Merge branch 'trailminator' of https://git.wmi.amu.edu.pl/s478841/SD-project-22 into trailminator

trailminator/dp.py

@@ -1,5 +1,14 @@
 class Dp:
-    def __init__(self):
-        self.actions = {
-            "name": ['imie', 'imię'] 
-        }
+    def __init__(self, act, param):
+        self.act = act
+        self.param = param
+        self.answers = {
+            'request': {'imie': "welcomemsg"}
+        }
+        
+    def identify(self):
+        req = self.answers.get(self.act)
+        response = req.get(self.param[0]) if not req is None else "null"
+        return response if not response is None else "null"
+    
+    

trailminator/nlg.py

@@ -1,20 +1,14 @@
-from asyncio.constants import SENDFILE_FALLBACK_READBUFFER_SIZE
+import random
+from urllib import response
 
+from scipy import rand
 
 class Nlg:
-    def __init__(self):
-        
+    def __init__(self, response_type: str) -> None:
+        self._response_type = response_type
+        self.responses = {"welcomemsg": ["I AM TRAIlMinATor!", "Cześć, jestem TrailMinator"],
+                          "null": "Can't hear you"}
 
-        self.reactions = {
-            "name": "Mam na imie Trailminator."
-        }
-
-    def detect_action(self, message):
-        for k, v in self.actions:
-            if any(m in v for m in message):
-                return k
-
-    def generate_action(self, action):
-        return self.reactions[action]
-
-    
+    def response(self) -> str:
+        avail_resps = self.responses.get(self._response_type)
+        return avail_resps[random.randint(0, len(avail_resps)-1)]

trailminator/nlu.py

@@ -42,5 +42,3 @@ class Nlu:
 
         return (act, param)
 
-nlu = Nlu()
-print(nlu.parse('jak masz na imie?'))

trailminator/trailminator.py

@@ -0,0 +1,12 @@
+import dst
+import nlu
+import dp
+import nlg
+
+if __name__ == "__main__":
+    msg = "Jak masz na imie?"
+    dp_params = nlu.Nlu().parse(msg)
+    # print(dp_params)
+    nlg_params = dp.Dp(*dp_params).identify()
+    # print(nlg_params)
+    print(f"{msg}\n{nlg.Nlg(nlg_params).response()}")