diff --git "a/app.py" "b/app.py"
new file mode 100644--- /dev/null
+++ "b/app.py"
@@ -0,0 +1,3511 @@
+import streamlit as st
+import pandas as pd
+import pickle
+import matplotlib.pyplot as plt
+import numpy as np
+import altair as alt
+import plotly.graph_objects as go
+import plotly.express as px
+import statistics as stat
+from streamlit_echarts import st_echarts
+from streamlit_pandas_profiling import st_profile_report
+from st_aggrid import AgGrid
+# import pandas_profiling
+from streamlit_vega_lite import vega_lite_component, altair_component
+from statistics import mean
+from datetime import datetime
+import copy
+st.set_page_config(
+ page_title="Real Time Social Medias Analysis",
+ page_icon="🧊",
+ layout="wide",
+ initial_sidebar_state="expanded",
+ menu_items={
+ 'Get Help':None,
+ 'Report a bug': None,
+ 'About': "# This app gather social medias posts of 31 companies all around the world and then analysing it."
+ }
+ )
+
+
+
+############ functions needed inside st
+def make_clickable(link):
+ if link!="":
+ if "=" in link:
+ text = link.split('=')[0]
+ linki=link.split('=')[1]
+ return f'{text}'
+ else:
+ return link
+ else:
+ return ""
+
+
+
+
+def check_password():
+ """Returns `True` if the user had the correct password."""
+
+ def password_entered():
+
+ """Checks whether a password entered by the user is correct."""
+ if st.session_state["password"] == "etka":
+ st.session_state["password_correct"] = True
+ del st.session_state["password"] # don't store password
+ else:
+ st.session_state["password_correct"] = False
+
+ if "password_correct" not in st.session_state:
+ # First run, show input for password.
+ st.text_input(
+ "Password", type="password", on_change=password_entered, key="password"
+ )
+ return False
+ elif not st.session_state["password_correct"]:
+ # Password not correct, show input + error.
+ st.text_input(
+ "Password", type="password", on_change=password_entered, key="password"
+ )
+ st.error("😕 Password incorrect")
+ return False
+ else:
+ # Password correct.
+ return True
+jkl=False
+if check_password():
+ jkl=True
+
+national_company_name_fa=['افق کوروش', 'جانبو', 'رفاه', 'فروشگاه هفت', 'هایپر استار', 'هایپر می', 'توی توی', 'شهروند', 'یاس', 'سورنا', 'وین مارکت', 'کیوان', 'فروشگاه سروش', 'سبوس', 'دیجی کالا', 'اکالا', 'ایمالز', 'اسنپ مارکت', 'فروشگاه کاله']
+international_company_name_fa=['والمارت', 'کروگر', 'کاستکو', 'بیم مارکت', 'میگروس', 'ساوکو', 'لولو هاپر مارکت', 'گروه یونیون', 'ادکا', 'آلدی', 'کارفور', 'بیلا راشا']
+company_name1_fa=national_company_name_fa+international_company_name_fa
+
+insta_accounts=['okcs.ir','canbo.ir','myrefah','haftstores','hyperstar_ir','hyperme.chainstores','toytoy.ir','shahrvand','yassgroup.official','surena.cs','winmarketco','k1megastore','soroush.chain.store','saboosgroup','digikalacom','okalaclub','emalls_site','snappmarket','kallehbrand','walmart','krogerco','costco','bimturkiye','migros_tr','saveco','luluhypermarkets','union.coop','edeka','aldiusa','carrefour_iraq','billarussia']
+company_name=['Ofogh Kouroush','Canbo','Refah','Haft Stores','Hyperstar','Hyper Me','Toy Toy','Shahrvand','Yaas','Surena','Win Market','K 1','Soroush Chain Store','Saboos Group','DigiKala','Okala','Emalls','Snapp Market','Kalleh Store','Walmart','Kroger','Costco','Bim','Migros','Save','LuLu Hypermarkets','Union','Edeka','Aldi','Carrefour','Billa Russia']
