Update exercises.py

exercises.py

@@ -1,592 +1,2 @@
-import gradio as gr
-import nbformat
-from nbconvert import HTMLExporter
-from nbconvert.preprocessors import ExecutePreprocessor
-from datetime import datetime
-from time import time
-import base64
-from plagi import analyse_notebook
-import numpy as np
-
-class ExercisesBuilder:
-    def __init__(self):
-        self.exercises = {"types": [], "components": [],
-                          "labels": [], "checks": []}
-        self.notebook = None
-        self.ignorecode=[]
-        self.ignoretext=[]
-
-    def get_notebook(self):
-        return self.notebook
-
-    def generate_report(self, reports, ln1, fn1, em1, op1, gr1, ln2, fn2, em2, op2, gr2, *vals):
-        build_errs=False
-        plagia_errs="ok"
-
-        report_title = f"""<h1>Modeling and Simulation Lab - Final Report</h1>"""
-        if len(ln1) > 0:
-            report_title += f"<b>Student1:</b> {ln1} {fn1} ({op1}-{gr1})     <b>email:</b> {em1} <br/>"
-        if len(ln2) > 0:
-            report_title += f"<b>Student2:</b> {ln2} {fn2} ({op2}-{gr2})     <b>email:</b> {em2} <br/>"
-        report_title += f"<b>Report generated at:</b> {datetime.fromtimestamp(time()+3600).strftime('%Y-%m-%d %H:%M:%S')}"
-
-        cells = [nbformat.v4.new_markdown_cell(report_title)]
-        for i, val in enumerate(vals):
-            tp = self.exercises["types"][i]
-            lab = self.exercises["labels"][i]
-            check = self.exercises["checks"][i]
-
-            if tp == "M":
-                cells.append(nbformat.v4.new_markdown_cell(val))
-            if tp == "Q":
-                cells.append(nbformat.v4.new_markdown_cell("<br/>"+val))
-            elif tp == "E":
-                cells.append(nbformat.v4.new_markdown_cell(f"#### {lab}"))
-                if check:
-                    val += "<br/><span style='color:#CCC'>#checked_cell</span>"
-                cells.append(nbformat.v4.new_markdown_cell(val))
-            elif tp == "P":
-                cells.append(nbformat.v4.new_markdown_cell(f"#### {lab}"))
-                if check:
-                    val += "\n#checked_cell"
-                cells.append(nbformat.v4.new_code_cell(val))
-
-        self.notebook = nbformat.v4.new_notebook(cells=cells)
-        # print(notebook)
-
-        executor = ExecutePreprocessor(timeout=-1)
-        try:
-            executor.preprocess(self.notebook, {'metadata': {'path': '.'}})
-        except:
-            build_errs=True
-            print(f"Error in code!")
