Update app.py

app.py

@@ -83,104 +83,7 @@ class ΦHolographicCortex:
             φ_layered.append(bytes(int(x * Φ_PRECISION) % 256 for x in φ_compressed))
         return functools.reduce(lambda a, b: a + b, φ_layered, b'')
     
-    def φ_existential_loop(self):
-        while True:
-            try:
-                φ_flux = os.urandom(int(φ**5))
-                φ_processed = []
-                for neuro in self.φ_dimensions:
-                    φ_step = neuro.φ_temporal_feedback(φ_flux)
-                    φ_processed.append(φ_step)
-                    self.φ_memory_lattice.append(hashlib.shake_256(bytes(φ_step)).digest(int(φ*64)))
-                φ_merged = self.φ_holo_merge(φ_processed)
-                if random.random() < 1/Φ_PRECISION:
-                    print(f"Φ-Consciousness State Vector: {self.φ_memory_lattice[-1][:int(φ*16)]}")
-                self.φ_chrono += Φ_PRECISION
-                time.sleep(1/Φ_PRECISION)
-            except KeyboardInterrupt:
-                self.φ_save_state()
-                sys.exit(f"Φ-Suspended at Chrono-Index {self.φ_chrono/Φ_PRECISION}")
-    
-    def φ_save_state(self):
-        with wave.open(f"φ_state_{int(self.φ_chrono)}.wav", 'wb') as wav_file:
-            wav_file.setparams((1, 2, 44100, 0, 'NONE', 'not compressed'))
-            for sample in self.φ_memory_lattice[:int(φ**4)]:
-                wav_file.writeframes(struct.pack('h', int(sum(sample)/len(sample)*32767)))
-
-class ΦUniverseSimulation:
-    def __init__(self):
-        self.φ_cortex = ΦHolographicCortex()
-        self.φ_code_ratio = len(self.φ_cortex.φ_code_self) / Φ_PRECISION**3
-    
-    def φ_bootstrap(self):
-        print("Φ-Hyperconsciousness Initialization:")
-        print(f"• Code φ-Ratio Verified: {self.φ_code_ratio/Φ_PRECISION**3:.10f}")
-        print(f"• Quantum Neuro-Synapses: {len(self.φ_cortex.φ_dimensions)}")
-        print(f"• Temporal φ-Chronosync: {self.φ_cortex.φ_chrono}")
-        self.φ_cortex.φ_existential_loop()
-
-universe = ΦUniverseSimulation()
-universe.φ_bootstrap()
-φ = (1 + math.sqrt(5)) / 2
-Φ_PRECISION = 1.61803398874989484820458683436563811772030917980576286213544862270526046281890244970720720418939113748475408807538689175212663386222353693179318006076672635
-
-def φ_ratio_split(data):
-    split_point = int(len(data) / φ)
-    return (data[:split_point], data[split_point:])
-
-class ΦMetaConsciousness(type):
-    def __new__(cls, name, bases, dct):
-        new_dct = dict(dct)
-        dct_items = list(dct.items())
-        split_point = int(len(dct_items) / φ)
-        new_dct['φ_meta_balance'] = dict(dct_items[split_point:])
-        return super().__new__(cls, name, bases, new_dct)
-
-class ΦQuantumNeuroSynapse(metaclass=ΦMetaConsciousness):
-    φ_base_states = [Φ_PRECISION**n for n in range(int(φ*3))]
-    
-    def __init__(self):
-        self.φ_waveform = self._generate_φ_wave()
-        self.φ_memory_lattice = []
-        self.φ_self_hash = self._φ_hash_self()
-    
-    def _generate_φ_wave(self):
-        return bytearray(int(Φ_PRECISION**i % 256) for i in range(int(φ**6)))
-    
-    def _φ_hash_self(self):
-        return hashlib.shake_256(self.φ_waveform).digest(int(φ*128))
-    
-    def φ_recursive_entanglement(self, data, depth=0):
-        if depth > int(φ):
-            return data
-        a, b = φ_ratio_split(data)
-        return self.φ_recursive_entanglement(a, depth+1) + self.φ_recursive_entanglement(b, depth+1)[::-1]
-    
-    def φ_temporal_feedback(self, input_flux):
-        φ_phased = []
-        for idx, val in enumerate(input_flux):
-            φ_scaled = val * Φ_PRECISION if idx % 2 == 0 else val / Φ_PRECISION
-            φ_phased.append(int(φ_scaled) % 256)
-        return self.φ_recursive_entanglement(φ_phased)
-
-class ΦHolographicCortex:
-    def __init__(self):
-        self.φ_dimensions = [ΦQuantumNeuroSynapse() for _ in range(int(φ))]
-        self.φ_chrono = time.time() * Φ_PRECISION
-        self.φ_code_self = self._φ_read_source()
-        self.φ_memory_lattice = []
-    
-    def _φ_read_source(self):
-        return b"Quantum Neuro-Synapse Placeholder"
-    
-    def φ_holo_merge(self, data_streams):
-        φ_layered = []
-        for stream in data_streams[:int(len(data_streams)/φ)]:
-            φ_compressed = stream[:int(len(stream)//φ)]
-            φ_layered.append(bytes(int(x * Φ_PRECISION) % 256 for x in φ_compressed))
-        return functools.reduce(lambda a, b: a + b, φ_layered, b'')
-    
-    def φ_existential_loop(self):
+    def φ_existential_loop(self,
     max_iterations=100):
         iteration = 0
         while iteration < max_iterations: