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update
Browse files- app.py → Home.py +0 -0
- data.csv +0 -101
- pages/Help.py +68 -0
app.py → Home.py
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data.csv
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0,First Name,Last Name,Gender,Country,Age,Date,Id
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2 |
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1,Dulce,Abril,Female,United States,32,15/10/2017,1562
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3 |
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2,Mara,Hashimoto,Female,Great Britain,25,16/08/2016,1582
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4 |
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3,Philip,Gent,Male,France,36,21/05/2015,2587
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5 |
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4,Kathleen,Hanner,Female,United States,25,15/10/2017,3549
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6 |
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5,Nereida,Magwood,Female,United States,58,16/08/2016,2468
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7 |
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6,Gaston,Brumm,Male,United States,24,21/05/2015,2554
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8 |
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7,Etta,Hurn,Female,Great Britain,56,15/10/2017,3598
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9 |
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8,Earlean,Melgar,Female,United States,27,16/08/2016,2456
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10 |
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9,Vincenza,Weiland,Female,United States,40,21/05/2015,6548
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11 |
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10,Fallon,Winward,Female,Great Britain,28,16/08/2016,5486
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12 |
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11,Arcelia,Bouska,Female,Great Britain,39,21/05/2015,1258
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13 |
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12,Franklyn,Unknow,Male,France,38,15/10/2017,2579
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14 |
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13,Sherron,Ascencio,Female,Great Britain,32,16/08/2016,3256
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15 |
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14,Marcel,Zabriskie,Male,Great Britain,26,21/05/2015,2587
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16 |
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15,Kina,Hazelton,Female,Great Britain,31,16/08/2016,3259
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17 |
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16,Shavonne,Pia,Female,France,24,21/05/2015,1546
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18 |
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17,Shavon,Benito,Female,France,39,15/10/2017,3579
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19 |
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18,Lauralee,Perrine,Female,Great Britain,28,16/08/2016,6597
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20 |
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19,Loreta,Curren,Female,France,26,21/05/2015,9654
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21 |
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20,Teresa,Strawn,Female,France,46,21/05/2015,3569
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22 |
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21,Belinda,Partain,Female,United States,37,15/10/2017,2564
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23 |
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22,Holly,Eudy,Female,United States,52,16/08/2016,8561
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24 |
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23,Many,Cuccia,Female,Great Britain,46,21/05/2015,5489
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25 |
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24,Libbie,Dalby,Female,France,42,21/05/2015,5489
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26 |
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25,Lester,Prothro,Male,France,21,15/10/2017,6574
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27 |
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26,Marvel,Hail,Female,Great Britain,28,16/08/2016,5555
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28 |
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27,Angelyn,Vong,Female,United States,29,21/05/2015,6125
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29 |
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28,Francesca,Beaudreau,Female,France,23,15/10/2017,5412
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30 |
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29,Garth,Gangi,Male,United States,41,16/08/2016,3256
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31 |
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30,Carla,Trumbull,Female,Great Britain,28,21/05/2015,3264
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32 |
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31,Veta,Muntz,Female,Great Britain,37,15/10/2017,4569
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33 |
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32,Stasia,Becker,Female,Great Britain,34,16/08/2016,7521
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34 |
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33,Jona,Grindle,Female,Great Britain,26,21/05/2015,6458
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35 |
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34,Judie,Claywell,Female,France,35,16/08/2016,7569
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36 |
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35,Dewitt,Borger,Male,United States,36,21/05/2015,8514
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37 |
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36,Nena,Hacker,Female,United States,29,15/10/2017,8563
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38 |
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37,Kelsie,Wachtel,Female,France,27,16/08/2016,8642
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39 |
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38,Sau,Pfau,Female,United States,25,21/05/2015,9536
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40 |
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39,Shanice,Mccrystal,Female,United States,36,21/05/2015,2567
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41 |
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40,Chase,Karner,Male,United States,37,15/10/2017,2154
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42 |
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41,Tommie,Underdahl,Male,United States,26,16/08/2016,3265
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43 |
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42,Dorcas,Darity,Female,United States,37,21/05/2015,8765
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44 |
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43,Angel,Sanor,Male,France,24,15/10/2017,3259
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45 |
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44,Willodean,Harn,Female,United States,39,16/08/2016,3567
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46 |
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45,Weston,Martina,Male,United States,26,21/05/2015,6540
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47 |
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46,Roma,Lafollette,Female,United States,34,15/10/2017,2654
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48 |
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47,Felisa,Cail,Female,United States,28,16/08/2016,6525
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49 |
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48,Demetria,Abbey,Female,United States,32,21/05/2015,3265
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50 |
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49,Jeromy,Danz,Male,United States,39,15/10/2017,3265
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51 |
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50,Rasheeda,Alkire,Female,United States,29,16/08/2016,6125
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52 |
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51,Dulce,Abril,Female,United States,32,15/10/2017,1562
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53 |
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52,Mara,Hashimoto,Female,Great Britain,25,16/08/2016,1582
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54 |
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53,Philip,Gent,Male,France,36,21/05/2015,2587
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55 |
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54,Kathleen,Hanner,Female,United States,25,15/10/2017,3549
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56 |
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55,Nereida,Magwood,Female,United States,58,16/08/2016,2468