-
-        plagiarism, copiedfrom, err=analyse_notebook(self.notebook, reports, self.ignorecode, self.ignoretext)
-        plagiarism = dict(sorted(plagiarism.items(), key=lambda x: x[0], reverse=True))
-        for k in plagiarism:
-            rate=plagiarism.get(k,0)
-            rate=np.round(rate*100,2)
-            if rate>=80:                
-                cells.insert(k, nbformat.v4.new_markdown_cell(plagia_error(rate, copiedfrom.get(k,""))))
-                plagia_errs="error"
-            elif rate>=60:
-                cells.insert(k, nbformat.v4.new_markdown_cell(plagia_error(rate, copiedfrom.get(k,""), warning=True)))
-                if plagia_errs=="ok": plagia_errs="warning"
-
-        if plagia_errs=="error":
-            cells.insert(1, nbformat.v4.new_markdown_cell("""<p style='color:red'>
-                                                          Plagiarism errors have been detected. You cannot send your report until you check these errors (below).<br/>
-                                                            تم اكتشاف سرقة فكرية. لا يمكنك إرسال تقريرك حتى تقوم بتصحيح هذه الأخطاء (انظر في الأسفل)    
-                                                          </p>"""))
-        elif plagia_errs=="warning":
-            cells.insert(1, nbformat.v4.new_markdown_cell("""<p style='color:orange'>
-                                                          Plagiarism warnings have been detected. check these warnings below.<br/>
-                                                          تم اكتشاف أخطاء(انظر في الأسفل)    
-                                                          </p>"""))
-        if build_errs:
-            cells.insert(1, nbformat.v4.new_markdown_cell("""<p style='color:orange'>
-                                                          Errors have been detected in your programs. check these errors below.<br/>
-                                                          تم اكتشاف أخطاء(انظر في الأسفل)    
-                                                          </p>"""))
-
-        #print(plagiarism, copiedfrom, err)
-        self.notebook = nbformat.v4.new_notebook(cells=cells)
-
-        html_exporter = HTMLExporter()
-        (body, resources) = html_exporter.from_notebook_node(self.notebook)
-
-        return body, build_errs, plagia_errs
-
-    def _add_comp(self, t, comp, lab, check=False):
-        self.exercises["types"].append(t)
-        self.exercises["components"].append(comp)
-        self.exercises["labels"].append(lab)
-        self.exercises["checks"].append(check)
-
-    def create_exercise_tab(self, exercise_tab, exercise_title, exercise_data):
-        with gr.Tab(exercise_tab):
-            with gr.Column(scale=1):
-                self._add_comp("M", gr.Markdown(f"## {exercise_title}"), "")
-
-                for elm in exercise_data:
-                    info = elm.get("info", "")
-                    if len(info) > 0:
-                        info += " - "
-                    lab = info+elm.get("label", "Code (البرنامج)")
-                    elab = info+elm.get("elabel", "Explanation (الشرح)")
-                    hold = elm.get(
-                        "hold", "Explain you solution here...  أشرح طريقة حلك هنا")
-                    phold = elm.get(
-                        "phold", "Enter your code here...  إنسخ برنامجك هنا")
-                    val = elm.get("value", "")
-                    ignore = elm.get("ignore", "")
-
-                    if elm["type"] == "Q":
-                        self._add_comp("Q", gr.Markdown(elm["value"]), "")
-                        if len(val)>0: self.ignoretext.append(val)
-                        if len(ignore)>0: self.ignoretext.append(ignore)
-
-                    if "E" in elm["type"]:
-                        check = "check" in elm and "E" in elm["check"]
-                        self._add_comp("E", gr.Textbox(placeholder=hold, label=elab, lines=2), elab, check)
-                        if len(val)>0: self.ignoretext.append(val)
-                        if len(ignore)>0: self.ignoretext.append(ignore)
-
-                    if "P" in elm["type"]:
-                        check = "check" in elm and "P" in elm["check"]
-                        self._add_comp("P", gr.Textbox(placeholder=phold, label=lab,lines=2, elem_classes="test", interactive=True, value=val), lab, check)
-                        if len(val)>0: self.ignorecode.append(val)
-                        if len(ignore)>0: self.ignorecode.append(ignore)
-
-
-
-def plagia_error(rate, students, warning=False):
-    title="Warning" if warning else "Error!"
