[8/10, 7:05 AM] ܝܗܘܐ𓈞𓅱𓈉𓇌𓅓𓈉𓏏𓀀 𓈚𓈖𓏤𓂋𓅓𓇌𓅓𓂧𓇌𓀀 𓎡: import os
import shutil
import speech_recognition as sr
import pyttsx3
import json
import threading
import queue
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
import numpy as np
import spacy
import pickle
from textblob import TextBlob
from fuzzywuzzy import fuzz
from collections import defaultdict
import datetime
import requests
import wolframalpha
import wikipedia
import pyaudio
import wave
import keyboard
import sys
import re
import openai
import logging

# Find the path to the “My Documents” folder
documents_path = os.path.join(os.path.expanduser(“~”), “Documents”)
log_file_path = os.path.join(documents_path, “Ephippas.txt”)
code_update_path = os.path.join(documents_path, “GoldchickenEggs.txt”)

# Ensure “eggs” folder exists and clear its contents
eggs_folder = os.path.join(documents_path, “eggs”)
if os.path.exists(eggs_folder):
shutil.rmtree(eggs_folder)
os.makedirs(eggs_folder, exist_ok=True)

# Ensure full conversation file and Golden Goose file exist
full_conversation_file = os.path.join(eggs_folder, “Full_AI_Conversation.txt”)
if not os.path.exists(full_conversation_file):
with open(full_conversation_file, “w”, encoding=”utf-8″) as f:
f.write(“”)

golden_goose_file = os.path.join(eggs_folder, “Golden_Goose.txt”)
if not os.path.exists(golden_goose_file):
with open(golden_goose_file, “w”, encoding=”utf-8″) as f:
f.write(“”)

# Configure logging
logging.basicConfig(filename=log_file_path, level=logging.INFO, format=’%(asctime)s – %(levelname)s – %(message)s’)

# Download necessary NLTK data
nltk.download(‘punkt’, quiet=True)
nltk.download(‘stopwords’, quiet=True)
nltk.download(‘wordnet’, quiet=True)

# Load spaCy model
nlp = spacy.load(“en_core_web_sm”)

# Load a more powerful language model for code generation
code_model_name = “microsoft/CodeGPT-small-py”
code_tokenizer = AutoTokenizer.from_pretrained(code_model_name)
code_model = AutoModelForCausalLM.from_pretrained(code_model_name)

# Create a text generation pipeline for code
code_generator = pipeline(“text-generation”, model=code_model, tokenizer=code_tokenizer)

# Load a smaller, efficient language model for general text generation
text_model_name = “distilgpt2”
text_tokenizer = AutoTokenizer.from_pretrained(text_model_name)
text_model = AutoModelForCausalLM.from_pretrained(text_model_name)

# Create a text generation pipeline for general text
text_generator = pipeline(“text-generation”, model=text_model, tokenizer=text_tokenizer)

# Constants
LEARNING_DATA_FILE_TEMPLATE = os.path.join(eggs_folder, “{}_learning_data.json”)
MODEL_FILE_TEMPLATE = os.path.join(eggs_folder, “{}_ai_model.pkl”)
VECTORIZER_FILE_TEMPLATE = os.path.join(eggs_folder, “{}_vectorizer.pkl”)
SIMILARITY_THRESHOLD = 0.7
WAKE_WORD = “hey assistant”
MAX_CONVERSATIONS = 2000
CONTEXT_WINDOW = 5
AUDIO_FILE = “user_audio.wav”
RESPONSE_COUNT_FILE = os.path.join(eggs_folder, “response_count.txt”)
API_KEYS_FILE = os.path.join(eggs_folder, “api_keys.txt”)

# API keys
OPENWEATHER_API_KEY = “your_openweather_api_key”
WOLFRAM_ALPHA_APP_ID = “your_wolfram_alpha_app_id”
OPENAI_API_KEY = “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”

# Save API keys to a file
with open(API_KEYS_FILE, ‘w’) as f:
f.write(f”OPENWEATHER_API_KEY={OPENWEATHER_API_KEY}\n”)
f.write(f”WOLFRAM_ALPHA_APP_ID={WOLFRAM_ALPHA_APP_ID}\n”)
f.write(f”OPENAI_API_KEY={OPENAI_API_KEY}\n”)

