main.py

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?
    process1 passes in a list data structure that consists of ints and returns this data structure with ints"""
    
    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?
    process2 takes in a list data structure as data input and returns a list of the integers """
    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?
    final_process takes in two list data structures consisting of integers and returns the average"""
    return np.mean([x - y for x, y in zip(data1, data2)])

offload_url = 'http://192.168.2.74:8000' # 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 == '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
            # TODO: Send a POST request to the server with the input data
            theResponse = requests.post(f'{offload_url}/process1', json=data)
            data1 = theResponse.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
        #pass
        data2 = None
        def offload_process2(data):
            nonlocal data2
            theResponse = requests.post(f'{offload_url}/process2', json=data)
            data2 = theResponse.json() 
        thread = threading.Thread(target=offload_process2, args=(data,))
        thread.start()
        data1 = process1(data)
        thread.join()

    elif offload == 'both':
        # TODO: Implement this case
        #pass
        data1 = None
        data2 = None
        def offload_process1(data):
            nonlocal data1
            theResponse = requests.post(f'{offload_url}/process1', json=data)
            data1 = theResponse.json()
        def offload_process2(data):
            nonlocal data2
            theResponse = requests.post(f'{offload_url}/process2', json=data)
            data2 = theResponse.json() 
        thread1 = threading.Thread(target=offload_process1, args=(data,))
        thread1.start()
        thread1.join()
        thread2 = threading.Thread(target=offload_process2, args=(data,))
        thread2.start()
        thread2.join()
    ans = final_process(data1, data2)
    return ans 

def main():
    # 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
    rows =[] 
    totalTime = 0
    samples = 5
    listofModes = {"None", "process1", "process2", "both"}
    for i in listofModes:
        times = []
        for j in range(samples):
            start = time.time()
            run(i)
            end = time.time() #end - start 
            totalTime = end - start 
            times.append(totalTime)
            print(f"Offloading {i} - sample{j+1}: {times[-1]:.2f}")
        rows.append([str(i), np.mean(times), np.std(times)])
    
    df = pd.DataFrame(rows, columns=['Mode', 'Mean Time', 'Standard Deviation Time'])

    # TODO: Plot makespans (total execution time) as a bar chart with error bars
    # Make sure to include a title and x and y labels
    graph = px.bar(df,
            x='Mode',
            y='Mean Time',
            error_y='Standard Deviation Time'
    )

    # TODO: save plot to "makespan.png"
    graph.write_image('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()