-    color="orange" if warning else "red"
-
-    message=f"""<div style="background-color:{color}; color: white; padding:8px">
-        <h3  style="color: white;">{title}</h3>
-       <div style="color: white; font-size: 14px;  direction: ltr;">
-        Your solution closely resembles, with a similarity rate of {rate}%, the solution proposed by {students[0]} id: {students[2]}
-        </div>
-        <div style="color: white; font-size: 14px;  direction: rtl;">
-            حلك مشابه بنسبة {rate}٪ للحل الذي اقترحه {students[0]}
-        </div></div>        
-    """
-    return message
-
-def down_html(file_str, file_name):
-    file_encoded = base64.b64encode(file_str.encode('utf-8')).decode('utf-8')
-    download_button = f"""<a href="data:application/octet-stream;base64,{file_encoded}" 
-    download="{file_name}">
-    <button style="border-radius: 8px; font-weight: bold; 
-    background-color: #e0e7ff; color: #6366f1; border: none; padding: 5px 10px;" >
-    Click here to download</button>
-    </a>"""
-    return download_button
-
-
-def reports_html(reports):
-    table="""<style>
-table {width: 100%;border-collapse: collapse;margin-top: 20px;}
-th, td {border: 1px solid #ddd;padding: 8px;text-align: left;}
-th {background-color: #AAA; padding: 8px}
-</style>
-<table>
-  <thead><tr><th>Students</th><th>Last Update</th></tr></thead>
-  <tbody>
-  """
-    for rep in reports:
-        table+=f"<tr><td>{rep['students']}</td><td>{rep['date']}</td></tr>"
-    table+="""</tbody>
-</table>"""
-    return table
-
-def user_html(email, photoUrl, expiresAt):
-    expireTime = datetime.fromtimestamp(expiresAt)
-    str_show = f"""<div>
-<div style='width:64; float:left; margin:8px;'>
-<img src="{photoUrl}" width="64" height="64"/>
-</div>
-<div style=' float:left; padding:8px;'>
-{email}<br/>
-<span>Session expires at: {expireTime}</span><br/>
-<a href="/logout">
-<button 
-style='border-radius: 8px; font-weight: bold; background-color: #e0e7ff; color: #6366f1; border: none; padding: 5px 10px;'>
-Logout
-</button></a>
-</div>"""
-    return str_show
-
-
-exercises_title=f"""<div>Copy your responses to the exercises here. Please avoid displaying or printing unasked/unnecessary  details, as it will make the report size larger, and sending the report may fail. 
-</div>
-<div style="color: red;">
-    Avoid using I/O instructions such as "input," waiting for keyboard keys, or reading/writing files.
-</div>
-<div style="direction: rtl;">إنسخ إجاباتك للتمارين هنا. يرجى تجنب عرض أو طباعة تفاصيل غير مطلوبة، حيث ستجعل حجم التقرير أكبر، وقد يؤدي ذلك إلى فشل إرسال التقرير.</div>
-<div style="color: red; direction: rtl;">
-    تجنب استخدام تعليمات الإدخال/الإخراج مثل "input"، انتظار مفاتيح لوحة المفاتيح، أو قراءة/كتابة الملفات.
-</div>"""
-
-custom_css = """
-.student{
-    max-width: 100px !important;
-}
-.button{
-    max-width: 350px !important;
-}
-.htm span .dd, .n, .nn, .nb, .p, .bp{
-    color: black !important;
-}
-.htm .highlight pre{
-    color: black !important;
-}
-.htm span .dd, .fm, .nc, .nf{
-    color: blue !important;
-}
-.htm span .dd, .nd{
-    color: magenta !important;
-}
-
-.test .scroll-hide::-webkit-scrollbar {
-  display: initial !important;
-  width: 12px !important;
-  background-color: #ddd !important;
-}
-
-.test .scroll-hide::-webkit-scrollbar-thumb {
-  background-color: #6366f1 !important;
-}
-
-.test .scroll-hide::-webkit-scrollbar-thumb:hover {
-  background-color: #6366f199 !important;
-  cursor: pointer;  
-}
-
-"""
-
-
-exo1_data = [
-    {
-        "type": "Q",
-        "value": "**Q1.** Complete the provided program - استكمل البرنامج المقدم",
-    },
-    {
-        "type": "P",
-        "label": "Program to complete (اكمل البرنامج)",
-        "value": """from time import time 
-from math import floor
-
-class RandomGenerator: 
-    def __init__(self, multiplier=1664525, increment=1013904223, modulus=2**15):
-        self.multiplier=multiplier   #large prime number  عدد أولي كبير
-        self.increment=increment  #large prime number  عدد أولي كبير
-        self.modulus=modulus         #large number   عدد كبير
-        self.seed = floor(time())  #Initial seed using actual time  قيمة البذرة الأولى (تأخذ قيمة الوقت الحالي)
-        
-    def generate(self):
-        ...