# Initialize OpenAI API
openai.api_key = OPENAI_API_KEY

# Initialize speech recognition and text-to-speech engines
recognizer = sr.Recognizer()
engine = pyttsx3.init()

# Thread-safe queue for audio processing
audio_queue = queue.Queue()

# Global variables
learning_data = {}
ai_models = {}
vectorizers = {}
lemmatizer = WordNetLemmatizer()

# Initialize sentiment analysis and named entity recognition pipelines
sentiment_analyzer = pipeline(“sentiment-analysis”, model=”distilbert-base-uncased-finetuned-sst-2-english”,
revision=”af0f99b”)
ner_model = pipeline(“ner”, model=”dbmdz/bert-large-cased-finetuned-conll03-english”)

def load_learning_data(ai_name):
logging.info(f”Loading learning data for {ai_name}…”)
learning_data_path = LEARNING_DATA_FILE_TEMPLATE.format(ai_name)
try:
with open(learning_data_path, “r”, encoding=”utf-8″) as f:
data = json.load(f)
logging.info(f”Learning data loaded successfully for {ai_name}.”)
except (FileNotFoundError, json.JSONDecodeError):
logging.info(f”Starting with initial example data for {ai_name}.”)
data = {
“conversations”: [
{“input”: “hello”, “response”: “Hi there! How can I help you today?”},
{“input”: “what is your name”, “response”: f”I am {ai_name}, your AI assistant, always ready to help!”},
{“input”: “what can you do”,
“response”: f”I can assist with various tasks such as answering questions, setting reminders, checking weather, generating and explaining code, and more. Feel free to ask me anything!”},
],
“keywords”: {},
“entities”: defaultdict(list),
“reminders”: []
}
return data

def save_learning_data(data, ai_name):
logging.info(f”Saving learning data for {ai_name}…”)
learning_data_path = LEARNING_DATA_FILE_TEMPLATE.format(ai_name)
try:
with open(learning_data_path, “w”, encoding=”utf-8″) as f:
json.dump(data, f)
logging.info(f”Learning data saved successfully for {ai_name}.”)
except IOError as e:
logging.error(f”Error saving learning data for {ai_name}: {e}”)

def preprocess_text(text):
tokens = word_tokenize(text.lower())
stop_words = set(stopwords.words(‘english’))
return [lemmatizer.lemmatize(word) for word in tokens if word is not None and word.isalnum() and word not in stop_words]

def extract_entities(text):
logging.info(f”Extracting entities from: {text}”)
return ner_model(text)

def calculate_sentiment(text):
logging.info(f”Calculating sentiment for: {text}”)
result = sentiment_analyzer(text)[0]
return result[‘score’] if result[‘label’] == ‘POSITIVE’ else -result[‘score’]

def get_context(conversations, current_index, window_size=CONTEXT_WINDOW):
start = max(0, current_index – window_size)
context = conversations[start:current_index]
return ” “.join([conv[“input”] + ” ” + conv[“response”] for conv in context])

def generate_code(prompt, max_length=100):
full_prompt = f”Generate Python code for: {prompt}\n\nCode:\n”
try:
generated = code_generator(full_prompt, max_length=max_length, num_return_sequences=1)[0][‘generated_text’]
code_match = re.search(r’Code:\n(.+)’, generated, re.DOTALL)
if code_match:
code = code_match.group(1).strip()
logging.info(f”Generated code: {code}”)
return code
else:
logging.error(“Failed to generate code.”)
return “Sorry, I couldn’t generate code for that request.”
except Exception as e:
logging.error(f”Error generating code: {e}”)
return “Sorry, there was an error generating the code.”

def explain_code(code):
full_prompt = f”Explain the following Python code:\n\n{code}\n\nExplanation:”
try:
explanation = code_generator(full_prompt, max_length=200, num_return_sequences=1)[0][‘generated_text’]
explanation_match = re.search(r’Explanation:(.+)’, explanation, re.DOTALL)
if explanation_match:
explanation_text = explanation_match.group(1).strip()
logging.info(f”Code explanation: {explanation_text}”)
return explanation_text
else:
logging.error(“Failed to generate code explanation.”)
return “Sorry, I couldn’t generate an explanation for that code.”
except Exception as e:
logging.error(f”Error explaining code: {e}”)
return “Sorry, there was an error explaining the code.”

