Initial change from dicts to objects (#9)

* Initial change from dicts to objects

* add newline at EOF

* Add __str__ methods to objects

* Change naming of objects to remove 'Cohere'

* Change object names to be nouns

* change sanity-test.py and rename ChooseBest object

cohere/__init__.py

@@ -25,22 +25,22 @@ def generate(self, model, prompt, max_tokens=20, temperature=1, k=0, p=0.75):
         "p": p,
       })
     response = self.__request(json_body, GENERATE_URL, model)
-    return response["text"]
+    return Generation(response["text"])
 
   def similarity(self, model, anchor, targets):
     json_body = json.dumps({
         "anchor": anchor,
         "targets": targets,
       })
     response = self.__request(json_body, SIMILARITY_URL, model)
-    return response["similarities"]
+    return Similarities(response["similarities"])
 
   def embed(self, model, texts):
     json_body = json.dumps({
         "texts": texts,
       })
     response = self.__request(json_body, EMBED_URL, model)
-    return response["embeddings"]
+    return Embeddings(response["embeddings"])
 
   def choose_best(self, model, query, options, mode=""):
     json_body = json.dumps({
@@ -49,14 +49,14 @@ def choose_best(self, model, query, options, mode=""):
         "mode": mode,
       })
     response = self.__request(json_body, CHOOSE_BEST_URL, model)
-    return response
+    return BestChoices(response['likelihoods'], mode)
 
   def likelihood(self, model, text):
     json_body = json.dumps({
         "text": text,
       })
     response = self.__request(json_body, LIKELIHOOD_URL, model)
-    return response
+    return Likelihoods(response['likelihood'], response['token_likelihoods'])
 
   def __request(self, json_body, endpoint, model):
     headers = {
@@ -96,3 +96,40 @@ def __repr__(self):
             self.message,
             self.http_status,
         )
+
+class Generation:
+    def __init__(self, text):
+        self.text = text
+
+    def __str__(self):
+        return self.text
+
+class Similarities:
+    def __init__(self, similarities):
+        self.similarities = similarities
+
+    def __str__(self):
+        return str(self.similarities)
+
+class Embeddings:
+    def __init__(self, embeddings):
+        self.embeddings = embeddings
+
+    def __str__(self):
+        return str(self.embeddings)
+
+class BestChoices:
+    def __init__(self, likelihoods, mode):
+        self.likelihoods = likelihoods
+        self.mode = mode
+
+    def __str__(self):
+        return str(self.likelihoods)
+
+class Likelihoods:
+    def __init__(self, likelihood, token_likelihoods):
+        self.likelihood = likelihood
+        self.token_likelihoods = token_likelihoods
+
+    def __str__(self):
+        return str(self.likelihood) + "\n" + str(self.token_likelihoods)

sanity-test.py

@@ -10,7 +10,7 @@
             model="baseline-355m",
             prompt="co:here",
             max_tokens=10)
-print('prediction: {}'.format(prediction))
+print('prediction: {}'.format(prediction.text))
 
 embeddings = co.embed(
             model="baseline-124m",
@@ -20,21 +20,21 @@
             model="baseline-124m",
            	anchor="cohere ai",
             targets=["co:here", "cohere"])
-print('Similarity value of `co:here`: {}'.format(similarities[0]))
+print('Similarity value of `co:here`: {}'.format(similarities.similarities[0]))
 
 options = co.choose_best(
             model="baseline-355m",
             query="hello {}",
             options=["world", "cohere"])
-print('first option is `world`, with likelihood value of {}'.format(options['likelihoods'][0]))
+print('first option is `world`, with likelihood value of {}'.format(options.likelihoods[0]))
 print('Selected mode was {}'.format(options['mode']))
 
 likelihood = co.likelihood(
             model="baseline-355m",
             text="hello, my name is johnny SURPRISE")
-print('likelihood of text is {}'.format(likelihood['likelihood']))
+print('likelihood of text is {}'.format(likelihood.likelihood))
 print('token likelihoods are: (first token has no likelihood)')
-for token in likelihood['token_likelihoods']:
+for token in likelihood.token_likelihoods:
       print(token['token'], token.get('likelihood', ''))
 try:
 	predictions = co.generate(