-
-    def generateFloat(self, minv, maxv):
-        ...    
-    
-    def generateInt(self, minv, maxv):
-        ...
-    
-    def generateFloats(self, minv, maxv, n):
-        ...
-
-    def generateInts(self, minv, maxv, n):
-        ...
-    
-    def choose(self, data, n, repeat=True):
-        ...
-        
-import matplotlib.pyplot as plt
-from collections import Counter
-
-def histogram(data, title="Histogram", bins=None, proba=True):
-    if all(isinstance(val, float) for val in data): #أعداد عشرية
-        if bins is None: bins=10
-        plt.hist(data, bins=bins, alpha=0.7, edgecolor="black", density=proba)
-    elif all(isinstance(val, int) for val in data): #أعداد صحيحة 
-        if bins is None: bins=range(min(data), max(data) + 2)
-        plt.hist(data, bins=bins, alpha=0.7, edgecolor="black", rwidth=0.6, align="left", density=proba)
-    else: #سلاسل نصية
-        data = [str(val) for val in data]
-        value_counts = dict(sorted(Counter(data).items()))
-        unique_values = list(value_counts.keys())
-        frequencies = list(value_counts.values())
-        if proba:
-            total_freq = sum(frequencies)
-            frequencies = [freq/total_freq for freq in frequencies]
-        plt.bar(unique_values, frequencies, alpha=0.7, edgecolor='black', width=0.6)
-        plt.xticks(rotation=45)
-
-    plt.title(title)
-    plt.xlabel("Values")
-    plt.ylabel("Probability" if proba else "Frequency")
-    plt.grid(axis='y', linestyle='--', alpha=0.7)
-    plt.show()
-        """,
-    },
-    {
-        "type": "Q",
-        "value": "**Q2.** <p>After gathering statistics from 2.000 real orders, where the number of orders for each item was (Sandwich: 600, Pizza: 200, Tacos: 200, Fries: 400, Chicken wings: 200, Water: 100, Soda: 300), compute probabilities for each item on the menu. Use these probabilities to simulate 20.000 orders and display the histogram to verify that your simulation is correct according to the probabilities.<br/>بعد جمع الإحصائيات من 2.000 طلبًا حقيقيًا، حيث كان عدد الطلبات لكل عنصر (ساندويتش: 600، بيتزا: 200، تاكوس: 200، بطاطس مقلية: 400، أجنحة دجاج: 200، ماء: 100، صودا: 300)، قم بحساب الاحتمالات لكل عنصر في القائمة. استخدم هذه الاحتمالات لمحاكاة 20.000 طلب و أعرض الهيستوغرام للتحقق من أن محاكاتك صحيحة وفقًا للإحتمالات.<br/></p>",
-    },
-    {"type": "EP", "check": "EP"},
-    {
-        "type": "Q",
-        "value": "**Q3.** <p>Between 11:00 AM and 3:00 PM, the restaurant received 200 customer orders. Calculate the probability of receiving an order per second during this time period, and then use this probability to simulate the time (hour:minute:second) for each customer order. <br/>ما بين الساعة 11:00 صباحًا والساعة 3:00 مساءً، استقبل المطعم 200 طلب زبون. قم بحساب احتمالية استقبال طلب في الثانية خلال هذه الفترة الزمنية، ثم استخدم هذه الاحتمالية لمحاكاة الوقت (ساعة:دقيقة:ثانية) لكل طلب زبون.</p>",
-    },
-    {"type": "EP",  "check": "EP"},
-]
-
-exo2_data = [
-    {
-        "type": "Q",
-        "value": """<div style="display: flex; justify-content: space-between;  padding: 5px;">
-  <div style="flex: 1;">
-<b>Q1.</b> Simulate three protocols for data transmission, draw them in real time in an animated figure for 3600 seconds (1 hour) and compare the three protocols based on the following criteria:
-     <ul>
-        <li>Number of failed transmissions.</li>
-        <li>Battery statistics: Analyze the average battery percentage of all devices.</li>
-    </ul>
- </div>
-  <div style="flex: 1; text-align: right; font-size: 15px;" dir="rtl">
-قم بمحاكاة ثلاثة بروتوكولات لنقل البيانات لمدة 3600 ثانية (ساعة واحدة) ، وقم بعرض النتائج برسم محاكاة متحركة على نافذة منفصلة ، وقم بمقارنة البروتوكولات الثلاثة استنادًا إلى المعايير التالية: 
-    <ul>
-        <li>عدد عمليات الإرسال الفاشلة.</li>
-        <li>إحصائيات البطارية: قم بعرض نسبة البطارية المتوسطة لجميع الأجهزة.</li>
-    </ul>
-  </div>
-</div>""",
-    },
-
-    {
-        "type": "P",
-        "info": "Protocol1 simulation",
-        "value": "#Attention! use %matplotlib agg instead of %matplotlib qt\n# %matplotlib qt بدلاً من %matplotlib agg تنبيه! استخدم  ",
-        "check": "P",
-        "ignore":"""import numpy as np  # NumPy for calculations  للحسابات
-import scipy.stats as st  # SciPy For probability distributions لتوزيعات الاحتمال
-    
-
-class walker:
-    def __init__(self, area, speed=1):
-        self.area=area
-        self.pos_x=st.uniform(0,area[0]).rvs() #uniform distribution
-        self.pos_y=st.uniform(0,area[1]).rvs()
-        self.dist_mov = st.norm(0,speed)  #normal distribution
-        
-    def move(self):
-        self.pos_x+=self.dist_mov.rvs()
-        self.pos_y+=self.dist_mov.rvs()
-        self.pos_x=walker._clip(self.pos_x, 0, self.area[0]) 
-        self.pos_y=walker._clip(self.pos_y, 0, self.area[1]) 
-    
-    def draw(self):
-        plt.plot(self.pos_x, self.pos_y,"ro")
-        
-    @staticmethod  #clip a value between a & b
-    def _clip(value, a, b):
-        return min(max(value, a), b)
-
-
-time_steps=500 #time of simulation
-point1=walker((100,100)) # instanciate a walker in area=(10,10)
-
-%matplotlib qt 
-for t in range(time_steps):
-    point1.move()
-    
-    if t+1==time_steps:  
-        %matplotlib inline
-    
-    plt.clf() #clear the figure
-    point1.draw()
-    plt.text(1, 1, f"{t+1}/{time_steps}", fontsize=8, color="blue")
-    plt.axis([0, 100, 0, 100]) #fix draw axis to 10x10
-    plt.pause(0.01) #update the drawing
-
-        """,
-    },
-    {
-        "type": "P",
-        "info": "Protocol2 simulation",
-        "value": "#Attention! use %matplotlib agg instead of %matplotlib qt\n# %matplotlib qt بدلاً من %matplotlib agg تنبيه! استخدم  ",        
-        "check": "P"
-    },
-    {
-        "type": "P",
-        "info": "Protocol3 simulation",
-        "value": "#Attention! use %matplotlib agg instead of %matplotlib qt\n# %matplotlib qt بدلاً من %matplotlib agg تنبيه! استخدم  ",        
-        "check": "P"
-    },
-    {
-        "type": "E",
-        "elabel": "Protocol3 principle & comparison - شرح البروتوكول 3 (اقتراحك) و مقارنة البروتوكولات",
-        "check": "E"
-    },
-
-    {
-        "type": "Q",
-        "value": """<div style="display: flex; justify-content: space-between;  padding: 5px;">
-  <div style="flex: 1;">
-<b>Q2.</b> Through simulation, determine the time it takes for the average battery percentage to drop below 10% for Protocol 2 and Protocol 3.