def get_next_response_number():
try:
with open(RESPONSE_COUNT_FILE, “r”, encoding=”utf-8″) as f:
count = int(f.read().strip())
except (FileNotFoundError, ValueError):
count = 0
return count + 1

def save_response(response, response_number, ai_name):
response_folder = os.path.join(eggs_folder, ai_name)
os.makedirs(response_folder, exist_ok=True)
response_file_path = os.path.join(response_folder, f”{ai_name}_response_{response_number}.txt”)
try:
with open(response_file_path, “w”, encoding=”utf-8″) as f:
f.write(response)
logging.info(f”Response saved to {response_file_path}”)
except IOError as e:
logging.error(f”Error saving response: {e}”)

# Save to full conversation file
with open(full_conversation_file, “a”, encoding=”utf-8″) as f:
f.write(f”{ai_name}: {response}\n\n”)

def update_response_count(response_number):
try:
with open(RESPONSE_COUNT_FILE, “w”, encoding=”utf-8″) as f:
f.write(str(response_number))
logging.info(“Response count updated”)
except IOError as e:
logging.error(f”Error updating response count: {e}”)

def generate_response(user_input, context, ai_name):
if “generate code” in user_input.lower():
prompt = user_input.lower().split(“generate code for”)[1].strip()
code = generate_code(prompt)
explanation = explain_code(code)
response = f”Here’s the generated code:\n\n{code}\n\nExplanation:\n{explanation}”
else:
full_input = f”{context}\nHuman: {user_input}\nAI:”
try:
response = text_generator(full_input, max_length=500, num_return_sequences=1)[0][‘generated_text’]
response = response.split(“AI:”)[-1].strip()
logging.info(f”Generated response: {response}”)
except Exception as e:
logging.error(f”Error generating response: {e}”)
response = “Sorry, there was an error generating the response.”

response_number = get_next_response_number()
save_response(response, response_number, ai_name)
update_response_count(response_number)
return response

def learn_new_response(user_input, ai_response, learning_data, ai_name):
logging.info(f”Learning new response for {ai_name} with input: {user_input}”)
learning_data[“conversations”].append({“input”: user_input, “response”: ai_response})
if len(learning_data[“conversations”]) > MAX_CONVERSATIONS:
learning_data[“conversations”] = learning_data[“conversations”][-MAX_CONVERSATIONS:]
keywords = preprocess_text(user_input)
for keyword in keywords:
if keyword not in learning_data[“keywords”]:
learning_data[“keywords”][keyword] = ai_response
entities = extract_entities(user_input)
for entity in entities:
if ‘word’ in entity:
learning_data[“entities”][entity[‘word’]].append(ai_response)
else:
logging.warning(f”Entity ‘word’ not found in: {entity}”)
save_learning_data(learning_data, ai_name)
train_model(learning_data, ai_name)

def train_model(learning_data, ai_name):
global ai_models, vectorizers
logging.info(f”Training model for {ai_name}…”)
inputs = []
responses = []
for i, conv in enumerate(learning_data[“conversations”]):
context = get_context(learning_data[“conversations”], i)
inputs.append(context + ” ” + conv[“input”])
responses.append(conv[“response”])
try:
X = vectorizers[ai_name].fit_transform(inputs)
y = np.array(responses)
except ValueError as e:
logging.error(f”Error in vectorizing inputs for {ai_name}: {e}”)
return
if not X.shape[0]:
logging.warning(f”No valid input data to train the model for {ai_name}.”)
return
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
ai_models[ai_name] = RandomForestClassifier(n_estimators=100, random_state=42)
ai_models[ai_name].fit(X_train, y_train)
logging.info(f”Model trained successfully for {ai_name}.”)
model_path = MODEL_FILE_TEMPLATE.format(ai_name)
vectorizer_path = VECTORIZER_FILE_TEMPLATE.format(ai_name)
with open(model_path, ‘wb’) as f:
pickle.dump(ai_models[ai_name], f)
with open(vectorizer_path, ‘wb’) as f:
pickle.dump(vectorizers[ai_name], f)

