import logging import tensorflow as tf from transformers import AutoModelForCausalLM, AutoTokenizer from tensorflow.keras.models import Model from tensorflow.keras import backend as K from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint import numpy as np from typing import Optional, List, Tuple, Dict, Callable, Any from functools import lru_cache import asyncio import time import nest_asyncio # Allow nested async loops

Set up logging

logging.basicConfig( filename='app.log', level=logging.INFO, format='%(asctime)s:%(levelname)s:%(message)s' )

Apply the workaround for nested event loops

nest_asyncio.apply()

class ModelInitializationError(Exception): """Raised when model initialization fails."""

def log_execution_time(func: Callable) -> Callable: """ A decorator that logs the execution time of the function.

:param func: The function to be wrapped.
:return: Wrapped function with execution time logging.
"""
async def wrapper(*args, **kwargs):
    start_time = time.time()
    result = await func(*args, **kwargs)
    end_time = time.time()
    logging.info(f"Executed {func.__name__} in {end_time - start_time:.2f} seconds.")
    return result
return wrapper

class AGISystemSTEM: def init(self, model_loader: Optional[Callable] = None) -> None: """ Initializes the AGI system.

    :param model_loader: Optional function for loading models, useful for dependency injection.
    """
    self.memory: Dict[str, Any] = {}
    self.models: Dict[str, Any] = {}
    self.device = self._detect_device()
    self.model_loader = model_loader if model_loader else self._default_model_loader
    self.model_cache = {}

@lru_cache(maxsize=5)
@log_execution_time
async def load_model(self, model_name: str, model_class: str) -> None:
    """
    Asynchronously loads a model.

    :param model_name: Name of the model to load.
    :param model_class: Class of the model (e.g., nlp_v1).
    """
    if model_class in self.models:
        logging.info(f"{model_class} already loaded.")
        return
    try:
        start_time = time.time()
        self.models[model_class] = await asyncio.to_thread(
            self.model_loader, model_name, model_class
        )
        logging.info(f"{model_class} loaded successfully in {time.time() - start_time:.2f} seconds.")
    except ModelInitializationError as e:
        logging.error(f"Model load failed: {str(e)}")
    except Exception as e:
        logging.error(f"Unexpected error while loading model: {str(e)}")

def _create_dense_model(
    self, input_shape: Tuple[int], model_name: str, num_classes: int
) -> Optional[Model]:
    """
    Creates a dense neural network model.

    :param input_shape: Input shape of the model.
    :param model_name: Name of the model.
    :param num_classes: Number of classes for the model.
    :return: The created model or None if creation fails.
    """
    try:
        with tf.device(self.device):
            model = tf.keras.Sequential(
                [
                    tf.keras.layers.Dense(128, activation="relu", input_shape=input_shape),
                    tf.keras.layers.Dense(64, activation="relu"),
                    tf.keras.layers.Dense(num_classes, activation="softmax" if num_classes > 1 else "sigmoid"),
                ]
            )
            loss = "categorical_crossentropy" if num_classes > 1 else "binary_crossentropy"
            model.compile(
                optimizer="adam",
                loss=loss,
                metrics=["accuracy", tf.keras.metrics.Precision(), tf.keras.metrics.Recall()],
            )
            logging.info(f"{model_name} model created successfully.")
            return model
    except tf.errors.ResourceExhaustedError:
        logging.error(f"Model creation failed due to insufficient memory on {self.device}.")
        return None
    except Exception as e:
        logging.error(f"Error creating {model_name} model: {str(e)}")
        return None

def _detect_device(self) -> str:
    """Detects the available device (GPU or CPU)."""
    return "/GPU:0" if tf.config.list_physical_devices('GPU') else "/CPU:0"

def _default_model_loader(self, model_name: str, model_class: str) -> Any:
    """Default model loading function."""
    try:
        if model_class == "nlp_v1":
            tokenizer = AutoTokenizer.from_pretrained(model_name)
            tokenizer.pad_token = tokenizer.eos_token  # Add pad token
            model = AutoModelForCausalLM.from_pretrained(model_name)
            return {"tokenizer": tokenizer, "model": model}
        else:
            raise ModelInitializationError(f"Unknown model class: {model_class}")
    except Exception as e:
        logging.error(f"Error loading model {model_name}: {str(e)}")
        raise ModelInitializationError(f"Failed to load model {model_name}")

def save_model(self, model: Model, model_name: str) -> None:
    """
    Saves a TensorFlow model.

