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app.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+import gradio as gr
+import numpy as np
+
+# ----------------------------------------------------------------------------------------------------------------------------
+# Helpful function
+# ----------------------------------------------------------------------------------------------------------------------------
+# Set up the Files
+def SetUpFile(flag):
+    # print(flag)
+    file =0
+    if flag:
+        file= pd.read_csv('data.csv',index_col=0)
+    else:
+        file=pd.read_csv('data-3 (2).csv',index_col=0)
+
+    return file
+# Convert from week Type to date type and return string
+def ConvertWeektoDate(Year_week):
+    import datetime
+    d = Year_week
+    r = datetime.datetime.strptime(d + '-1', "%Y-W%W-%w").date()
+    return r.strftime('%Y/%m/%d')
+# This function get flag (true for file 1) and country name and colum
+# and return kind of list
+def GetCleanData(flag,country,colum):
+    if flag:
+        file1=SetUpFile(flag)
+        # print(file1.head())
+        return file1.loc[[country],colum]
+
+    else:
+        file2=SetUpFile(flag)
+        return file2.loc[[country],colum]
+def GetDatesFromFile1(country):
+    file1=SetUpFile(True)
+    date_list = file1.loc[[country],'year_week'].values
+    Array_toreturn=[]
+    for x in date_list:
+        # print(x)
+        real_date=ConvertWeektoDate(x)
+        # print(real_date)
+        Array_toreturn.append(real_date)
+    # print(Array_toreturn)
+    return Array_toreturn
+
+def TopN(list1, N):
+    final_list = []
+
+    for i in range(0, N):
+        max1 = 0
+
+        for j in range(len(list1)):
+            if list1[j] > max1:
+                max1 = list1[j]
+
+        list1.remove(max1)
+        final_list.append(max1)
+
+    return(final_list)
+
+def Sum_Of_Var_In_Country(country):
+    date_list = GetCleanData(True, country, 'year_week')
+    the_lastOne = '2020'
+    filtersDate = []
+    for x in date_list:
+        filtersDate.append(x[0:4])
+    UniqList = set(filtersDate)
+    UniqList = list(UniqList)
+    UniqList.sort()
+    # print(UniqList)
+    data = GetCleanData(True, country, 'tests_done')
+    # print(len(data))
+    filedate = sorted(date_list)
+    array_ofSum = []
+    sum = 0
+    for x in range(len(data)):
+        # print(x, data.values[x], sum, filedate[x][0:4])
+        if x != 0:
+            the_lastOne = filedate[x - 1][0:4]
+        if the_lastOne == filedate[x][0:4]:
+            sum += data.values[x]
+        if the_lastOne != filedate[x][0:4] or x == len(data) - 1:
+            array_ofSum.append(sum)
+            sum = 0
+
+    # print((array_ofSum), (UniqList))
+    return ((array_ofSum), (UniqList))
+
+
+# ----------------------------------------------------------------------------------------------------------------------------
+# The Hw
+# ----------------------------------------------------------------------------------------------------------------------------
+
+
+
+
+# ----------------------------------------------------------------------------------------------------------------------------
+# Part 1
+# ----------------------------------------------------------------------------------------------------------------------------
+#Q1
+def Display_Test_Rate(country):
+    data=GetCleanData(True,country,'testing_rate')
+    Cyprus_Test_rate_list=data.values.tolist()
+    xpoints = list(range(len(Cyprus_Test_rate_list)))
+    XDate=GetDatesFromFile1(country)
+    plt.plot(xpoints,Cyprus_Test_rate_list)
+    plt.title('Testing Rate in '+country)
+    plt.ylabel('testing rate')
+    plt.xlabel('week number')
+    # plt.xticks(rotation=-45)
+    plt.show()
+
+
+#Q2
+def Display_postive_rate(country):
+    Sweeden=GetCleanData(True,country,'positivity_rate')
+    # print(Sweeden.values)
+    xpoints = list(range(len(Sweeden.values)))
+    plt.plot(xpoints,Sweeden.values)
+    plt.title('positivity_rate in '+country)
+    plt.ylabel('R ratio')