+company_name1=['Ofogh Kouroush','Canbo','Refah','Haft Stores','Hyperstar','Hyper Me','Toy Toy','Shahrvand','Yaas','Surena','Win Market','K 1','Soroush Chain Store','Saboos Group','DigiKala','Okala','Emalls','Snapp Market','Kalleh Store','Walmart','Kroger','Costco','Bim','Migros','Save','LuLu Hypermarkets','Union','Edeka','Aldi','Carrefour','Billa Russia']
+company_name2=['Ofogh_Kouroush','Canbo','Refah','Haft_Stores','Hyperstar','Hyper_Me','Toy_Toy','Shahrvand','Yaas','Surena','Win_Market','K_1','Soroush_Chain_Store','Saboos_Group','DigiKala','Okala','Emalls','Snapp_Market','Kalleh_Store','Walmart','Kroger','Costco','Bim','Migros','Save','LuLu_Hypermarkets','Union','Edeka','Aldi','Carrefour','Billa_Russia']
+national_insta_accounts = ['okcs.ir', 'canbo.ir', 'myrefah', 'haftstores', 'hyperstar_ir', 'hyperme.chainstores', 'toytoy.ir', 'shahrvand', 'yassgroup.official', 'surena.cs', 'winmarketco', 'k1megastore', 'soroush.chain.store', 'saboosgroup', 'digikalacom', 'okalaclub', 'emalls_site', 'snappmarket', 'kallehbrand']
+international_insta_accounts = ['walmart', 'krogerco', 'costco', 'bimturkiye', 'migros_tr', 'saveco', 'luluhypermarkets', 'union.coop', 'edeka', 'aldiusa', 'carrefour_iraq', 'billarussia']
+national_company_name = ['Ofogh Kouroush', 'Canbo', 'Refah', 'Haft Stores', 'Hyperstar', 'Hyper Me', 'Toy Toy', 'Shahrvand', 'Yaas', 'Surena', 'Win Market', 'K 1', 'Soroush Chain Store', 'Saboos Group', 'DigiKala', 'Okala', 'Emalls', 'Snapp Market', 'Kalleh Store']
+international_company_name = ['Walmart', 'Kroger', 'Costco', 'Bim', 'Migros', 'Save', 'LuLu Hypermarkets', 'Union', 'Edeka', 'Aldi', 'Carrefour', 'Billa Russia']
+national_company_name2 = ['Ofogh_Kouroush', 'Canbo', 'Refah', 'Haft_Stores', 'Hyperstar', 'Hyper_Me', 'Toy_Toy', 'Shahrvand', 'Yaas', 'Surena', 'Win_Market', 'K_1', 'Soroush_Chain_Store', 'Saboos_Group', 'DigiKala', 'Okala', 'Emalls', 'Snapp_Market', 'Kalleh_Store']
+international_company_name2 = ['Walmart', 'Kroger', 'Costco', 'Bim', 'Migros', 'Save', 'LuLu_Hypermarkets', 'Union', 'Edeka', 'Aldi', 'Carrefour', 'Billa_Russia']
+ide = [3145372723, 2965548667, 1988671655, 2197031285, 2233092127, 7485093484, 1181917352, 547966386, 2104826426, 7706142910, 7621001555, 4517720528, 1682386117, 1734407253, 1434739621, 6223711957, 1688949901, 7625600229, 536309671, 391362994, 1372092025, 1286044267, 6428046303, 324691922, 193450010, 1109269555, 1969219287, 1501689572, 525355730, 1494499024, 3646541728]
+ide_national = [3145372723, 2965548667, 1988671655, 2197031285, 2233092127, 7485093484, 1181917352, 547966386, 2104826426, 7706142910, 7621001555, 4517720528, 1682386117, 1734407253, 1434739621, 6223711957, 1688949901, 7625600229, 536309671]
+ide_international =[391362994, 1372092025, 1286044267, 6428046303, 324691922, 193450010, 1109269555, 1969219287, 1501689572, 525355730, 1494499024, 3646541728]
+Website=['https://www.okcs.com','http://canbo.ir','http://refah.ir','http://www.7haftstores.com/Pages/index.aspx','https://www.hyperstariran.com','https://www.hyperme.ir','https://link.toytoy.ir/','https://www.shahrvand.ir','https://www.yassgroup.ir','https://surenacs.com','http://winmarketco.com','https://k1stores.com/?page_id=116','https://supersoroush.com','https://saboosgroup.ir','https://www.digikala.com','https://okala.com','https://emalls.ir','https://snapp.market','https://gilan.kalleh.com','https://www.walmart.com','https://www.kroger.com','https://www.costco.ca','https://www.bim.com.tr/Categories/104/magazalar.aspx','https://www.migros.com.tr','https://www.saveco.com','https://www.luluhypermarket.com/en-ae/','https://corporate.unioncoop.ae/en/online-shopping/','https://www.edeka.de','https://www.aldi.us','https://www.carrefouriraq.com','https://www.billa.ru']