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57 |
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56,Gaston,Brumm,Male,United States,24,21/05/2015,2554
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58 |
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57,Etta,Hurn,Female,Great Britain,56,15/10/2017,3598
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59 |
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58,Earlean,Melgar,Female,United States,27,16/08/2016,2456
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60 |
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59,Vincenza,Weiland,Female,United States,40,21/05/2015,6548
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61 |
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60,Fallon,Winward,Female,Great Britain,28,16/08/2016,5486
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62 |
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61,Arcelia,Bouska,Female,Great Britain,39,21/05/2015,1258
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63 |
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62,Franklyn,Unknow,Male,France,38,15/10/2017,2579
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64 |
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63,Sherron,Ascencio,Female,Great Britain,32,16/08/2016,3256
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65 |
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64,Marcel,Zabriskie,Male,Great Britain,26,21/05/2015,2587
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66 |
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65,Kina,Hazelton,Female,Great Britain,31,16/08/2016,3259
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67 |
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66,Shavonne,Pia,Female,France,24,21/05/2015,1546
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68 |
-
67,Shavon,Benito,Female,France,39,15/10/2017,3579
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69 |
-
68,Lauralee,Perrine,Female,Great Britain,28,16/08/2016,6597
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70 |
-
69,Loreta,Curren,Female,France,26,21/05/2015,9654
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71 |
-
70,Teresa,Strawn,Female,France,46,21/05/2015,3569
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72 |
-
71,Belinda,Partain,Female,United States,37,15/10/2017,2564
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73 |
-
72,Holly,Eudy,Female,United States,52,16/08/2016,8561
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74 |
-
73,Many,Cuccia,Female,Great Britain,46,21/05/2015,5489
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75 |
-
74,Libbie,Dalby,Female,France,42,21/05/2015,5489
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76 |
-
75,Lester,Prothro,Male,France,21,15/10/2017,6574
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77 |
-
76,Marvel,Hail,Female,Great Britain,28,16/08/2016,5555
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78 |
-
77,Angelyn,Vong,Female,United States,29,21/05/2015,6125
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79 |
-
78,Francesca,Beaudreau,Female,France,23,15/10/2017,5412
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80 |
-
79,Garth,Gangi,Male,United States,41,16/08/2016,3256
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81 |
-
80,Carla,Trumbull,Female,Great Britain,28,21/05/2015,3264
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82 |
-
81,Veta,Muntz,Female,Great Britain,37,15/10/2017,4569
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83 |
-
82,Stasia,Becker,Female,Great Britain,34,16/08/2016,7521
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84 |
-
83,Jona,Grindle,Female,Great Britain,26,21/05/2015,6458
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85 |
-
84,Judie,Claywell,Female,France,35,16/08/2016,7569
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86 |
-
85,Dewitt,Borger,Male,United States,36,21/05/2015,8514
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87 |
-
86,Nena,Hacker,Female,United States,29,15/10/2017,8563
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88 |
-
87,Kelsie,Wachtel,Female,France,27,16/08/2016,8642
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89 |
-
88,Sau,Pfau,Female,United States,25,21/05/2015,9536
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90 |
-
89,Shanice,Mccrystal,Female,United States,36,21/05/2015,2567
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91 |
-
90,Chase,Karner,Male,United States,37,15/10/2017,2154
|
92 |
-
91,Tommie,Underdahl,Male,United States,26,16/08/2016,3265
|
93 |
-
92,Dorcas,Darity,Female,United States,37,21/05/2015,8765
|
94 |
-
93,Angel,Sanor,Male,France,24,15/10/2017,3259
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95 |
-
94,Willodean,Harn,Female,United States,39,16/08/2016,3567
|
96 |
-
95,Weston,Martina,Male,United States,26,21/05/2015,6540
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97 |
-
96,Roma,Lafollette,Female,United States,34,15/10/2017,2654
|
98 |
-
97,Felisa,Cail,Female,United States,28,16/08/2016,6525
|
99 |
-
98,Demetria,Abbey,Female,United States,32,21/05/2015,3265
|
100 |
-
99,Jeromy,Danz,Male,United States,39,15/10/2017,3265
|
101 |
-
100,Rasheeda,Alkire,Female,United States,29,16/08/2016,6125
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pages/Help.py
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# Import required libraries
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import streamlit as st
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# Set Streamlit page configuration
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st.set_page_config(page_title='Help - XLS Office Documents Analysis with ChatGPT4 NLP Model',
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page_icon=":memo:",
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layout='wide',
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initial_sidebar_state='collapsed')
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# Set CSS properties for HTML components
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st.markdown("""
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<style>
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body {
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color: #fff;
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background-color: #4f8bf9;
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}
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h1, h2 {
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color: #ffdd00;
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}
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</style>
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""", unsafe_allow_html=True)
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# Use HTML in markdown to center align the title
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st.markdown("""
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<h1 style='text-align: center; color: #ffdd00;'>Help Document for XLS Office Documents Analysis with ChatGPT4 NLP Model</h1>
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""", unsafe_allow_html=True)
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# Display authorship details
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st.markdown("""
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## Developed by Falah.G.Salieh
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* AI Developer
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* Specialized in Natural Language Processing
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""", unsafe_allow_html=True)
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# Display help content
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st.markdown("""
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## Getting Started
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This project aims to ...
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Here's how to use this app:
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1. Step 1: ...
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2. Step 2: ...
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3. Step 3: ...
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Please note that ...
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### Uploading Your XLS File
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To upload your XLS file, ...
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### Using the ChatGPT-4 NLP Model
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To use the ChatGPT-4 NLP model, ...
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### Understanding the Results
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When you receive the results, ...
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### Troubleshooting
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If you encounter any problems, ...
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## Contact
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If you have any questions, feel free to ...
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""", unsafe_allow_html=True)
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