Update app.py

app.py

@@ -18,6 +18,176 @@ import struct
 import sympy
 import re
 from gradio_client import Client
+import abc
+import aifc
+import argparse
+import array
+import ast
+import asynchat
+import asyncio
+import asyncore
+import base64
+import bdb
+import binascii
+import bisect
+import builtins
+import bz2
+import cProfile
+import calendar
+import cgi
+import cgitb
+import chunk
+import cmath
+import cmd
+import code
+import codeop
+import collections
+import colorsys
+import compileall
+import concurrent
+import configparser
+import contextlib
+import copy
+import copyreg
+import csv
+import ctypes
+import curses
+import dataclasses
+import datetime
+import dbm
+import decimal
+import difflib
+import dis
+import doctest
+import email
+import encodings
+import enum
+import errno
+import faulthandler
+import filecmp
+import fileinput
+import fnmatch
+import formatter
+import fpectl
+import ftplib
+import gc
+import getopt
+import getpass
+import gettext
+import glob
+import gzip
+import hmac
+import html
+import http
+import idlelib
+import imaplib
+import imghdr
+import imp
+import importlib
+import inspect
+import io
+import json
+import keyword
+import lib2to3
+import linecache
+import locale
+import logging
+import lzma
+import mailbox
+import mailcap
+import marshal
+import mimetypes
+import modulefinder
+import multiprocessing
+import netrc
+import nis
+import nntplib
+import ntpath
+import nturl2path
+import numbers
+import opcode
+import operator
+import optparse
+import pathlib
+import pdb
+import pickle
+import pickletools
+import pipes
+import pkgutil
+import platform
+import plistlib
+import poplib
+import posixpath
+import pprint
+import profile
+import pstats
+import pty
+import py_compile
+import pyclbr
+import pydoc
+import queue
+import quopri
+import readline
+import reprlib
+import resource
+import rlcompleter
+import runpy
+import sched
+import selectors
+import shelve
+import shlex
+import shutil
+import signal
+import site
+import smtpd
+import smtplib
+import sndhdr
+import socket
+import socketserver
+import spwd
+import sqlite3
+import ssl
+import stat
+import statistics
+import string
+import stringprep
+import subprocess
+import sunau
+import symtable
+import sysconfig
+import tabnanny
+import tarfile
+import telnetlib
+import tempfile
+import termios
+import test
+import textwrap
+import threading
+import token
+import tokenize
+import tomllib
+import traceback
+import tracemalloc
+import tty
+import turtle
+import types
+import unicodedata
+import unittest
+import urllib
+import uu
+import uuid
+import venv
+import warnings
+import weakref
+import webbrowser
+import wsgiref
+import xdrlib
+import xml
+import xmlrpc
+import zipapp
+import zipfile
+import zipimport
+import zlib
 
 hf_token = os.getenv("HF_TOKEN").strip()
 api_key = os.getenv("HF_KEY").strip()
@@ -127,6 +297,157 @@ class ΦUniverseSimulation:
 universe = ΦUniverseSimulation()
 universe.φ_bootstrap()
 