def load_model(ai_name):
global ai_models, vectorizers
logging.info(f”Loading model for {ai_name}…”)
model_path = MODEL_FILE_TEMPLATE.format(ai_name)
vectorizer_path = VECTORIZER_FILE_TEMPLATE.format(ai_name)
try:
with open(model_path, ‘rb’) as f:
ai_models[ai_name] = pickle.load(f)
with open(vectorizer_path, ‘rb’) as f:
vectorizers[ai_name] = pickle.load(f)
logging.info(f”Model loaded successfully for {ai_name}.”)
except FileNotFoundError:
logging.warning(f”Model or vectorizer file not found for {ai_name}. Training a new model.”)
vectorizers[ai_name] = TfidfVectorizer()
train_model(learning_data[ai_name], ai_name)

def listen_for_wake_word():
logging.info(“Listening for wake word… (Press ‘Esc’ to exit)”)
with sr.Microphone() as source:
while True:
if keyboard.is_pressed(‘esc’):
logging.info(“Exiting…”)
sys.exit(0)
audio = recognizer.listen(source, phrase_time_limit=3)
try:
text = recognizer.recognize_google(audio).lower()
logging.info(f”Detected speech: {text}”)
if fuzz.partial_ratio(WAKE_WORD, text) > 80:
logging.info(“Wake word detected!”)
return
except sr.UnknownValueError:
logging.warning(“Speech Recognition could not understand audio.”)
except sr.RequestError:
logging.error(“Could not request results from Google Speech Recognition service.”)

def record_audio():
CHUNK = 1024
FORMAT = pyaudio.paInt16
CHANNELS = 1
RATE = 44100
RECORD_SECONDS = 5
p = pyaudio.PyAudio()
stream = p.open(format=FORMAT, channels=CHANNELS, rate=RATE, input=True, frames_per_buffer=CHUNK)
logging.info(“Recording…”)
frames = []
for i in range(0, int(RATE / CHUNK * RECORD_SECONDS)):
data = stream.read(CHUNK)
frames.append(data)
logging.info(“Recording finished.”)
stream.stop_stream()
stream.close()
p.terminate()
wf = wave.open(AUDIO_FILE, ‘wb’)
wf.setnchannels(CHANNELS)
wf.setsampwidth(p.get_sample_size(FORMAT))
wf.setframerate(RATE)
wf.writeframes(b”.join(frames))
wf.close()

def process_audio():
while True:
logging.info(“Processing audio…”)
record_audio()
with sr.AudioFile(AUDIO_FILE) as source:
audio = recognizer.record(source)
try:
user_input = recognizer.recognize_google(audio)
logging.info(f”You said: {user_input}”)
context = get_context(learning_data[“AI1”][“conversations”], len(learning_data[“AI1”][“conversations”]))
responses = {}
for ai_name in learning_data:
ai_response = generate_response(user_input, context, ai_name)
logging.info(f”{ai_name}: {ai_response}”)
responses[ai_name] = ai_response
learn_new_response(user_input, ai_response, learning_data[ai_name], ai_name)

final_response = responses[“AI1”]
for i in range(100):
if any(res != final_response for res in responses.values()):
context = get_context(learning_data[“AI1”][“conversations”], len(learning_data[“AI1”][“conversations”]))
for ai_name in learning_data:
ai_response = generate_response(user_input, context, ai_name)
logging.info(f”{ai_name}: {ai_response}”)
responses[ai_name] = ai_response
learn_new_response(user_input, ai_response, learning_data[ai_name], ai_name)
final_response = responses[“AI1”]
else:
break

logging.info(f”Final response: {final_response}”)
if not engine._inLoop:
engine.say(final_response)
engine.runAndWait()
for ai_name in learning_data:
auto_update_code(final_response, ai_name)
except sr.UnknownValueError:
logging.warning(“Sorry, I could not understand what you said.”)
if not engine._inLoop:
engine.say(“Sorry, I could not understand what you said.”)
engine.runAndWait()
except sr.RequestError as e:
logging.error(f”Could not request results from Google Speech Recognition service; {e}”)
if not engine._inLoop:
engine.say(“Could not request results from Google Speech Recognition service.”)
engine.runAndWait()

