Filter conversion plugins

Closes #12

* Shows only analysis plugins by default on /api/plugins
* Adds a plugin_type parameter to get other types of plugins
* default_plugin chosen from analysis plugins

senpy/api.py

@@ -26,6 +26,13 @@ API_PARAMS = {
         "aliases": ["emotionModel", "emoModel"],
         "required": False
     },
+    "plugin_type": {
+        "@id": "pluginType",
+        "description": 'What kind of plugins to list',
+        "aliases": ["pluginType", "plugin_type"],
+        "required": True,
+        "default": "analysisPlugin"
+    },
     "conversion": {
         "@id": "conversion",
         "description": "How to show the elements that have (not) been converted",

senpy/blueprints.py

@@ -121,7 +121,9 @@ def api():
 @basic_api
 def plugins():
     sp = current_app.senpy
-    dic = Plugins(plugins=list(sp.plugins.values()))
+    ptype = request.params.get('plugin_type')
+    plugins = sp.filter_plugins(plugin_type=ptype)
+    dic = Plugins(plugins=list(plugins.values()))
     return dic
 
 

senpy/extensions.py

@@ -5,7 +5,8 @@ It orchestrates plugin (de)activation and analysis.
 from future import standard_library
 standard_library.install_aliases()
 
-from .plugins import SentimentPlugin, SenpyPlugin
+from . import plugins
+from .plugins import SenpyPlugin
 from .models import Error, Entry, Results
 from .blueprints import api_blueprint, demo_blueprint, ns_blueprint
 from .api import API_PARAMS, NIF_PARAMS, parse_params
@@ -367,6 +368,22 @@ class Senpy(object):
 
     def filter_plugins(self, **kwargs):
         """ Filter plugins by different criteria """
+        ptype = kwargs.pop('plugin_type', None)
+        logger.debug('#' * 100)
+        logger.debug('ptype {}'.format(ptype))
+        if ptype:
+            try:
+                ptype = ptype[0].upper() + ptype[1:]
+                pclass = getattr(plugins, ptype)
+                logger.debug('Class: {}'.format(pclass))
+                candidates = filter(lambda x: isinstance(x, pclass),
+                                    self.plugins.values())
+            except AttributeError:
+                raise Error('{} is not a valid type'.format(ptype))
+        else:
+            candidates = self.plugins.values()
+
+        logger.debug(candidates)
 
         def matches(plug):
             res = all(getattr(plug, k, None) == v for (k, v) in kwargs.items())
@@ -374,15 +391,11 @@ class Senpy(object):
                 "matching {} with {}: {}".format(plug.name, kwargs, res))
             return res
 
-        if not kwargs:
-            return self.plugins
-        else:
-            return {n: p for n, p in self.plugins.items() if matches(p)}
+        if kwargs:
+            candidates = filter(matches, candidates)
+        return {p.name: p for p in candidates}
 
-    def sentiment_plugins(self):
-        """ Return only the sentiment plugins """
-        return {
-            p: plugin
-            for p, plugin in self.plugins.items()
-            if isinstance(plugin, SentimentPlugin)
-        }
+    @property
+    def analysis_plugins(self):
+        """ Return only the analysis plugins """
+        return self.filter_plugins(plugin_type='analysisPlugin')

senpy/plugins/__init__.py

@@ -30,6 +30,15 @@ class SenpyPlugin(models.Plugin):
     def get_folder(self):
         return os.path.dirname(inspect.getfile(self.__class__))
 
+    def activate(self):
+        pass
+
+    def deactivate(self):
+        pass
+
+
+class AnalysisPlugin(SenpyPlugin):
+
     def analyse(self, *args, **kwargs):
         raise NotImplemented(
             'Your method should implement either analyse or analyse_entry')
@@ -48,30 +57,27 @@ class SenpyPlugin(models.Plugin):
         for i in results.entries:
             yield i
 
-    def activate(self):
-        pass
 
-    def deactivate(self):
+class ConversionPlugin(SenpyPlugin):
     pass
 
 
-class SentimentPlugin(models.SentimentPlugin, SenpyPlugin):
+class SentimentPlugin(models.SentimentPlugin, AnalysisPlugin):
     def __init__(self, info, *args, **kwargs):
         super(SentimentPlugin, self).__init__(info, *args, **kwargs)
         self.minPolarityValue = float(info.get("minPolarityValue", 0))
         self.maxPolarityValue = float(info.get("maxPolarityValue", 1))
 
 
-class EmotionPlugin(models.EmotionPlugin, SenpyPlugin):
+class EmotionPlugin(models.EmotionPlugin, AnalysisPlugin):
     def __init__(self, info, *args, **kwargs):
         super(EmotionPlugin, self).__init__(info, *args, **kwargs)
         self.minEmotionValue = float(info.get("minEmotionValue", -1))
         self.maxEmotionValue = float(info.get("maxEmotionValue", 1))
 
 
-class EmotionConversionPlugin(models.EmotionConversionPlugin, SenpyPlugin):
-    def __init__(self, info, *args, **kwargs):
-        super(EmotionConversionPlugin, self).__init__(info, *args, **kwargs)
+class EmotionConversionPlugin(models.EmotionConversionPlugin, ConversionPlugin):
+    pass
 
 
 class ShelfMixin(object):