import fer
 from images.features import facial_features
-from images.features import image_features
+from images.features import image_features
+
+political = partial(political, config.api_root)
+posneg = partial(posneg, config.api_root)
+sentiment = partial(sentiment, config.api_root)
+posneg = partial(sentiment, config.api_root)
+language = partial(language, config.api_root)
+fer = partial(fer, config.api_root)
+facial_features = partial(facial_features, config.api_root)
+image_features = partial(image_features, config.api_root)

indicoio/images/features.py

@@ -6,7 +6,7 @@ import numpy as np
 from indicoio import JSON_HEADERS
 from indicoio.utils import image_preprocess
 
-def facial_features(image):
+def facial_features(api_root, image):
     """
     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.
@@ -30,14 +30,14 @@ def facial_features(image):
     """
     
     data_dict = json.dumps({"face": image})
-    response = requests.post("http://api.indico.io/facialfeatures", data=data_dict, headers=JSON_HEADERS)
+    response = requests.post(api_root + "facialfeatures", data=data_dict, headers=JSON_HEADERS)
     response_dict = response.json()
     if 'response' not in response_dict:
       raise ValueError(response_dict.values()[0])
     else:
       return response_dict['response']
 
-def image_features(image):
+def image_features(api_root, 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.
@@ -69,7 +69,7 @@ def image_features(image):
     """
     image = image_preprocess(image)
     data_dict = json.dumps({"image": image})
-    response = requests.post("http://api.indico.io/imagefeatures", data=data_dict, headers=JSON_HEADERS)
+    response = requests.post(api_root + "imagefeatures", data=data_dict, headers=JSON_HEADERS)
     response_dict = response.json()
     if 'Features' not in response_dict:
       raise ValueError(response_dict.values()[0])

indicoio/images/fer.py

@@ -4,7 +4,7 @@ import requests
 import numpy as np
 from indicoio import JSON_HEADERS
 
-def fer(image):
+def fer(api_root, image):
     """
     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 
@@ -29,7 +29,7 @@ def fer(image):
     """
     
     data_dict = json.dumps({"face": image})
-    response = requests.post("http://api.indico.io/fer", data=data_dict, headers=JSON_HEADERS)
+    response = requests.post(api_root + "fer", data=data_dict, headers=JSON_HEADERS)
     response_dict = response.json()
     if len(response_dict) < 2:
       raise ValueError(response_dict.values()[0])

indicoio/local/__init__.py

@@ -0,0 +1,20 @@
+from functools import partial
+from indicoio.utils import 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.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)

indicoio/text/lang.py

@@ -3,7 +3,7 @@ import json
 
 from indicoio import JSON_HEADERS
 
-def language(text):
+def language(api_root, text):
     """
     Given input text, returns a probability distribution over 33 possible 
     languages of what language the text was written in.
@@ -27,7 +27,7 @@ def language(text):
     """
     
     data_dict = json.dumps({'text': text})
-    response = requests.post("http://api.indico.io/language", data=data_dict, headers=JSON_HEADERS)
+    response = requests.post(api_root + "language", data=data_dict, headers=JSON_HEADERS)
     response_dict = response.json()
     if len(response_dict) < 2:
       raise ValueError(response_dict.values()[0])

indicoio/text/sentiment.py

@@ -4,7 +4,7 @@ import json
 from indicoio import JSON_HEADERS
 from indicoio.utils import normalize
 
-def political(text):
+def political(api_root, text):
     """
     Given input text, returns a probability distribution over the political alignment of the speaker.
 