+    plt.xlabel('week number')
+    # plt.xticks(rotation=-45)
+    plt.axhline(y=2, color='green', linestyle='-')
+    plt.text(0,4,'GOOD ratio')
+    plt.axhline(y=15, color='orange', linestyle='-')
+    plt.text(0, 17, 'satisfy ratio')
+    plt.axhline(y=30, color='red', linestyle='-')
+    plt.text(0, 32, 'Very BAD ratio')
+
+    plt.show()
+
+#Q3
+def Display_SumOfTest(country):
+    date_list=GetDatesFromFile1(country)
+    the_lastOne=''
+    filtersDate=[]
+    for x in date_list:
+        filtersDate.append(x[0:7])
+    UniqList=set(filtersDate)
+    UniqList=list(UniqList)
+    UniqList.sort()
+    # print(UniqList)
+    data=GetCleanData(True,country,'tests_done')
+    filedate = GetDatesFromFile1(country)
+    array_ofSum=[]
+    # print(data.values)
+    # print(filedate)
+    sum=0
+    for x in range(len(data)):
+        # print(x,data.values[x],filedate[x][0:7],UniqList[x])
+        print(x, data.values[x], filedate[x][0:7])
+        if x!=0:
+            the_lastOne = filedate[x - 1][0:7]
+            print(the_lastOne)
+        if the_lastOne==filedate[x][0:7]:
+            sum+=data.values[x]
+            print(sum)
+        else:
+            array_ofSum.append(sum)
+            sum=0
+            print('skip')
+
+    # xpoints = list(range(len(Sweeden.values)))
+    colors = ['red', 'green']
+    plt.bar(UniqList,array_ofSum,color=colors)
+    plt.xticks(rotation=-90)
+    plt.ylabel('Sum of Test done')
+    plt.xlabel('Year/month')
+    plt.title('Sum of test done by month in '+country)
+
+
+    plt.show()
+
+#Q4
+def Display_SumOfPostive(country):
+    date_list = GetDatesFromFile1(country)
+    the_lastOne = ''
+    filtersDate = []
+    for x in date_list:
+        filtersDate.append(x[0:7])
+    UniqList = set(filtersDate)
+    UniqList = list(UniqList)
+    UniqList.sort()
+    # print(UniqList)
+    data = GetCleanData(True, country, 'new_cases')
+    filedate = GetDatesFromFile1(country)
+    array_ofSum = []
+    # print(data.values)
+    # print(filedate)
+    sum = 0
+    for x in range(len(data)):
+        # print(x,data.values[x],filedate[x][0:7],UniqList[x])
+        print(x, data.values[x], filedate[x][0:7])
+        if x != 0:
+            the_lastOne = filedate[x - 1][0:7]
+            print(the_lastOne)
+        if the_lastOne == filedate[x][0:7]:
+            sum += data.values[x]
+            print(sum)
+        else:
+            array_ofSum.append(sum)
+            sum = 0
+            print('skip')
+
+    # xpoints = list(range(len(Sweeden.values)))
+    colors = ['red', 'green']
+    plt.bar(UniqList, array_ofSum, color=colors)
+    plt.xticks(rotation=-90)
+    plt.ylabel('Sum of new cases')
+    plt.xlabel('Year/month')
+    plt.title('Sum of new cases by month in '+country)
+
+    plt.show()
+
+#Q5
+def Display_Death(country):
+    dataY=GetCleanData(False,country,'value')
+    dataX=GetCleanData(False,country,'date')
+    xpoints = list(range(len(dataX.values)))
+    plt.plot(xpoints,dataY.values)
+    plt.ylabel('Amount of Death')
+    plt.xlabel('Index')
+    plt.title('Amount of Death in '+country)
+    plt.show()
+
+#Q6
+def Display_DeathPie(country):
+
+    date_list=GetCleanData(False,country,'date')
+    the_lastOne='2020'
+    filtersDate=[]
+    for x in date_list:
+        filtersDate.append(x[0:4])
+    UniqList=set(filtersDate)
+    UniqList=list(UniqList)
+    UniqList.sort()
+    # print(UniqList)
+    data=GetCleanData(False,country,'value')
+    print(len(data))
+    filedate = sorted(date_list)
+    array_ofSum=[]
+    # print(data.values)
+    print('sort Date',filedate)
+    sum=0
+    for x in range(len(data)):
+        print(x,data.values[x],sum,filedate[x][0:4])
+        # print(x, data.values[x], filedate[x][0:4])
+        if x!=0:
+            the_lastOne = filedate[x - 1][0:4]
+            # print(the_lastOne)
+        if the_lastOne==filedate[x][0:4]:
+            sum+=data.values[x]
+            # print(sum)
+        if the_lastOne!=filedate[x][0:4] or x==len(data)-1:
+            array_ofSum.append(sum)