+Instag=['https://www.instagram.com/okcs.ir/','https://www.instagram.com/canbo.ir/','https://www.instagram.com/myrefah/','https://www.instagram.com/haftstores/','https://www.instagram.com/hyperstar_ir/','https://www.instagram.com/hyperme.chainstores/','https://www.instagram.com/khaneh_kashaneh/','https://www.instagram.com/shahrvandeshop/','https://www.instagram.com/yassgroup.official/','https://www.instagram.com/surena.cs/','https://www.instagram.com/winmarketco/','https://www.instagram.com/k1megastore/','https://www.instagram.com/soroush.chain.store','https://www.instagram.com/saboosgroup/?hl=en','https://www.instagram.com/digikalacom/','https://www.instagram.com/okalaclub/','https://www.instagram.com/emalls_site/','https://www.instagram.com/snappmarket/','https://www.instagram.com/kallehbrand/','https://www.instagram.com/walmart/','https://www.instagram.com/krogerco/?hl=en','https://www.instagram.com/costco/?hl=en','https://www.instagram.com/bimturkiye/?hl=tr','https://www.instagram.com/migrossanalmarket/','https://www.instagram.com/saveco/','https://www.instagram.com/luluhypermarkets/','https://www.instagram.com/union.coop/','https://www.instagram.com/edeka/','https://www.instagram.com/aldiusa/','https://www.instagram.com/carrefour_iraq/','https://www.instagram.com/billarussia/']
+facebo=['https://www.facebook.com/ofoghkooroshstores','','','','','','','','','','https://www.facebook.com/winmarketco','https://www.facebook.com/profile.php?id=100030590948072','','','','','','','','https://www.facebook.com/walmart','https://www.facebook.com/Kroger/','https://www.facebook.com/CostcoCanada','https://www.facebook.com/bimturkiye','https://www.facebook.com/MSanalMarket','https://www.facebook.com/Savecoq8/','https://www.facebook.com/LuLuHypermarket/?brand_redir=1665762276970506','https://www.facebook.com/union.cooperative/','https://www.facebook.com/EDEKA','https://www.facebook.com/ALDI.USA','https://www.facebook.com/Carrefour.Iraq/','https://www.facebook.com/billa.ru']
+twitt=['https://twitter.com/ofoghkoorosh','','','','','','','','','','https://twitter.com/winmarketco1/','https://twitter.com/K1Megastore','','','https://twitter.com/digikalacom','','','','','https://twitter.com/Walmart','https://twitter.com/Kroger','','','https://twitter.com/MSanalMarket','https://twitter.com/savecoQ8','https://twitter.com/luluhypr','https://twitter.com/UnionCoop','https://twitter.com/AldiUSA','','','']
+youtu=['','','','','','','','','','','','','','','','','','','','https://www.youtube.com/user/Walmart','https://www.youtube.com/user/kroger','','https://www.youtube.com/channel/UClAHcndi08TXV0U3osnhQAQ/featured','https://www.youtube.com/user/migrossanalmarket/','https://www.youtube.com/channel/UCfEyO6rTOeca5SUeB3Wuw2Q','https://www.youtube.com/user/luluhypermarkets','https://www.youtube.com/user/unioncoop','https://www.youtube.com/user/aldiusa?feature=watch','https://www.youtube.com/user/aldiusa?feature=watch','https://www.youtube.com/user/CarrefourIraq/videos','https://www.youtube.com/channel/UCSx84zsrpnikJX2PDnOYbMA?sub_confirmation=1']
