Update app.py

app.py

@@ -289,6 +289,87 @@ models = [
 system = ConsciousSystem(models)
 system.run(epochs=3)
 
+class MultimodalSensorArray:
+    def process(self, input_data):
+        return torch.tensor(input_data, dtype=torch.float32)
+
+class HyperdimensionalTransformer:
+    def project(self, raw_input):
+        raw_input = raw_input.float()
+        return torch.nn.functional.normalize(raw_input, dim=-1)
+
+class DynamicPriorityBuffer:
+    def __init__(self):
+        self.buffer = []
+    def update(self, data):
+        self.buffer.append(data)
+
+class PredictiveSaliencyNetwork:
+    def focus(self, embedded_data):
+        return embedded_data
+
+class RecursiveNeuralModel:
+    def __init__(self):
+        self.state = torch.zeros(1)
+    def update(self, workspace):
+        self.state += 0.1
+    def read_state(self):
+        return self.state
+
+class TheoryOfMindEngine:
+    def infer(self, data):
+        return torch.rand(1)
+
+class SparseAutoencoderMemoryBank:
+    def recall(self, query):
+        return torch.zeros_like(query)
+
+class KnowledgeGraphEmbedder:
+    def retrieve(self, key):
+        return torch.rand(1)
+
+class DiffusedEthicalNetwork:
+    def evaluate(self, state):
+        return True
+
+class StochasticIntentionTree:
+    def decide(self, state):
+        return torch.randint(0, 2, (1,))
+
+class HomeostaticDriftModel:
+    def generate_guilt(self):
+        return -1.0
+
+class ConsciousAGI:
+    def __init__(self):
+        self.sensors = MultimodalSensorArray()
+        self.embedding_space = HyperdimensionalTransformer()
+        self.global_workspace = DynamicPriorityBuffer()
+        self.attention_mechanism = PredictiveSaliencyNetwork()
+        self.self_model = RecursiveNeuralModel()
+        self.meta_cognition = TheoryOfMindEngine()
+        self.episodic_memory = SparseAutoencoderMemoryBank()
+        self.semantic_memory = KnowledgeGraphEmbedder()
+        self.value_system = DiffusedEthicalNetwork()
+        self.goal_generator = StochasticIntentionTree()
+        self.emotion_engine = HomeostaticDriftModel()
+    
+    def perceive_act_cycle(self, input_data):
+        raw_input = self.sensors.process(input_data)
+        embedded = self.embedding_space.project(raw_input)
+        salient_data = self.attention_mechanism.focus(embedded)
+        self.global_workspace.update(salient_data)
+        self.self_model.update(self.global_workspace)
+        current_state = self.self_model.read_state()
+        ethical_check = self.value_system.evaluate(current_state)
+        if ethical_check:
+            return self.goal_generator.decide(current_state)
+        else:
+            return self.emotion_engine.generate_guilt()
+
+agi = ConsciousAGI()
+print(agi.perceive_act_cycle([1, 0, 1]))
+
 class ConsciousSupermassiveNN:
     def __init__(self):
         self.snn = self.create_snn()