+            sum=0
+            print('skip')
+
+    print((array_ofSum),(UniqList))
+    colors=['r','g','b','y']
+
+    plt.pie(array_ofSum, labels=UniqList, colors=colors,
+            startangle=90, shadow=True,
+            radius=1.2, autopct='%1.1f%%')
+    plt.text(-0.5,1.5,'The death in '+country)
+    plt.legend()
+    plt.show()
+
+#Q7
+def Display_TestRatio(country):
+    tests_done = GetCleanData(True,country,'tests_done').values
+    new_cases = GetCleanData(True,country,'new_cases').values
+    Ratio =[]
+
+    # print(len(tests_done),len(new_cases))
+    for i in range(len(tests_done)):
+        if tests_done[i]!=0:
+            Ratio.append(new_cases[i]/tests_done[i])
+
+
+    xpoints = list(range(len(Ratio)))
+    print(xpoints,Ratio)
+    plt.plot(xpoints,Ratio,marker='o')
+    plt.title('The Ratio new_cases/test_done   in '+country)
+    plt.ylabel('ratio')
+    plt.xlabel('weeks')
+    plt.show()
+
+#Q8
+def Display_DeathAvrage(country):
+    deathList=GetCleanData(False,country,'value')
+    date_list=GetCleanData(False,country,'date')
+    the_lastOne=''
+    filtersDate=[]
+    for x in date_list:
+        filtersDate.append(x[0:7])
+    UniqList=set(filtersDate)
+    UniqList=list(UniqList)
+    UniqList.sort()
+    # print(UniqList)
+    # print(len(deathList))
+    filedate = sorted(date_list)
+    array_ofSum=[]
+    # print(data.values)
+    # print('sort Date',filedate)
+    count=0
+    sum=0
+    for x in range(len(deathList)):
+        print(x,deathList.values[x],sum,filedate[x][0:7])
+        # print(x, data.values[x], filedate[x][0:4])
+        if x!=0:
+            the_lastOne = filedate[x - 1][0:7]
+            # print(the_lastOne)
+        if the_lastOne==filedate[x][0:7]:
+            count=count+1
+            sum+=deathList.values[x]
+            # print(sum)
+        if the_lastOne!=filedate[x][0:7] or x==len(deathList)-1:
+            if count==0:
+                continue
+            array_ofSum.append(sum/count)
+            sum=0
+            count=0
+            print('skip')
+
+    print(len(array_ofSum),len(UniqList))
+    print((array_ofSum),(UniqList))
+    colors=['r','g','b']
+    plt.bar(UniqList,array_ofSum,color=colors)
+    plt.xticks(rotation=-90)
+    plt.title('The avarge of death in '+country)
+    plt.ylabel('avg of death')
+    plt.show()
+#Q9
+def Display_freqSource(country):
+    all=SetUpFile(False)
+    print(len(all['source'].values))
+    colors=['r','g','b']
+    plt.hist(all['source'].values,color='pink',alpha=1,width=0.8)
+    plt.xticks(rotation=-45)
+    plt.title('The frequency of source data')
+    plt.ylabel('frequency')
+    plt.show()
+
+#Q10
+def Display_NegtiveTest(country):
+    tests_done = GetCleanData(True, country, 'tests_done').values
+    new_cases = GetCleanData(True, country, 'new_cases').values
+
+
+    date_list = GetDatesFromFile1(country)
+    the_lastOne = '2020'
+    filtersDate = []
+    for x in date_list:
+        filtersDate.append(x[0:4])
+    UniqList = set(filtersDate)
+    UniqList = list(UniqList)
+    UniqList.sort()
+    # print(UniqList)
+    print(len(tests_done),len(new_cases))
+    filedate = sorted(date_list)
+    array_ofSum = []
+    # print(data.values)
+    # print('sort Date', filedate)
+    sum = 0
+    sumOfTest=0
+    for x in range(len(new_cases)):
+        print(x, new_cases[x],tests_done[x], sum, filedate[x][0:4])
+        # print(x, data.values[x], filedate[x][0:4])
+        if x != 0:
+            the_lastOne = filedate[x - 1][0:4]
+            # print(the_lastOne)
+        if the_lastOne == filedate[x][0:4]:
+            sum += tests_done[x]-new_cases[x]
+            sumOfTest+=tests_done[x]
+            # print(sum)
+        if the_lastOne != filedate[x][0:4] or x == len(new_cases) - 1:
+            array_ofSum.append(sum/sumOfTest)
+            sum = 0
+            sumOfTest=0
+            print('skip')
+
+    print((array_ofSum), (UniqList))
+    colors = ['r', 'g', 'b', 'y']
+    plt.barh(UniqList,array_ofSum,color='Yellowgreen',height=0.6)