@@ -31,14 +31,14 @@ def political(text):
     """
 
     data_dict = json.dumps({'text': text})
-    response = requests.post("http://api.indico.io/political", data=data_dict, headers=JSON_HEADERS)
+    response = requests.post(api_root + "political", data=data_dict, headers=JSON_HEADERS)
     response_dict = response.json()
     if len(response_dict) < 2:
       raise ValueError(response_dict.values()[0])
     else:
       return response_dict
 
-def posneg(text):
+def posneg(api_root, text):
     """
     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.
@@ -60,7 +60,7 @@ def posneg(text):
     """
     
     data_dict = json.dumps({'text': text})
-    response = requests.post("http://api.indico.io/sentiment", data=data_dict, headers=JSON_HEADERS)
+    response = requests.post(api_root + "sentiment", data=data_dict, headers=JSON_HEADERS)
     response_dict = response.json()
     if 'Sentiment' not in response_dict:
       raise ValueError(response_dict.values()[0])

indicoio/utils/config.py

@@ -0,0 +1,2 @@
+local_api_root = "http://localhost:9438/"
+api_root = "http://api.indico.io/"

tests/test_local.py

@@ -0,0 +1,90 @@
+import unittest
+
+import numpy as np
+
+from indicoio.local import political, sentiment, fer, facial_features, language
+
+
+class FullAPIRun(unittest.TestCase):
+
+    def test_political(self):
+        political_set = set(['Libertarian', 'Liberal', 'Conservative', 'Green'])
+        test_string = "Guns don't kill people, people kill people."
+        response = political(test_string)
+
+        self.assertTrue(isinstance(response, dict))
+        self.assertEqual(political_set, set(response.keys()))
+
+    def test_posneg(self):
+        posneg_set = set(['Sentiment'])
+        test_string = "Worst song ever."
+        response = sentiment(test_string)
+
+        self.assertTrue(isinstance(response, float))
+
+    def test_good_fer(self):
+        fer_set = set(['Angry', 'Sad', 'Neutral', 'Surprise', 'Fear', 'Happy'])
+        test_face = np.linspace(0,50,48*48).reshape(48,48).tolist()
+        response = fer(test_face)
+
+        self.assertTrue(isinstance(response, dict))
+        self.assertEqual(fer_set, set(response.keys()))
+
+    def test_bad_fer(self):
+        fer_set = set(['Angry', 'Sad', 'Neutral', 'Surprise', 'Fear', 'Happy'])
+        test_face = np.linspace(0,50,56*56).reshape(56,56).tolist()
+        response = fer(test_face)
+
+        self.assertTrue(isinstance(response, dict))
+        self.assertEqual(fer_set, set(response.keys()))
+
+    def test_good_facial_features(self):
+        test_face = np.linspace(0,50,48*48).reshape(48,48).tolist()
+        response = facial_features(test_face)
+
+        self.assertTrue(isinstance(response, list))
+        self.assertEqual(len(response), 48)
+
+    def test_language(self):
+        language_set = set([
+            'English',
+            'Spanish',
+            'Tagalog',
+            'Esperanto',
+            'French',
+            'Chinese',
+            'French',
+            'Bulgarian',
+            'Latin',
+            'Slovak',
+            'Hebrew',
+            'Russian',
+            'German',
+            'Japanese',
+            'Korean',
+            'Portuguese',
+            'Italian',
+            'Polish',
+            'Turkish',
+            'Dutch',
+            'Arabic',
+            'Persian (Farsi)',
+            'Czech',
+            'Swedish',
+            'Indonesian',
+            'Vietnamese',
+            'Romanian',
+            'Greek',
+            'Danish',
+            'Hungarian',
+            'Thai',
+            'Finnish',
+            'Norwegian',
+            'Lithuanian'
+        ])
+        language_dict = language('clearly an english sentence')
+        self.assertEqual(language_set, set(language_dict.keys()))
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/test_remote.py

@@ -20,8 +20,7 @@ class FullAPIRun(unittest.TestCase):
         test_string = "Worst song ever."
         response = sentiment(test_string)
 
-        self.assertTrue(isinstance(response, dict))
-        self.assertEqual(posneg_set, set(response.keys()))
+        self.assertTrue(isinstance(response, float))
 
     def test_good_fer(self):
         fer_set = set(['Angry', 'Sad', 'Neutral', 'Surprise', 'Fear', 'Happy'])