+pintres=['','','','','','','','','','','','','','','','','','','','https://www.pinterest.de/walmart/','https://www.pinterest.com/krogerco/','','','','','','https://www.pinterest.de/edeka/','https://www.pinterest.de/aldiusa/','','','']
+Linkdi=['https://www.linkedin.com/checkpoint/challengesV2/AQHGa5p3pWbEFgAAAXLmpWYtwLd1QPiyxE9LOCOU9YHEaZPoxyjNMuqSC2L4KiNIl-X079FNwycCwiZrh5wcGQYo-ansLaV8zg','https://www.linkedin.com/company/everest-modern-pars-chain-stores','','https://www.linkedin.com/company/oshanak-chain-stores-company-human-resources-department-','','','','','','','https://www.linkedin.com/company/winmarketco/','','','','https://www.linkedin.com/company/digikala/','','','','','','','','','','https://www.linkedin.com/authwall?trk=ripf&trkInfo=AQETU2kUtBRQNQAAAXLm9bl4bQDBlvDIfUZxUa_dLtjv3tqtxQfJEqEOxPkGITmOwZEh0VBky4JTEPPI2K-JLjqlevFNqsv1NweE9feyVANdc3JGzGQkZ2kSVeBnXF8wsBf4gFc=&originalReferer=&sessionRedirect=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fsaveco','','https://www.linkedin.com/authwall?trk=ripf&trkInfo=AQHx-TF6N6AmVQAAAXLm-34II5OTK0P5oy4dX42TB2T2yQSTNcgCIa4u3B00JR2NJtdKGbQq4MTAX6o-vlhxIN8kUcHA8ICQPIA2g5qGTmqNTwypN3CIhy6Gm-XG5xi-1HkJQVU=&originalReferer=&sessionRedirect=https%3A%2F%2Fae.linkedin.com%2Fin%2Funioncoop','','','','']
+Telegra=['https://t.me/okclub','https://telegram.me/canbo','http://telegram.me/myrefah','https://telegram.me/HAFTSTORESS','','https://telegram.me/hyperme_ir','https://t.me/hyperfamili','','','','http://telegram.me/winmarketco','https://t.me/k1megastore','','https://t.me/saboosgroup','','https://t.me/okala_club1/','','','','','','','','','','','','','','','']
+Whatsap=['','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','']
+Soru=['','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','']
+Bale=['','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','']
+igap=['','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','']
+aparat=['https://www.aparat.com/okclub','','','','https://www.aparat.com/Hyperstariran','','','https://www.aparat.com/shahrvand.ir','','https://www.aparat.com/surenacs','https://www.aparat.com/winmarketco','https://www.aparat.com/k1megastore','','','https://www.aparat.com/digikala/دیجی_کالا','https://www.aparat.com/okala/','','','','','','','','','','','','','','','']
+robica=['','','','','','','','','','','','','','','','','','','','','','','','','','','','','','','']
+
+
+
+
+kindii=["Takhfif","Jashn","Mosabeghe","Mahsol"]
+kindii_fa=["تخفیف","جشنواره","مسابقه","ارتقای محصول"]
+
+
+
+
+
+########## load the dataset
+data=pickle.load(open("data/data.pkl","rb"))
+
+
+######## make sheet 1
+
+
+
+
+company=national_company_name_fa+["کل","٪ شرکت ها","میانگین حساب کاربری هر شرکت"]
+instagram=["*" for i in range(19)]+["19","100",""]
+facebook=["*" if i==0 or i==10 or i==11 else "" for i in range(19)]+["3","15.79",""]
+twitter=["*" if i==0 or i==10 or i==11 or i==14 else "" for i in range(19)]+["4","21.05",""]
+linkedin=["*" if i==0 or i==10 or i==1 or i==14 or i==3 else "" for i in range(19)]+["5","26.32",""]
+telegram=["*" if i<=3 or i==5 or i==6 or i==10 or i==11 or i==13 or i==15 else "" for i in range(19)]+["10","52.63",""]
+Aparat=["*" if i==0 or i==4 or i==7 or i==10 or i==9 or i==10 or i==15 or i==14 else "" for i in range(19)]+["8","42.11",""]
+sorosh=["" for i in range(19)]+["0","0",""]