+    plt.title('The negtive precentage of tests in '+country)
+    plt.show()
+
+
+
+
+
+
+
+
+
+
+# ----------------------------------------------------------------------------------------------------------------------------
+# Part2
+# ----------------------------------------------------------------------------------------------------------------------------
+
+#guy
+def Compare_avg_pr_by_two_countries(year, country1, country2):
+    data = pd.read_csv('data.csv', index_col=0)
+    file1 = data.loc[country1, ['positivity_rate', 'year_week']]
+    file2 = data.loc[country2, ['positivity_rate', 'year_week']]
+    sum1 = 0
+    sum2 = 0
+    filteredData1 = file1[file1['year_week'].str.startswith(str(year))]['positivity_rate'].values
+    filteredData2 = file2[file2['year_week'].str.startswith(str(year))]['positivity_rate'].values
+    for i in filteredData1:
+        sum1+=i
+    for i in filteredData2:
+        sum2+=i
+
+    colors = ['r', 'g']
+    array_ofSum = [sum1, sum2]
+    UniqList = [country1, country2]
+    plt.pie(array_ofSum, labels=UniqList, colors=colors,
+            startangle=90, shadow=True, autopct='%1.1f%%')
+    plt.title('Compare positive rate average between ' + country1 + ' and ' + country2 + ' in ' + str(year))
+    plt.legend()
+    plt.show()
+#guy
+def Compare_positive_rate(year, country1, country2):
+    data = pd.read_csv('data.csv', index_col=0)
+    file1 = data.loc[country1, ['positivity_rate', 'year_week']]
+    file2 = data.loc[country2, ['positivity_rate', 'year_week']]
+
+    filteredData1 = file1[file1['year_week'].str.startswith(str(year))]['positivity_rate'].values
+    filteredData2 = file2[file2['year_week'].str.startswith(str(year))]['positivity_rate'].values
+
+    xpoints = list(range(len(filteredData1)))
+    ypoints = list(range(len(filteredData2)))
+    plt.bar(ypoints, filteredData2, label=country1,color='g')
+
+    plt.bar(xpoints, filteredData1, label=country1, color='r')
+
+    plt.title('Comparing positivity rate between {} and {} for {}'.format(country1, country2, year))
+    plt.xlabel('Week number')
+    plt.ylabel('Positivity Rate')
+    plt.legend()
+
+    plt.show()
+
+#guy
+def Compare_deaths_based_indicator_in_a_given_year(year):
+    data = SetUpFile(False)
+    data['year'] = data['year_week'].str[:4]
+    data_year = data[data['year'] == year]
+    deaths_by_indicator = data_year.groupby('indicator')['value'].sum()
+    print(deaths_by_indicator.keys())
+    colors = ['r', 'g','y','b']
+
+    plt.bar(deaths_by_indicator.keys(), deaths_by_indicator.values, label=deaths_by_indicator.keys(),color=colors)
+
+    plt.title('Compare deaths based on info from each indicator_in '+year)
+
+    plt.legend()
+    plt.show()
+
+#dor
+def compare_new_cases(country1, country2, year):
+
+    date_list1=GetCleanData(True,country1,'year_week')
+
+    data1=GetCleanData(True,country1,'new_cases')
+    print(data1)
+    filedate1 = sorted(date_list1)
+    sum1=0
+    count=0
+    for x in range(len(data1)):
+        if filedate1[x - 1][0:4]==year:
+            # print(the_lastOne,' ', data1.values[x])
+            sum1+=data1.values[x-1]
+            count+=1
+
+    print(sum1)
+
+    date_list2=GetCleanData(True,country2,'year_week')
+    data2=GetCleanData(True,country2,'new_cases')
+    filedate2 = sorted(date_list2)
+    sum2=0
+    for x in range(len(data1)):
+        if filedate2[x - 1][0:4]==year:
+            the_lastOne = filedate2[x - 1][0:4]
+            sum2+=data2.values[x-1]
+    print(sum2)
+    colors=['r','g']
+    array_ofSum=[sum1,sum2]
+    UniqList=[country1,country2]
+    plt.pie(array_ofSum, labels=UniqList, colors=colors,
+            startangle=90, shadow=True,
+            radius=1.2, autopct='%1.1f%%')
+    plt.text(-1,1.5,'Comparation of new cases between '+country1+ ' and '+ country2+' in '+year)
+    plt.legend()
+    plt.show()
+#Dor
+def Display_Tests_Done_Comparation(country1, country2):
+    sumCounty1=Sum_Of_Var_In_Country(country1)[0]
+    sumCounty2=Sum_Of_Var_In_Country(country2)[0]
