pi@192.20.10.9

import time
import numpy as np
from typing import List, Optional

import threading
import pandas as pd
import requests
import plotly.express as px

def generate_data() -> List[int]:
    """Generate some random data."""
    return np.random.randint(100, 10000, 1000).tolist()

def process1(data: List[int]) -> List[int]:
    """TODO: Document this function. What does it do? What are the inputs and outputs?"""
    def foo(x):
        """Find the next largest prime number."""
        while True:
            x += 1
            if all(x % i for i in range(2, x)):
                return x
    return [foo(x) for x in data]

def process2(data: List[int]) -> List[int]:
    """TODO: Document this function. What does it do? What are the inputs and outputs?"""
    def foo(x):
        """Find the next largest prime number."""
        while True:
            x += 1
            if int(np.sqrt(x)) ** 2 == x:
                return x
    return [foo(x) for x in data]

def final_process(data1: List[int], data2: List[int]) -> List[int]:
    """TODO: Document this function. What does it do? What are the inputs and outputs?"""
    return np.mean([x - y for x, y in zip(data1, data2)])

offload_url = 'http://192.168.4.74:5000' # TODO: Change this to the IP address of your server

def run(offload: Optional[str] = None) -> float:
    """Run the program, offloading the specified function(s) to the server.
    
    Args:
        offload: Which function(s) to offload to the server. Can be None, 'process1', 'process2', or 'both'.

    Returns:
        float: the final result of the program.
    """
    data = generate_data()
    if offload is None: # in this case, we run the program locally
        data1 = process1(data)
        data2 = process2(data)
    elif offload == 'process1':
        data1 = None
        def offload_process1(data):
            nonlocal data1
            url = f"{offload_url}/process1"  # URL of the process1 endpoint on the server
            data_dict = {"data": data}  # create a dictionary containing the input data
            response = requests.post(url, json=data_dict)  # send a POST request to the server with the input data
            # TODO: Send a POST request to the server with the input data
            data1 = response.json()
        thread = threading.Thread(target=offload_process1, args=(data,))
        thread.start()
        data2 = process2(data)
        thread.join()
        # Question 2: Why do we need to join the thread here?
        # Question 3: Are the processing functions executing in parallel or just concurrently? What is the difference?
        #   See this article: https://oxylabs.io/blog/concurrency-vs-parallelism
        #   ChatGPT is also good at explaining the difference between parallel and concurrent execution!
        #   Make sure to cite any sources you use to answer this question.
    elif offload == 'process2':
        # TODO: Implement this case
        data2 = None
        def offload_process2(data):
            nonlocal data2
            url = f"{offload_url}/process2"  # URL of the process1 endpoint on the server
            data_dict = {"data": data}  # create a dictionary containing the input data
            response = requests.post(url, json=data_dict)  # send a POST request to the server with the input data
            # TODO: Send a POST request to the server with the input data
            data2 = response.json()
        thread = threading.Thread(target=offload_process2, args=(data,))
        thread.start()
        data1 = process1(data)
        thread.join()
        pass
    elif offload == 'both':
        # TODO: Implement this case
        data1 = None
        data2 = None
        def offload_both(data):
            nonlocal data1
            nonlocal data2
            url = f"{offload_url}/process1"  # URL of the process1 endpoint on the server
            data_dict = {"data": data}  # create a dictionary containing the input data
            response = requests.post(url, json=data_dict)  # send a POST request to the server with the input data
            # TODO: Send a POST request to the server with the input data
            data1 = response.json()

            url2 = f"{offload_url}/process2"  # URL of the process1 endpoint on the server
            response = requests.post(url2, json=data_dict)  # send a POST request to the server with the input data
            # TODO: Send a POST request to the server with the input data
            data2 = response.json()

        thread = threading.Thread(target=offload_both, args=(data,))
        thread.start()
        thread.join()
        pass

    ans = final_process(data1, data2)
    return ans 


def main():
    modes = [None, 'process1', 'process2', 'both']

    results = []
    for mode in modes:
        execution_times = []
        for i in range(5):
            start_time = time.time()
            run(mode)
            execution_time = time.time() - start_time
            execution_times.append(execution_time)
            print(f"Mode {mode}, run {i}: {execution_time:.2f}s")
        results.append({'Mode': mode, 'Execution Time (s)': execution_times})

    df = pd.DataFrame(results)

    fig = px.bar(df, x='Mode', y='Execution Time (s)', error_y='Execution Time (s)').update_layout(
        title='Execution Time for Different Offloading Modes',
        xaxis_title='Offloading Mode',
        yaxis_title='Execution Time (s)',
    )
    fig.write_image('makespan.png')

    print(df.describe())
    print(f"Best offloading mode: {df.iloc[df['Execution Time (s)'].idxmin()]['Mode']}")
    print(f"Worst offloading mode: {df.iloc[df['Execution Time (s)'].idxmax()]['Mode']}")    


    # TODO: Run the program 5 times for each offloading mode, and record the total execution time
    #   Compute the mean and standard deviation of the execution times
    #   Hint: store the results in a pandas DataFrame, use previous labs as a reference


    # TODO: Plot makespans (total execution time) as a bar chart with error bars
    # Make sure to include a title and x and y labels


    # TODO: save plot to "makespan.png"


    # Question 4: What is the best offloading mode? Why do you think that is?
    # Question 5: What is the worst offloading mode? Why do you think that is?
    # Question 6: The processing functions in the example aren't very likely to be used in a real-world application. 
    #   What kind of processing functions would be more likely to be used in a real-world application?
    #   When would you want to offload these functions to a server?
    
    
if __name__ == '__main__':
    main()