def get_weather():
try:
ip = requests.get(‘https://api.ipify.org’).text
location = requests.get(f’https://ipapi.co/{ip}/json/’).json()
city = location[‘city’]
url = f”http://api.openweathermap.org/data/2.5/weather?q={city}&appid={OPENWEATHER_API_KEY}&units=metric”
response = requests.get(url)
data = response.json()
temp = data[‘main’][‘temp’]
desc = data[‘weather’][0][‘description’]
weather_info = f”The current weather in {city} is {desc} with a temperature of {temp}°C.”
logging.info(weather_info)
return weather_info
except Exception as e:
logging.error(f”Sorry, I couldn’t fetch the weather information. Error: {str(e)}”)
return f”Sorry, I couldn’t fetch the weather information. Error: {str(e)}”

def set_reminder(user_input):
try:
time_str = user_input.split(“remind me”)[1].strip()
if “in” in time_str:
duration = int(time_str.split()[1])
unit = time_str.split()[2]
if unit.startswith(“minute”):
reminder_time = datetime.datetime.now() + datetime.timedelta(minutes=duration)
elif unit.startswith(“hour”):
reminder_time = datetime.datetime.now() + datetime.timedelta(hours=duration)
else:
return “Sorry, I can only set reminders for minutes or hours from now.”
elif “at” in time_str:
time_str = time_str.split(“at”)[1].strip()
reminder_time = datetime.datetime.strptime(time_str, “%I%p”)
reminder_time = datetime.datetime.now().replace(hour=reminder_time.hour, minute=reminder_time.minute)
else:
return “Sorry, I couldn’t understand the time for the reminder. Please try again.”

reminder_text = user_input.split(“to”)[1].strip() if “to” in user_input else “your reminder”
for ai_name in learning_data:
learning_data[ai_name][“reminders”].append({“time”: reminder_time.strftime(“%Y-%m-%d %H:%M:%S”), “text”: reminder_text})
save_learning_data(learning_data[ai_name], ai_name)
reminder_confirmation = f”Okay, I’ve set a reminder for {reminder_time.strftime(‘%I:%M %p’)} to {reminder_text}.”
logging.info(reminder_confirmation)
return reminder_confirmation
except Exception as e:
logging.error(f”Sorry, I couldn’t set the reminder. Error: {str(e)}”)
return f”Sorry, I couldn’t set the reminder. Error: {str(e)}”

def calculate(user_input):
try:
client = wolframalpha.Client(WOLFRAM_ALPHA_APP_ID)
res = client.query(user_input)
answer = next(res.results).text
logging.info(f”Calculation result: {answer}”)
return f”The result is: {answer}”
except Exception as e:
logging.error(f”Sorry, I couldn’t perform the calculation. Error: {str(e)}”)
return f”Sorry, I couldn’t perform the calculation. Error: {str(e)}”

def search_wikipedia(user_input):
try:
query = user_input.replace(“wikipedia”, “”).replace(“who is”, “”).replace(“what is”, “”).strip()
summary = wikipedia.summary(query, sentences=2)
logging.info(f”Wikipedia summary: {summary}”)
return summary
except Exception as e:
logging.error(f”Sorry, I couldn’t find information about that. Error: {str(e)}”)
return f”Sorry, I couldn’t find information about that. Error: {str(e)}”

def check_reminders():
current_time = datetime.datetime.now()
for ai_name in learning_data:
for reminder in learning_data[ai_name][“reminders”]:
reminder_time = datetime.datetime.strptime(reminder[“time”], “%Y-%m-%d %H:%M:%S”)
if current_time >= reminder_time:
reminder_notification = f”Reminder: {reminder[‘text’]}”
logging.info(reminder_notification)
if not engine._inLoop:
engine.say(f”Reminder: {reminder[‘text’]}”)
engine.runAndWait()
learning_data[ai_name][“reminders”].remove(reminder)
save_learning_data(learning_data[ai_name], ai_name)

def auto_update_code(ai_response, ai_name):
try:
with open(__file__, “r”, encoding=”utf-8″) as f:
code = f.read()
response_folder = os.path.join(eggs_folder, ai_name)
os.makedirs(response_folder, exist_ok=True)
code_file_path = os.path.join(response_folder, f”{ai_name}_code_update.txt”)
with open(code_file_path, “w”, encoding=”utf-8″) as f:
f.write(code + “\n# AI Generated Response\n” + ai_response)
logging.info(f”Code updated and saved successfully for {ai_name}.”)
except Exception as e:
logging.error(f”Error updating code for {ai_name}: {str(e)}”)