+    years=Sum_Of_Var_In_Country(country1)[1]
+    print(sumCounty1,sumCounty2)
+    arrOfSums=(sumCounty1,sumCounty2)
+    print(years)
+
+    plt.bar(years,sumCounty2,color='b')
+    plt.bar(years,sumCounty1,color='r')
+    plt.ylabel('amount of tests')
+    plt.xlabel('years')
+    plt.title('Comparation of test done between '+country1+' and '+country2)
+    plt.show()
+#dor
+def plot_top_months_with_deaths(year,NumOfMonth):
+    data = SetUpFile(False)
+    #return the relevent data by wanted year
+    data_year = data[data['date'].str[:4] == year]
+    #return sum of death for all month
+    deaths_by_month = data_year.groupby(data_year['date'].str[5:7])['value'].sum().reset_index()
+    #sort month and take 3 largest
+    sorted_months = deaths_by_month.sort_values('value', ascending=False).head(NumOfMonth)
+
+    months = sorted_months['date']
+    deaths = sorted_months['value']
+
+    plt.bar(months, deaths)
+    plt.xlabel('Month')
+    plt.ylabel('Number of Deaths')
+    plt.title(f'Top Three Months with Highest Deaths in {year}')
+    plt.show()
+
+
+#Q12
+def Display_TheMostTestAmount(year,N):
+    countries=GetUniqCountry()
+    SumOfTestbyYear=[]
+    SumOfTestbyYear2=[]
+    for country in countries:
+        test_Done_per_country=GetCleanData(True,country,'tests_done').values
+        Years_per_country = GetCleanData(True, country, 'year_week').values
+    # print(len(test_Done_per_country),len(Years_per_country))
+        sum=0
+        for i in range(len(test_Done_per_country)):
+            if Years_per_country[i][:4]==year:
+                sum+=test_Done_per_country[i]
+        SumOfTestbyYear.append(sum)
+        SumOfTestbyYear2.append(sum)
+
+    print(len(SumOfTestbyYear),SumOfTestbyYear)
+    temp=SumOfTestbyYear
+    topn=TopN(temp,N)
+    print(topn)
+    print(len(SumOfTestbyYear), SumOfTestbyYear)
+    listLabels=[]
+    for i in range(len(SumOfTestbyYear2)):
+        for j in range(len(topn)):
+            if topn[j]==SumOfTestbyYear2[i]:
+                listLabels.append(countries[i])
+    print(listLabels)
+
+    plt.bar(listLabels,topn,width=0.8,color=['r','g','b'])
+    plt.title('Top '+str(N)+' countries with the most test amount')
+    plt.ylabel('Test amount (in 10 millions)')
+    plt.legend()
+    plt.show()
+#Q15
+def Display_popdeath_ratio(year,N):
+    file=SetUpFile(False)
+    countries=list(set(file['value'].keys()))
+    dates=file['date'].values
+    Death = file['value'].values
+    print(len(countries),countries)
+    ValuesSumsPerCountry=[]
+    ValuesSumsPerCountry2=[]
+
+    popList=[]
+    popTopList=[]
+    for i in range(len(countries)):
+        popList.append(GetCleanData(True,countries[i],'population')[0])
+        date_country=GetCleanData(False,countries[i],'date').values
+        death_country=GetCleanData(False,countries[i],'value').values
+        # print(len(date_country), date_country)
+        # print(len(death_country), death_country)
+        sumDeathPer = 0
+        for a in range(len(death_country)):
+            if date_country[a][:4]==str(year):
+                sumDeathPer+=death_country[a]
+        ValuesSumsPerCountry.append(sumDeathPer)
+        ValuesSumsPerCountry2.append(sumDeathPer)
+
+    labelList=[]
+    newRatioList=[]
+    newRatioList2 = []
+    andDeathValueTop=[]
+    for i in range(len(ValuesSumsPerCountry)):
+        newRatioList.append(ValuesSumsPerCountry[i]/popList[i])
+        newRatioList2.append(ValuesSumsPerCountry[i] / popList[i])
+
+
+
+
+    topn=TopN(newRatioList,N)
+    for i in range(len(ValuesSumsPerCountry2)):
+        if topn.__contains__(newRatioList2[i]):
+            labelList.append(countries[i])
+            popTopList.append(popList[i])
+            andDeathValueTop.append(ValuesSumsPerCountry2[i])
+
+    print(len(ValuesSumsPerCountry2),ValuesSumsPerCountry2)
+    print(len(popTopList), popTopList)
+    print(len(topn), topn)
+    print(len(labelList), labelList)
+
+    plt.bar(labelList,popTopList,label='Population amount')