+bale=["" for i in range(19)]+["0","0",""]
+igap=["" for i in range(19)]+["0","0",""]
+robica=["" for i in range(19)]+["0","0",""]
+total=['6', '3', '2', '3', '2', '2', '2', '2', '1', '2', '6', '5', '1', '2', '4', '3', '1', '1', '1', '49', '', '']
+
+website_national=[f"*={Website[i]}" if Website[i]!="" else "" for i in range(19)]+["19","100","2.58"]
+insta_national=[f"*={Instag[i]}" if Instag[i]!="" else "" for i in range(19)]+["19","100",""]
+facebook_national=[f"*={facebo[i]}" if facebo[i]!="" else "" for i in range(19)]+["3","15.79",""]
+
+twitter_national=[f"*={twitt[i]}" if twitt[i]!="" else "" for i in range(19)]+["4","21.05",""]
+
+linkedin_natioanl=[f"*={Linkdi[i]}" if Linkdi[i]!="" else "" for i in range(19)]+["5","26.32",""]
+
+aparat_national=[f"*={aparat[i]}" if aparat[i]!="" else "" for i in range(19)]+["8","42.11",""]
+
+telegram_national=[f"*={Telegra[i]}" if Telegra[i]!="" else "" for i in range(19)]+["10","52.63",""]
+
+
+
+sheet1=pd.DataFrame(data={"شرکت ها":company ,"وب سایت":website_national,"اینستاگرام":insta_national,"فیسبوک":facebook_national,
+"توییتر":twitter_national,"لینکدین":linkedin_natioanl,"تلگرام":telegram_national,
+"آپارات":aparat_national,"کل":total
+})
+for j in ["وب سایت","اینستاگرام","فیسبوک","توییتر","لینکدین","تلگرام","آپارات"]:
+ sheet1[j]=sheet1[j].apply(make_clickable)
+
+sheet1=sheet1.to_html(escape=False)
+
+
+del company,instagram,facebook,twitter,linkedin,telegram,Aparat,sorosh,bale,igap,robica,total,website_national,insta_national,facebook_national,twitter_national,linkedin_natioanl,aparat_national,telegram_national
+
+
+
+
+
+
+
+sheet7_0data=[6,3,2,3,2,2,2,2,1,2,6,5,1,2,4,3,1,1,1]
+dfsheet0= pd.DataFrame(data={"Companies":national_company_name_fa,"The Number of Accounts":sheet7_0data})
+
+
+
+
+
+company=international_company_name_fa+["کل","٪ شرکتها","میانگین حساب کاربری هر شرکت"]
+instagram=["*" for i in range(12)]+["12","100","2.53"]
+facebook=["*" for i in range(12)]+["12","100",""]
+pintrest=["*" if i<=1 or i==7 or i==8 else "" for i in range(12)]+["4","33.33",""]
+twitter=["*" if i<=1 or (i>=4 and i<=8) else "" for i in range(12)]+["7","58.33",""]
+youtube=["*" if i<=1 or (i>=3 and i<=11) else "" for i in range(12)]+["11","91.67",""]
+linkedin=["*" if i==5 or i==7 else "" for i in range(12)]+["2","16.67",""]
+whatsapp=["" for i in range(12)]+["0","0",""]
+total=['5', '5', '2', '3', '4', '5', '4', '6', '5', '3', '3', '3', '48',"",""]
+
+
+
+insta_international=[f"*={Instag[i+19]}" if Instag[i+19]!="" else "" for i in range(12)]+["12","100",""]
+website_international=[f"*={Website[i+19]}" if Website[i+19]!="" else "" for i in range(12)]+["12","100","2.53"]
+facebook_international=[f"*={facebo[i+19]}" if facebo[i+19]!="" else "" for i in range(12)]+["12","100",""]
+twitter_international=[f"*={twitt[i+19]}" if twitt[i+19]!="" else "" for i in range(12)]+["7","58.33",""]
+linkedin_internatioanl=[f"*={Linkdi[i+19]}" if Linkdi[i+19]!="" else "" for i in range(12)]+["2","16.67",""]
+youtube_international=[f"*={youtu[i+19]}" if youtu[i+19]!="" else "" for i in range(12)]+["11","91.67",""]
+pintrest_international=[f"*={pintres[i+19]}" if pintres[i+19]!="" else "" for i in range(12)]+["4","33.33",""]
+
+
+sheet1_1=pd.DataFrame(data={"شرکت ها":company,"وب سایت":website_international,"اینستاگرام":insta_international,"فیسبوک":facebook_international,"پینترست":pintrest_international,