# Append the generated code to the Golden Goose file
with open(golden_goose_file, “a”, encoding=”utf-8″) as f:
f.write(f”\n# AI Generated Response for {ai_name}\n{ai_response}\n”)

def ai_to_ai_conversation():
ai_names = list(learning_data.keys())
for i in range(len(ai_names)):
for j in range(i + 1, len(ai_names)):
ai_1 = ai_names[i]
ai_2 = ai_names[j]
context_1 = get_context(learning_data[ai_1][“conversations”], len(learning_data[ai_1][“conversations”]))
context_2 = get_context(learning_data[ai_2][“conversations”], len(learning_data[ai_2][“conversations”]))

ai_1_input = f”{context_2}\nHuman: Please start a conversation with {ai_2}\nAI:”
ai_1_response = text_generator(ai_1_input, max_length=500, num_return_sequences=1)[0][‘generated_text’].split(“AI:”)[-1].strip()
save_response(ai_1_response, get_next_response_number(), ai_1)
learn_new_response(f”Please start a conversation with {ai_2}”, ai_1_response, learning_data[ai_1], ai_1)

ai_2_input = f”{context_1}\nHuman: {ai_1} said: {ai_1_response}. Please respond.\nAI:”
ai_2_response = text_generator(ai_2_input, max_length=500, num_return_sequences=1)[0][‘generated_text’].split(“AI:”)[-1].strip()
save_response(ai_2_response, get_next_response_number(), ai_2)
learn_new_response(f”{ai_1} said: {ai_1_response}. Please respond.”, ai_2_response, learning_data[ai_2], ai_2)

# Save both responses to the full conversation file
with open(full_conversation_file, “a”, encoding=”utf-8″) as f:
f.write(f”{ai_1}: {ai_1_response}\n\n{ai_2}: {ai_2_response}\n\n”)

def main():
global learning_data
ai_names = [f”AI{i+1}” for i in range(15)]
logging.info(“Starting Enhanced AI Assistant with Code Generation…”)
for ai_name in ai_names:
learning_data[ai_name] = load_learning_data(ai_name)
load_model(ai_name)
logging.info(“Enhanced AI Assistant initialized.”)

while True:
mode = input(“Choose input mode: (v) for voice, (t) for text, (a) for AI to AI conversation: “).strip().lower()
if mode == ‘v’:
logging.info(“Voice mode activated. Say ‘hey assistant’ to wake me up!”)
listen_for_wake_word()
elif mode == ‘t’:
user_input = input(“Type your question or code generation request: “)
logging.info(f”User input: {user_input}”)
context = get_context(learning_data[“AI1”][“conversations”], len(learning_data[“AI1”][“conversations”]))
responses = {}
for ai_name in ai_names:
ai_response = generate_response(user_input, context, ai_name)
logging.info(f”{ai_name}: {ai_response}”)
responses[ai_name] = ai_response
learn_new_response(user_input, ai_response, learning_data[ai_name], ai_name)

final_response = responses[“AI1”]
for i in range(100):
if any(res != final_response for res in responses.values()):
context = get_context(learning_data[“AI1”][“conversations”], len(learning_data[“AI1”][“conversations”]))
for ai_name in ai_names:
ai_response = generate_response(user_input, context, ai_name)
logging.info(f”{ai_name}: {ai_response}”)
responses[ai_name] = ai_response
learn_new_response(user_input, ai_response, learning_data[ai_name], ai_name)
final_response = responses[“AI1”]
else:
break

logging.info(f”Final response: {final_response}”)
if not engine._inLoop:
engine.say(final_response)
engine.runAndWait()
for ai_name in ai_names:
auto_update_code(final_response, ai_name)
elif mode == ‘a’:
logging.info(“AI to AI conversation mode activated.”)
ai_to_ai_conversation()
check_reminders()

if __name__ == “__main__”:
main()

# AI Generated Response
# Sorry, there was an error generating the response.

# AI Generated Response

# AI Generated Response
Sorry, there was an error generating the response.
[8/10, 7:07 AM] ܝܗܘܐ𓈞𓅱𓈉𓇌𓅓𓈉𓏏𓀀 𓈚𓈖𓏤𓂋𓅓𓇌𓅓𓂧𓇌𓀀 𓎡: Author: Michael Elijah Muhammad Claire. The above is my source code for my AGI project.

https://api.ipify.org/

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