    :param model: The Keras model to save.
    :param model_name: The file name for saving the model.
    """
    try:
        model.save(f'{model_name}.h5')
        logging.info(f"Model {model_name} saved successfully.")
    except Exception as e:
        logging.error(f"Failed to save model {model_name}: {str(e)}")

def load_trained_model(self, model_name: str) -> Optional[Model]:
    """
    Loads a previously saved TensorFlow model.

    :param model_name: The name of the model file to load.
    :return: The loaded model.
    """
    try:
        model = tf.keras.models.load_model(f'{model_name}.h5')
        logging.info(f"Model {model_name} loaded successfully.")
        return model
    except Exception as e:
        logging.error(f"Failed to load model {model_name}: {str(e)}")
        return None

Example usage

async def main(): agi_system = AGISystemSTEM() await agi_system.load_model("Salesforce/codegen-350M-multi", "nlp_v1")

Run the example

if name == "main": asyncio.run(main())

from transformers import pipeline

Load a pre-trained model (can be fine-tuned for STEM-specific tasks)

nlp_model = pipeline("question-answering", model="deepset/roberta-base-squad2")

context = """ Newton's second law of motion states that the acceleration of an object is dependent upon two variables - the net force acting upon the object and the mass of the object. """ question = "What does Newton's second law of motion state?"

result = nlp_model(question=question, context=context) print(result)

from z3 import *

Define variables

x = Int('x') y = Int('y')

Create a solver

solver = Solver()

Add some constraints

solver.add(x + y > 5) solver.add(x - y < 2)

Check if solution exists

if solver.check() == sat: print("Solution found:") print(solver.model()) else: print("No solution.")

import sympy as sp

Define symbolic variables

x, y = sp.symbols('x y')

Define a function

f = x**2 + 2*y

Compute its derivative w.r.t. x

df_dx = sp.diff(f, x) print(f"Derivative with respect to x: {df_dx}")

Solve an equation

equation = sp.Eq(f, 0) solutions = sp.solve(equation, x) print(f"Solutions: {solutions}")

import torch import torch.nn as nn import torch.optim as optim

Define a simple feedforward neural network

class SimpleNN(nn.Module): def init(self): super(SimpleNN, self).init() self.fc1 = nn.Linear(10, 50) self.fc2 = nn.Linear(50, 10) self.fc3 = nn.Linear(10, 1)

def forward(self, x):
    x = torch.relu(self.fc1(x))
    x = torch.relu(self.fc2(x))
    x = self.fc3(x)
    return x

Initialize the network, loss function, and optimizer

net = SimpleNN() criterion = nn.MSELoss() optimizer = optim.Adam(net.parameters(), lr=0.001)

Dummy input and target

input_data = torch.randn(10) target = torch.tensor([1.0])

Training step

optimizer.zero_grad() output = net(input_data) loss = criterion(output, target) loss.backward() optimizer.step()

print(f"Loss: {loss.item()}")

import networkx as nx

Create a graph to represent knowledge

G = nx.Graph()

Add nodes (concepts) and edges (relationships)

G.add_node("Force", category="Physics concept") G.add_node("Newton's Second Law", category="Law") G.add_edge("Force", "Newton's Second Law", relationship="Defined by")

Visualize the graph structure

print(G.nodes(data=True)) print(G.edges(data=True))

import pybullet as p import time

Connect to PyBullet

physicsClient = p.connect(p.GUI)

Load a plane and a cube

p.loadURDF("plane.urdf") cubeId = p.loadURDF("r2d2.urdf", [0, 0, 1])

Run simulation

for i in range(1000): p.stepSimulation() time.sleep(1./240.)

Disconnect

p.disconnect()

pip install transformers tensorflow numpy loguru nest_asyncio z3-solver pybullet