+
+ "توییتر":twitter_international,"یوتیوب":youtube_international,"لینکدین":linkedin_internatioanl,"کل":total
+ })
+
+for j in ["وب سایت","اینستاگرام","فیسبوک","توییتر","لینکدین","یوتیوب","پینترست"]:
+ sheet1_1[j]=sheet1_1[j].apply(make_clickable)
+sheet1_1=sheet1_1.to_html(escape=False)
+
+del company,instagram,facebook,pintres,twitter,youtube,linkedin,whatsapp,total,insta_international,website_international,facebook_international,twitter_international,linkedin_internatioanl,youtube_international,pintrest_international
+
+
+
+sheet7_0data=[5,5,2,3,4,5,4,6,5,3,3,3]
+dfsheet0_1 = pd.DataFrame(data={"Companies":international_company_name_fa,"The Number of Accounts":sheet7_0data})
+
+
+
+source0_1 = pd.DataFrame({"Popularities": [12,12,4,7,11,2,0],"Social Medias":['Instagram','FaceBook','Pintrest','Twitter','Youtube','LinkedIn','WhatsApp']})
+
+
+
+
+
+
+
+
+
+
+###### make sheet2 dataframe
+
+followers=[]
+following=[]
+posts=[]
+
+for i in national_company_name2:
+ followers.append(data["information"][i]["bio"]["followers"])
+ following.append(data["information"][i]["bio"]["following"])
+ posts.append(data["information"][i]["bio"]["posts_count"])
+sheet2=pd.DataFrame(data={"شرکت ها":national_company_name_fa,"تعداد دنبال کنندگان":followers,"تعداد دنبال کرده ها":following,"تعداد پست ها":posts})
+sheet2bar=pd.DataFrame(data={"Companies":national_company_name_fa,"Followers":followers})
+sheet2barpost=pd.DataFrame(data={"Companies":national_company_name_fa,"Number of posts":posts})
+sheet3followers=followers
+
+
+
+
+followers=[]
+following=[]
+posts=[]
+for i in international_company_name2:
+ followers.append(data["information"][i]["bio"]["followers"])
+ following.append(data["information"][i]["bio"]["following"])
+ posts.append(data["information"][i]["bio"]["posts_count"])
+sheet2_1=pd.DataFrame(data={"شرکت ها":international_company_name_fa,"تعداد دنبال کنندگان":followers,"تعداد دنبال کرده ها":following,"تعداد پست ها":posts})
+sheet2bar_1=pd.DataFrame(data={"Companies":international_company_name_fa,"Followers":followers})
+sheet2barpost_1=pd.DataFrame(data={"Companies":international_company_name_fa,"Number of posts":posts})
+sheet3_1followers=followers
+
+
+del followers,following,posts
+
+
+
+
+######### make sheet 3 for national and international companies post analysis
+
+temp=["بیشترین تعداد پسندیده شدن" ,"بیشترین تعداد نظرات","بیشترین تعداد بازدی","میانگین تعداد پسندیده شدن","میانگین تعداد نظرات","میانگین تعداد بازدید","میانگین مشارکت","٪ تصاویر","٪ ویدیو ها","میانگین روزانه تعداد پست","میانگین ساعتی تعداد پست ها"]
+temp2=["max_like","max_comment","max_view","ave_like","ave_comment","ave_view","ave_participation","percent_image","percent_video","ave_post_per_day","ave_post_per_hour"]
+company=[]
+time_horizon=[]
+last_month=[]
+last_three_months=[]
+overal=[]
+tir2=[last_month,last_three_months,overal]
+sheet3like=[[] for i in range(3)]
+sheet3comment=[[] for i in range(3)]
+sheet3view=[[] for i in range(3)]
+sheet3participation=[[] for i in range(3)]
+sheet3image=[[] for i in range(3)]
+sheet3postperday=[[] for i in range(3)]
+tir=["last_month","last_three_months","overal"]
+for i in national_company_name2:
+ index=national_company_name2.index(i)
+ for j in temp:
+ if temp.index(j)==0:
+ company.append(national_company_name_fa[index])
+ else :
+ company.append("")
+ time_horizon.append(j)
