import cagi import ai_expander import discord from discord.ext import commands import gym import numpy as np from keras.models import load_model from keras.applications import MobileNetV2 from keras.applications.mobilenet_v2 import preprocess_input, decode_predictions from keras.preprocessing import image from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier import onnx import onnxruntime from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer import openai from keras.models import load_model from keras import Sequential

Convert Keras Model to ONNX

keras_model = load_model('path_to_your_keras_model.h5') onnx_model = onnx.load('keras_model.onnx') onnx.save_model(onnx_model, 'keras_model.onnx')

Convert Sklearn Model to ONNX

sklearn_model = joblib.load('path_to_your_sklearn_model.pkl') onnx_model = onnxmltools.convert_sklearn(sklearn_model) onnxmltools.utils.save_model(onnx_model, 'sklearn_model.onnx')

onnx_model_path = 'path_to_your_model.onnx' sess = onnxruntime.InferenceSession(onnx_model_path)

openai.api_key = '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'

model = MobileNetV2(weights='imagenet')

def agi(): def train_q_learning():

Define your Q-learning parameters

    alpha = 0.1  # Learning rate
    gamma = 0.9  # Discount factor

    # Initialize Q-values (you might want to do this based on your specific state and action spaces)
    Q = {}

    for episode in range(num_episodes):
        # Initialize the environment and state
        state = env.reset()

        done = False
        while not done:
            # Choose an action (you might want to implement an exploration strategy here)
            action = q_learning_agent(state)

            # Take the chosen action and observe the next state and reward
            next_state, reward, done, _ = env.step(action)

            # Update Q-value
            Q[(state, action)] = (1 - alpha) * Q.get((state, action), 0) + alpha * (reward + gamma * max(Q.get((next_state, a), 0) for a in possible_actions))

            # Transition to the next state
            state = next_state

    return Q

def agi(): intents = discord.Intents.default() intents.message_content = True

bot = commands.BATMANAI(command_prefix="!", intents=intents)

def interpret_acronym(acronym, acronym_dict): return acronym_dict.get(acronym.upper(), "Acronym not found in the dictionary.")

def interact_with_gym_environment(): env = gym.make('CartPole-v1') obs = env.reset()

for _ in range(1000):
    env.render()
    # Assuming q_learning_agent is your Q-learning agent
    action = q_learning_agent(obs)
    obs, reward, done, _ = env.step(action)

    if done:
        obs = env.reset()

env.close()

acronym_dict = { "AI": "Artificial Intelligence", "ML": "Machine Learning", "DL": "Deep Learning", "NLP": "Natural Language Processing", "API": "Application Programming Interface", "CAGI": "Comprehensive Artificial General Intelligence" }

Assuming you have trained a Q-learning agent

def train_q_learning():

Define your Q-learning parameters and train the agent

# ...
return q_learning_agent

Train the Q-learning agent

q_learning_agent = train_q_learning()

async def chat_with_bot(message): if message.content.lower() == 'hello': await message.channel.send("Hello. Need my help on a Mission?") elif message.content.lower() == 'goodbye': await message.channel.send("Goodbye. Have a good day!") else: await message.channel.send("I'll try to come up with a better response. Try asking me about an acronym.")

@bot.event async def on_ready(): print(f'Logged in as {bot.user.name}')