Merge pull request #24 from refuel-ai/explain_docs

Add application explanation docs

docs/python-sdk.md

@@ -370,7 +370,7 @@ inputs = [
   {'col_1': 'value_1', 'col_2': 'value_2' ...},
 ]
 
-response = refuel_client.label(application='<APPLICATION NAME>', inputs=inputs)
+response = refuel_client.label(application='<APPLICATION NAME>', inputs=inputs, explain=False)
 ```
 
 Each element in `inputs` is a dictionary, with keys as names of the input columns defined in the task. For example, let's consider an application for sentiment classification called `my_sentiment_classifier`, with two input fields - `source` and `text`. You can use it as follows:
@@ -398,22 +398,32 @@ response = refuel_client.label(application='my_sentiment_classifier', inputs=inp
      'refuel_fields': 
         {
           'sentiment': {
-            'label': 'positive,
+            'label': ['positive'],
             'confidence': 0.9758
           }
         }
     }]
 }
 ```
 
+You can also set the optional `explain` parameter to `True` to get an explanation for why the provided label was returned. The explanation will be returned in the `explanation` field in the response, along with the `label` and `confidence`:
+
+```
+'sentiment': {
+    'label': ['positive'],
+    'confidence': 0.9758,
+    'explanation': 'The model predicted a positive sentiment because the text contains positive words like "liked" and "good".'
+  }
+```
+
 
 ### Share feedback for application outputs
 
 The SDK allows users to log feedback for online predictions. When logging predictions, it is important to identify the input request for which you're logging feedback using `refuel_uuid` from the response above:
 
 ```python
 
-label = {'sentiment': 'negative'}
+label = {'sentiment': ['negative']}
 refuel_client.feedback(application='my_sentiment_classifier', refuel_uuid='...', label=label)
 ```