+ for j in tir:
+ index=tir.index(j)
+ for t in temp2:
+ if temp2.index(t)==7 or temp2.index(t)==8:
+ tir2[index].append("{:.0f}".format(data["information"][i]["analysis"]["participation"][j][t]*100)+" %")
+ if temp2.index(t)==7 :
+ sheet3image[index].append(data["information"][i]["analysis"]["participation"][j][t])
+ elif temp2.index(t)==9 or temp2.index(t)==10:
+ tir2[index].append("{:.2f}".format(data["information"][i]["analysis"]["participation"][j][t]))
+ if temp2.index(t)==9 :
+ sheet3postperday[index].append(data["information"][i]["analysis"]["participation"][j][t])
+ else:
+ tir2[index].append("{:.0f}".format(data["information"][i]["analysis"]["participation"][j][t]))
+ if t=="ave_like":
+ sheet3like[index].append(data["information"][i]["analysis"]["participation"][j][t])
+ elif t=="ave_comment":
+ sheet3comment[index].append(data["information"][i]["analysis"]["participation"][j][t])
+ elif t=="ave_view":
+ sheet3view[index].append(data["information"][i]["analysis"]["participation"][j][t])
+ elif t=="ave_participation":
+ sheet3participation[index].append(data["information"][i]["analysis"]["participation"][j][t])
+sheet3=pd.DataFrame(data={"شرکت ها":company,"افق زمانی":time_horizon,"یک ماه اخیر":tir2[0],"سه ماه اخیر":tir2[1],"کل":tir2[2]})
+
+
+del company,time_horizon,tir2,last_month,last_three_months,overal
+
+
+
+temp=["بیشترین تعداد پسندیده شدن" ,"بیشترین تعداد نظرات","بیشترین تعداد بازدی","میانگین تعداد پسندیده شدن","میانگین تعداد نظرات","میانگین تعداد بازدید","میانگین مشارکت","٪ تصاویر","٪ ویدیو ها","میانگین روزانه تعداد پست","میانگین ساعتی تعداد پست ها"]
+temp2=["max_like","max_comment","max_view","ave_like","ave_comment","ave_view","ave_participation","percent_image","percent_video","ave_post_per_day","ave_post_per_hour"]
+company=[]
+time_horizon=[]
+last_month=[]
+last_three_months=[]
+overal=[]
+tir2=[last_month,last_three_months,overal]
+sheet3like_1=[[] for i in range(3)]
+sheet3comment_1=[[] for i in range(3)]
+sheet3view_1=[[] for i in range(3)]
+sheet3participation_1=[[] for i in range(3)]
+sheet3image_1=[[] for i in range(3)]
+sheet3postperday_1=[[] for i in range(3)]
+tir=["last_month","last_three_months","overal"]
+for i in international_company_name2:
+ index=international_company_name2.index(i)
+ for j in temp:
+ if temp.index(j)==0:
+ company.append(international_company_name_fa[index])
+ else :
+ company.append("")
+ time_horizon.append(j)
+ for j in tir:
+ index=tir.index(j)
+ for t in temp2:
+ if temp2.index(t)==7 or temp2.index(t)==8:
+ tir2[index].append("{:.0f}".format(data["information"][i]["analysis"]["participation"][j][t]*100)+" %")
+ if temp2.index(t)==7 :
+ sheet3image_1[index].append(data["information"][i]["analysis"]["participation"][j][t])
+ elif temp2.index(t)==9 or temp2.index(t)==10:
+ tir2[index].append("{:.2f}".format(data["information"][i]["analysis"]["participation"][j][t]))
+ if temp2.index(t)==9 :
+ sheet3postperday_1[index].append(data["information"][i]["analysis"]["participation"][j][t])
+ else:
+ tir2[index].append("{:.0f}".format(data["information"][i]["analysis"]["participation"][j][t]))
+ if t=="ave_like":
+ sheet3like_1[index].append(data["information"][i]["analysis"]["participation"][j][t])
+ elif t=="ave_comment":
+ sheet3comment_1[index].append(data["information"][i]["analysis"]["participation"][j][t])
+ elif t=="ave_view":