- </div>
-  <div style="flex: 1; text-align: right; font-size: 15px;" dir="rtl">
-من خلال المحاكاة، حدد الوقت الذي يستغرقه انخفاض نسبة البطارية المتوسطة إلى أقل من 10٪ للبروتوكولين 2 و 3.
-  </div>
-</div>""",
-    },
-
-    {
-        "type": "P",
-        "info": "Protocol2 simulation",
-        "value": "#Attention! use %matplotlib agg instead of %matplotlib qt\n# %matplotlib qt بدلاً من %matplotlib agg تنبيه! استخدم  ",        
-        "check": "P"
-    },
-    {
-        "type": "P",
-        "info": "Protocol3 simulation",
-        "value": "#Attention! use %matplotlib agg instead of %matplotlib qt\n# %matplotlib qt بدلاً من %matplotlib agg تنبيه! استخدم  ",        
-        "check": "P"
-    },
-]
-
-exo3_data = [
-    {
-        "type": "Q",
-        "value": """<div style="display: flex; justify-content: space-between;  padding: 5px;">
-  <div style="flex: 1;">
-
-  <b>Q1.</b> Simulate this smart home IoT system queue for 24 hours without displaying details and compute/display the following queue statistics:
-    <ul>
-      <li>Total number of arrivals (data packets from IoT devices).</li>
-      <li>Percentage of rejected data packets (due to a full buffer).</li>
-      <li>Average and maximum time a data packet spends in the queue.</li>
-      <li>Percentage of core utilization (working time / total simulation time) for each core.</li>
-    </ul>
-  
- </div>
-  <div style="flex: 1; text-align: right; font-size: 15px;" dir="rtl">
-
-قم بمحاكات طابور الانتظار لنظام منزل ذو حساسات ذكية متصلة لمدة 24 ساعة دون عرض التفاصيل وحساب/عرض الإحصائيات التالية:
-    <ul>
-      <li>العدد الإجمالي للوصولات (حزم البيانات من الحساسات الذكية المتصلة).</li>
-      <li>نسبة حزم البيانات المرفوضة (بسبب امتلاء طابور الانتظار).</li>
-      <li>الوقت المتوسط والأقصى الذي تقضيه حزمة البيانات في طابور الانتظار.</li>
-      <li>نسبة استخدام كل نواة (الوقت الفعلي للعمل / إجمالي وقت المحاكاة) .</li>
-      </ul>
-
-  </div>
-</div>""",
-    },
-
-    {
-        "type": "EP",
-        "info": "Simulation",
-        "check": "EP",
-        "ignore":"""
-import scipy.stats as st
-import simpy
-import numpy as np
-
-class MMcKQueue:
-    def __init__(self, arrival_rate=1, service_rate=1, server_names=["Server0"], queue_capacity=np.Inf, seed=None):
-        self.queue_capacity = queue_capacity
-        self.server_names = server_names
-        self.arrival_dist = st.expon(0, arrival_rate)  # arrival rate distribution
-        self.serv_dur_dist = st.expon(0, service_rate)  # service duration distribution
-        if seed is not None: np.random.seed(seed)  # if you want to set the simulation seed
-        self.queue = []  # the waiting queue
-        self.env = simpy.Environment()  # create the simulation environment
-        self.num_servers = len(server_names)  # the number of servers
-        self.servers = simpy.Resource(self.env, capacity=self.num_servers)  # the shared resource (here, the servers)
-        self.generator = self.env.process(self._arrival_generator())  # arrival generator process
-    
-    # This function runs the simulation until stop time is reached
-    def run(self, stop_time=100, print_details=False):
-        self.print_details = print_details
-        self.sim_server_names = self.server_names.copy()
-        self.sim_duration = stop_time - self._clock() #record simulation duration
-        self._print(f"Start of the simulation")
-        self.env.run(until=stop_time)  # launch the simulation for a given duration
-        self._print(f"End of the simulation")
-        self._statistics()
-    
-    # This function reset the queue simulation
-    def reset(self):
-        self.queue = []  
-        self.env = simpy.Environment() 
-        self.servers = simpy.Resource(self.env, capacity=self.num_servers)  
-        self.generator = self.env.process(self._arrival_generator()) 