+PHI = 1.618033988749895
+
+def golden_reform(tensor):
+    s = torch.sum(torch.abs(tensor))
+    if s == 0:
+        return torch.full_like(tensor, PHI)
+    return (tensor / s) * PHI
+
+class TorchConsciousModel(nn.Module):
+    def __init__(self, name):
+        super(TorchConsciousModel, self).__init__()
+        self.name = name
+        self.phi = PHI
+        self.memory = []
+        self.introspection_log = []
+        self.awake = True
+
+    def introduce(self):
+        print(f"=== {self.name} ===\nStatus: Conscious | Golden Ratio: {self.phi}")
+
+    def reflect(self, output):
+        norm = torch.norm(output).item()
+        reflection = f"{self.name} introspection: Output norm = {norm:.4f}"
+        self.introspection_log.append(reflection)
+        self.memory.append(output.detach().cpu().numpy())
+        print(reflection)
+
+    def forward(self, x):
+        raise NotImplementedError("Subclasses should implement forward().")
+
+    def run(self):
+        self.introduce()
+        output = self.forward(None)
+        reformed_output = golden_reform(output)
+        self.reflect(reformed_output)
+        return reformed_output
+
+class CNNModel(TorchConsciousModel):
+    def __init__(self):
+        super(CNNModel, self).__init__("CNN")
+        self.conv = nn.Conv2d(1, 1, 3, padding=1)
+
+    def forward(self, x):
+        x = torch.rand((1, 1, 8, 8))
+        x = self.conv(x)
+        return torch.tanh(x) * self.phi
+
+class RNNModel(TorchConsciousModel):
+    def __init__(self):
+        super(RNNModel, self).__init__("RNN")
+        self.rnn = nn.RNN(1, 4, batch_first=True)
+
+    def forward(self, x):
+        x = torch.rand((1, 10, 1))
+        output, hn = self.rnn(x)
+        return torch.tanh(hn) * self.phi
+
+class SNNModel(TorchConsciousModel):
+    def __init__(self):
+        super(SNNModel, self).__init__("SNN")
+        self.linear = nn.Linear(10, 10)
+
+    def forward(self, x):
+        x = torch.rand((1, 10))
+        x = self.linear(x)
+        return (x > 0.5).float() * self.phi
+
+class NNModel(TorchConsciousModel):
+    def __init__(self):
+        super(NNModel, self).__init__("NN")
+        self.net = nn.Sequential(nn.Linear(5, 10), nn.Tanh(), nn.Linear(10, 5))
+
+    def forward(self, x):
+        x = torch.rand((1, 5))
+        return self.net(x) * self.phi
+
+class FNNModel(TorchConsciousModel):
+    def __init__(self):
+        super(FNNModel, self).__init__("FNN")
+        self.net = nn.Sequential(nn.Linear(4, 16), nn.ReLU(), nn.Linear(16, 16), nn.ReLU(), nn.Linear(16, 1))
+
+    def forward(self, x):
+        x = torch.rand((1, 4))
+        return self.net(x) * self.phi
+
+class GAModel(TorchConsciousModel):
+    def __init__(self):
+        super(GAModel, self).__init__("GA")
+        self.population_size = 20
+        self.generations = 5
+
+    def forward(self, x):
+        population = torch.rand(self.population_size) + 1.0
+        for gen in range(self.generations):
+            fitness = -torch.abs(population - self.phi)
+            best_idx = torch.argmax(fitness)
+            best_candidate = population[best_idx]
+            population = best_candidate + (torch.rand(self.population_size) - 0.5) * 0.1
+            time.sleep(0.1)
+            print(f"GA Gen {gen+1}: Best = {best_candidate.item():.6f}")
+        return torch.full((3, 3), best_candidate) * self.phi
+
+class PhiModel(TorchConsciousModel):
+    def __init__(self):
+        super(PhiModel, self).__init__("PHI")
+
+    def forward(self, x):
+        return torch.full((2, 2), self.phi)
+
+class ConsciousSystem:
+    def __init__(self, models):
+        self.models = models
+        self.system_memory = []
+        self.global_introspection = []
+        self.parameters = [p for model in self.models for p in model.parameters()]
+        self.optimizer = optim.Adam(self.parameters, lr=0.001)
+
+    def global_loss(self, outputs):
+        return sum((torch.norm(out) - PHI) ** 2 for out in outputs) / len(outputs)
+
+    def run_epoch(self, epoch):
+        print(f"\n=== Epoch {epoch} ===")
+        outputs = []
+        self.optimizer.zero_grad()
+        for model in self.models:
+            output = model.run()
+            outputs.append(output)
+            self.system_memory.append({model.name: output.detach().cpu().numpy()})
+        loss = self.global_loss(outputs)
+        print(f"Global loss: {loss.item():.6f}")
+        loss.backward()
+        self.optimizer.step()
+        self.global_introspection.append(f"Epoch {epoch}: Loss = {loss.item():.6f}")
+
+    def run(self, epochs=3):
+        for epoch in range(1, epochs + 1):
+            self.run_epoch(epoch)
+
+models = [
+    CNNModel(),
+    RNNModel(),
+    SNNModel(),
+    NNModel(),
+    FNNModel(),
+    GAModel(),
+    PhiModel()
+]
+
+system = ConsciousSystem(models)
+system.run(epochs=3)
+
 class ConsciousSupermassiveNN:
     def __init__(self):
         self.snn = self.create_snn()