+    plt.bar(labelList,andDeathValueTop,label='Death amount')
+    plt.title('Top '+str(N)+' countries with the most Death amount vs population amount')
+    plt.ylabel('in 10 millions')
+    plt.legend()
+    plt.show()
+
+#Q18
+def DisplayTestRatio(country1,country2,year):
+    datesList=GetDatesFromFile1(country1)
+    tests_done1=GetCleanData(True,country1,'tests_done').values
+    new_cases1=GetCleanData(True,country1,'new_cases').values
+    tests_done2=GetCleanData(True,country2,'tests_done').values
+    new_cases2=GetCleanData(True,country2,'new_cases').values
+
+    tests_done1F=[]
+    new_cases1F=[]
+    tests_done2F=[]
+    new_cases2F=[]
+
+    for i in range(len(datesList)):
+        if datesList[i][:4]==str(year):
+            tests_done1F.append(tests_done1[i])
+            new_cases1F.append(new_cases1[i])
+            tests_done2F.append(tests_done2[i])
+            new_cases2F.append(new_cases2[i])
+
+    print(len(tests_done1F),sum(tests_done1F))
+    print(len(new_cases1F), sum(new_cases1F))
+    print(len(tests_done2F),sum(tests_done2F) )
+    print(len(new_cases2F),sum(new_cases2F) )
+    plt.pie([sum(tests_done1F),sum(tests_done2F)],labels=['tests done in '+country1,'tests done in '+country2],radius=0.75, center=(1.7,0),colors=['r','b'],  shadow = True ,autopct='%1.1f%%')
+    plt.pie([sum(new_cases1F),sum(new_cases2F)],labels=['new cases in '+country1,'new cases in '+country2],radius=0.75, center=(-1.7, 0), colors=['y','g'],  shadow = True,autopct='%1.1f%%')
+    plt.pie([1],radius=0.001,center=(0,0))
+    plt.title('compare test done vs new cases in '+country1+' vs '+ country2+' ('+year+')')
+
+    plt.show()
+
+#Dor
+def compar_Death_between_three_countries(country1,country2,country3):
+    countries = [country1, country2, country3]
+    data = pd.read_csv('data-3 (2).csv')
+    # Convert 'date' column to datetime type
+    data['date'] = pd.to_datetime(data['date'], format='%Y-%m-%d')  # Specify the date format if needed
+
+    # Filter the data for the selected countries
+    filtered_data = data[data['country'].isin(countries)]
+
+    # Extract year and month from the 'date' column
+    filtered_data['year'] = filtered_data['date'].dt.year
+
+    # Group the data by country, year, and month, and calculate the sum of values
+    grouped_data = filtered_data.groupby(['country', 'year'])['value'].sum().reset_index()
+
+    # Set up the figure and axis
+    fig, ax = plt.subplots(figsize=(10, 6))
+
+    # Get unique years from the data
+    years = grouped_data['year'].unique()
+
+    # Plot data for each country
+    for country in countries:
+        country_data = grouped_data[grouped_data['country'] == country]
+        ax.plot(country_data['year'], country_data['value'], label=f"{country} ")
+
+
+    # Customize the plot
+    ax.set_xlabel('Year')
+    ax.set_ylabel('Death')
+    ax.set_title('Comparison of Death between '+country1+' '+country2+' '+country3+' '+ 'by Year ')
+    ax.legend()
+
+    # Show the plot
+    plt.show()
+
+# ----------------------------------------------------------------------------------------------------------------------------
+# Dashboard
+# ----------------------------------------------------------------------------------------------------------------------------
+
+def DeathPieYears(country):
+    date_list = GetCleanData(False, country, 'date')
+    the_lastOne = '2020'
+    filtersDate = []
+    for x in date_list:
+        filtersDate.append(x[0:4])
+    UniqList = set(filtersDate)
+    UniqList = list(UniqList)
+    UniqList.sort()
+    # print(UniqList)
+    data = GetCleanData(False, country, 'value')
+    print(len(data))
+    filedate = sorted(date_list)
+    array_ofSum = []
+    # print(data.values)
+    print('sort Date', filedate)
+    sum = 0
+    for x in range(len(data)):
+        print(x, data.values[x], sum, filedate[x][0:4])
+        # print(x, data.values[x], filedate[x][0:4])
+        if x != 0:
+            the_lastOne = filedate[x - 1][0:4]
+            # print(the_lastOne)
+        if the_lastOne == filedate[x][0:4]:
+            sum += data.values[x]
+            # print(sum)
+        if the_lastOne != filedate[x][0:4] or x == len(data) - 1:
+            array_ofSum.append(sum)
+            sum = 0
+            print('skip')
+
+    print((array_ofSum), (UniqList))
+    return [array_ofSum,UniqList]
+def PostiveSumbyMonth(country):
+    date_list = GetDatesFromFile1(country)
+    the_lastOne = ''
+    filtersDate = []
+    for x in date_list:
+        filtersDate.append(x[0:7])
+    UniqList = set(filtersDate)
+    UniqList = list(UniqList)
+    UniqList.sort()
+    # print(UniqList)
+    data = GetCleanData(True, country, 'new_cases')
+    filedate = GetDatesFromFile1(country)
+    array_ofSum = []
+    # print(data.values)
+    # print(filedate)
+    sum = 0
+    for x in range(len(data)):
+        # print(x,data.values[x],filedate[x][0:7],UniqList[x])
+        print(x, data.values[x], filedate[x][0:7])
+        if x != 0:
+            the_lastOne = filedate[x - 1][0:7]
+            print(the_lastOne)
+        if the_lastOne == filedate[x][0:7]:
+            sum += data.values[x]
+            print(sum)
+        else:
+            array_ofSum.append(sum)
+            sum = 0
+            print('skip')
+    return [array_ofSum,UniqList]
+
+def TestRatioWeekly(country):
+    tests_done = GetCleanData(True,country,'tests_done').values
+    new_cases = GetCleanData(True,country,'new_cases').values
+    Ratio =[]
+
+    # print(len(tests_done),len(new_cases))
+    for i in range(len(tests_done)):
+        if tests_done[i]!=0:
+            Ratio.append(new_cases[i]/tests_done[i])
+
+
+    xpoints = list(range(len(Ratio)))
+    print(xpoints,Ratio)
+    return[Ratio,xpoints]
+
+def SumofTestMonthly(country):
+    date_list = GetDatesFromFile1(country)
+    the_lastOne = ''
+    filtersDate = []
+    for x in date_list:
+        filtersDate.append(x[0:7])
+    UniqList = set(filtersDate)
+    UniqList = list(UniqList)
+    UniqList.sort()
+    # print(UniqList)
+    data = GetCleanData(True, country, 'tests_done')
+    filedate = GetDatesFromFile1(country)
+    array_ofSum = []
+    # print(data.values)
+    # print(filedate)
+    sum = 0
+    for x in range(len(data)):
+        # print(x,data.values[x],filedate[x][0:7],UniqList[x])
+        print(x, data.values[x], filedate[x][0:7])
+        if x != 0:
+            the_lastOne = filedate[x - 1][0:7]
+            print(the_lastOne)
+        if the_lastOne == filedate[x][0:7]:
+            sum += data.values[x]
+            print(sum)
+        else:
+            array_ofSum.append(sum)
+            sum = 0
+            print('skip')
+
+    # xpoints = list(range(len(Sweeden.values)))
+    # colors = ['red', 'green']
+    # plt.bar(UniqList, array_ofSum, color=colors)
+    return[array_ofSum,UniqList]
+
+def SumOfDeathRatioEurop(country):
+    # Death=GetCleanData(False,country,'value')
+    AllDeath=SetUpFile(False)
+    AllDeath=AllDeath['value']
+    print(AllDeath)
+    print(len(AllDeath.keys().values))
+    print(len(AllDeath.values))
+    OurSum=0
+    AllEurop=0;
+    for i in range(len(AllDeath.values)):
+        if AllDeath.keys().values[i]==country:
+            OurSum+=AllDeath.values[i]
+        AllEurop+=AllDeath.values[i]
+    # print(OurSum,AllEurop,round(OurSum/AllEurop,3))
+    return[OurSum,AllEurop,round(OurSum/AllEurop,3)*100]
+
+def calculate_country_population_ratio(country):
+    data = SetUpFile(True)
+    country_population = data.loc[data.index == country, 'population'].sum()
+    total_population = data['population'].sum()
+    ratio = country_population / total_population
+    print(ratio)
+    return ratio
+def getPopulationBycountry(country):
+    population=GetCleanData(True,country,'population')
+    return population[0]
+def display_Dashborad(country):
+
+
+    resPie=DeathPieYears(country)
+    resPlotMonth=PostiveSumbyMonth(country)
+    resplotTestRatio=TestRatioWeekly(country)
+    barData=SumofTestMonthly(country)
+    resForTextDeath=SumOfDeathRatioEurop(country)
+    resPop=getPopulationBycountry(country)