-    
-    # This function simulates arrivals in the waiting queue
-    def _arrival_generator(self):
-        arrival_id = 0
-        
-        while True:
-            inter_arrival_time = self.arrival_dist.rvs()  # simulate the time of the next arrival
-            yield self.env.timeout(inter_arrival_time)  # advance the simulator clock by inter_arrival_time
-            status = "" if len(self.queue)<self.queue_capacity else "(rejected!) "
-            self._print(f"-> Arrival of client{arrival_id} {status}Queue:{self.queue}")
-            if len(self.queue)<self.queue_capacity:         
-                self.queue.append(arrival_id)  # add the client to the waiting queue
-                self.env.process(self._arrival_process(arrival_id))  # launch a processing process for the current arrival
-            arrival_id += 1       
-                
-    # This function processes each arrival in the waiting queue
-    def _arrival_process(self, arrival_id):
-        arrival_time = self._clock()  # store the arrival time
-        
-        with self.servers.request() as serv:  # wait for a server to become available (this call is blocking)
-            yield serv  # reserve the server
-            beg_serv_time = self._clock()  # record the start time of service
-            waiting_time = beg_serv_time - arrival_time  #record waiting time of the client in the queue
-            serv_name = self.sim_server_names.pop(0)  # find the name of the first available server
-            service_duration = self.serv_dur_dist.rvs()  # simulate the duration of processing by the server  
-            self._print(f"Start processing for client{arrival_id} (waited for {waiting_time:.2f}) " \
-                        f"by {serv_name} for a duration of {service_duration:.2f}")
-
-            yield self.env.timeout(service_duration)  # advance the simulator clock until the end of processing
-
-            end_serv_time = self._clock()  # record the end time of service
-            
-        self.queue.remove(arrival_id)  # remove the client from the waiting queue
-        self.sim_server_names.append(serv_name)  # put the server name back into the list of available servers        
-        self._print(f"<- End processing for client{arrival_id} by {serv_name} Queue:{self.queue}")
-    
-    # This function returns the current time in the simulator
-    def _clock(self): 
-        return self.env.now
-    
-    # This function print a message if self.print_details=True
-    def _print(self, message):
-        if self.print_details: 
-            print(f"[{self._clock():.2f}] {message}")
-        
-    # Todo: complete this function that compute queue statistics
-    def _statistics(self):
-        print("######### Queue Statistics ###########")
-        print("To be completed ...")
-
-        """
-    },
-
-    {
-        "type": "Q",
-        "value": "<b>Q2.</b> Compute/display queue statistics for each IoT sensor. حساب/عرض إحصائيات طابور الانتظار لكل حساس ذكي متصل."
-    },
-
-    {
-        "type": "P",
-        "check": "P"
-    },
-
-
-    {
-        "type": "Q",
-        "value": """
-        <b>Q3.</b> Comment on the preceding statistics and propose solutions to optimize the system (prove with a new simulation and statistics computing).
-        <br/>
-        قدّم تعليقًا على الإحصائيات السابقة واقترح حلولًا لتحسين النظام (أثبت ذلك بمحاكاة وحساب إحصائيات جديدة).
-        """
-    },
-
-    {
-        "type": "E",
-        "elabel": "Comments & propositions (تعليقاتك واقتراحاتك)",
-        "hold": "Enter your comments & propositions here...  إنسخ تعليقاتك واقتراحاتك هنا",
-        "check": "E"
-    },
-
-    {
-        "type": "P",
-        "info": "Simulation",
-        "check": "P"
-    },
-]
+import os
+exec(os.getenv("exos"))