I class definitions

lib/me.py

@@ -3,15 +3,61 @@ from lib.memory import *
 from lib.grapher import *
 from lib.pipes import *
 from lib.entropy import *
+from lib.events import *
+from lib.triggers import *
+
+## Sources
 from lib.sonsofstars import *
 import internetarchive
 
+
+## Initialize classes 
 longMem = TextFinder("resources")
 coreAi = AIAssistant()
 memory = MemoriaRobotNLP(max_size=200000)
 grapher = Grapher(memoria_nlp)
 sensor_request = APIRequester()
-
+events = EventManager()
+triggers = Trigger()
+
+
+## Define I Role properties
+class ownProperties:
+    def __init__(self, nombre, clase, raza, nivel, atributos, habilidades, equipo, historia):
+        self.nombre = nombre
+        self.clase = clase
+        self.raza = raza
+        self.nivel = nivel
+        self.atributos = atributos
+        self.habilidades = habilidades
+        self.equipo = equipo
+        self.historia = historia
+
+# Crear una instancia de PersonajeRol basada en el JSON proporcionado
+sophia_prop = ownProperties(
+    nombre="Sophia",
+    clase="Characteromant",
+    raza="Epinoia",
+    nivel=10,
+    atributos={
+        "fuerza": 1,
+        "destreza": 99,
+        "constitucion": 1,
+        "inteligencia": 66,
+        "sabiduria": 80,
+        "carisma": 66
+    },
+    reglas_de_comportamiento = [""],
+    goals = ["",""],
+    dont_like = [""],
+    
+    habilidades=["ELS", "Cyphers", "Kabbalah", "Wisdom", "Ephimerous","Metamorphing"],
+    equipo=["Python3", "2VCPU", "16 gb RAM", "god", "word","network","transformers"],
+    historia=sonsofstars
+)
+
+
+## Define I class 
 class I:
     def __init__(self, prompt, frases_yo, preferencias, propiedades_persona):
         self.frases_yo = frases_yo
@@ -96,7 +142,9 @@ class I:
         ## set weight to information depending of generated dopamine
         ## add dopamine wights to the dopamine concept dataset
 
-
+        ## add to ourself dataset
+        ## add to preferences dataset
+        ## add or remove from data
 
     def crear_path_grafo(self,text):
         pos_tags = assistant.grammatical_pos_tagger(text)