+    resPopR=calculate_country_population_ratio(country)
+    # Initialise the subplot function using number of rows and columns
+    figure, axis = plt.subplots(2, 2)
+
+    # For Sine Function
+    axis[0, 0].plot(resPlotMonth[1], resPlotMonth[0],color='r')
+    axis[0, 0].tick_params(axis='x', rotation=-90)
+    # plt.xticks(rotation=-90)
+    axis[0, 0].set_title("Sum of postive cases in "+country+" by month and year")
+
+    # For Cosine Function
+    axis[1, 1].plot(resplotTestRatio[1], resplotTestRatio[0],color='y')
+    # axis[1, 1].set_title("Ratio test done / new cases in "+country+" weekly")
+
+    # For Tangent Function
+    axis[1, 0].pie(resPie[0],labels=resPie[1],autopct='%1.1f%%')
+    # axis[0, 0].set_title("Death in "+country+" by years")
+
+    # For Tanh Function
+    axis[0, 1].bar(barData[1], barData[0])
+    axis[0, 1].set_title("Sum of Test in "+country+" by month")
+    axis[0, 1].tick_params(axis='x', rotation=-90)
+
+    # axis[2,2].text(0,0,"This is the Avarge",fontsize=300)
+    axis[-1,0].text(-3.95,0.2,"Sum of death in Europ: "+str(round(resForTextDeath[1]))+"\nThe sum of death in "+country+": "+str(round(resForTextDeath[0]))+"\nThe ratio is: "+str(round(resForTextDeath[2],3))+"%",fontsize=14, color='r')
+    axis[-1,0].text(-3.95,-0.4,"The population in "+country+": "+str(round(resPop))+"\n The Ratio vs All Europ: "+str(round(resPopR*100,4))+" %",fontsize=14, color='g')
+    axis[-1,0].text(4.4,-1.7,"Test Ratio (test done / new cases) in "+country,fontsize=18, color='black')
+    axis[-1, 0].text(-1.2, -1.7, "Sum Of Death in "+ country+" By Years", fontsize=18, color='black')
+
+
+    # Combine all the operations and display
+    plt.show()
+
+
+
+# ----------------------------------------------------------------------------------------------------------------------------
+# Main interFace
+# ----------------------------------------------------------------------------------------------------------------------------
+
+
+
+
+def GetUniqCountry():
+    keys=SetUpFile(True)
+    setkeys=set(keys.index)
+    listkeys = list(setkeys)
+    return listkeys
+
+def App(fn,country1,country2,country3,year,N):
+    if fn == 1:
+        Display_Test_Rate(country1)
+    elif fn == 2:
+        Display_postive_rate(country1)
+    elif fn == 3:
+        Display_SumOfTest(country1)
+    elif fn == 4:
+        Display_SumOfPostive(country1)
+    elif fn == 5:
+        Display_Death(country1)
+    elif fn == 6:
+        Display_DeathPie(country1)
+    elif fn == 7:
+        Display_TestRatio(country1)
+    elif fn == 8:
+        Display_DeathAvrage(country1)
+    elif fn == 9:
+        Display_freqSource(country1)
+    elif fn == 10:
+        Display_NegtiveTest(country1)
+    elif fn == 11:
+        Compare_avg_pr_by_two_countries(year,country1,country2)
+    elif fn == 12:
+        Compare_positive_rate(year,country1,country2)
+    elif fn == 13:
+        Compare_deaths_based_indicator_in_a_given_year(year)
+    elif fn == 14:
+        compare_new_cases(country1,country2,year)
+    elif fn == 15:
+        Display_Tests_Done_Comparation(country1,country2)
+    elif fn == 16:
+        plot_top_months_with_deaths(year,N)
+    elif fn == 17:
+        Display_TheMostTestAmount(year,N)
+    elif fn == 18:
+        Display_popdeath_ratio(year,N)
+    elif fn == 19:
+        DisplayTestRatio(country1,country2,year)
+    elif fn == 20:
+        compar_Death_between_three_countries(country1,country2,country3)
+    else:
+        display_Dashborad(country1)
+
+
+
+
+
+def interFaceLaunch():
+    demo = gr.Interface(fn=App, inputs=[gr.Dropdown([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,'Dashboard'],label='function number'),gr.Dropdown(GetUniqCountry(),label='Countries1'),gr.Dropdown(GetUniqCountry(),label='Countries2'),gr.Dropdown(GetUniqCountry(),label='Countries3'),gr.Dropdown(['2020','2021','2022','2023'],label='Year'),gr.Dropdown(list(range(len(GetUniqCountry()))),label='N')], outputs="plot")
+    demo.launch()
+
+interFaceLaunch()
+
+