+ sheet3view_1[index].append(data["information"][i]["analysis"]["participation"][j][t])
+ elif t=="ave_participation":
+ sheet3participation_1[index].append(data["information"][i]["analysis"]["participation"][j][t])
+
+
+sheet3_1=pd.DataFrame(data={"شرکت ها":company,"افق زمانی":time_horizon,"یک ماه اخیر":tir2[0],"سه ماه اخیر":tir2[1],"کل":tir2[2]})
+
+del tir2,company
+
+
+
+
+
+
+
+
+
+#################### sheet 4 hashtag
+
+
+company=[]
+temp=["��یانگین تعداد # ها در هر پست","بیشترین تعداد # در یک پست","کم ترین تعداد # در یک پست","محبوب ترین # های استفاده شده"]
+time_horizon=[]
+tom=[[],[],[]]
+hori=["last_month","last_three_months","overal"]
+avermax=[[[],[],[]],[[],[],[]]]
+kori=["averge_per_post","max_number","min_number","most_common"]
+for i in national_company_name2:
+ for t in temp:
+ if temp.index(t)==0:
+ company.append(national_company_name_fa[national_company_name2.index(i)])
+ else:
+ company.append("")
+
+ time_horizon.append(t)
+ for j in range(3):
+ for c in kori:
+ if c=="most_common":
+
+ if len(data["information"][i]["analysis"]["hashtag"][hori[j]]["all_posts_type"][c]) !=0 :
+ h=""
+ for g in range(len(data["information"][i]["analysis"]["hashtag"][hori[j]]["all_posts_type"][c])):
+ if g!=len(data["information"][i]["analysis"]["hashtag"][hori[j]]["all_posts_type"][c])-1:
+ h+=data["information"][i]["analysis"]["hashtag"][hori[j]]["all_posts_type"][c][g][0]+" - "
+ else:
+ h+=data["information"][i]["analysis"]["hashtag"][hori[j]]["all_posts_type"][c][g][0]
+ tom[j].append(h)
+ else:
+ tom[j].append("")
+ else:
+ tom[j].append("{:.2f}".format(data["information"][i]["analysis"]["hashtag"][hori[j]]["all_posts_type"][c]))
+ if c=="averge_per_post":
+ avermax[0][j].append(data["information"][i]["analysis"]["hashtag"][hori[j]]["all_posts_type"][c])
+ elif c=="max_number":
+ avermax[1][j].append(data["information"][i]["analysis"]["hashtag"][hori[j]]["all_posts_type"][c])
+
+
+
+
+sheet4=pd.DataFrame(data={"شرکتها":company,"افق زمانی":time_horizon,"یک ماه اخیر":tom[0],"سه ماه اخیر":tom[1],"کل":tom[2]})
+capdia40=[]
+for i in range(2):
+ capdia40.append(pd.DataFrame(
+ {
+ "Company":national_company_name_fa,
+ "Last Month":avermax[i][0],
+ "Last Three Months":avermax[i][1],
+ "Overal":avermax[i][2]},
+ columns=['Company', 'Last Month', 'Last Three Months','Overal']
+ ))
+
+
+del h,j,c,t,i,kori,tom,time_horizon,temp,company,avermax
+
+
+
+company=[]
+temp=["میانگین تعداد # ها در هر پست","بیشترین تعداد # در یک پست","کم ترین تعداد # در یک پست","محبوب ترین # های استفاده شده"]
+time_horizon=[]
+tom=[[],[],[]]
+hori=["last_month","last_three_months","overal"]
+avermax=[[[],[],[]],[[],[],[]]]
+kori=["averge_per_post","max_number","min_number","most_common"]
+for i in international_company_name2:
+ for t in temp:
+ if temp.index(t)==0:
+ company.append(international_company_name_fa[international_company_name2.index(i)])
+ else:
+ company.append("")
+
+ time_horizon.append(t)
+ for j in range(3):
+ for c in kori:
+ if c=="most_common":
+
+ if len(data["information"][i]["analysis"]["hashtag"][hori[j]]["all_posts_type"][c]) !=0 :
+ h=""
+ for g in range(len(data["information"][i]["analysis"]["hashtag"][hori[j]]["all_posts_type"][c])):
+ if g!= len(data["information"][i]["analysis"]["hashtag"][hori[j]]["all_posts_type"][c])-1:
+
+ h+=data["information"][i]["analysis"]["hashtag"][hori[j]]["all_posts_type"][c][g][0]+" - "
+ else:
+ h+=data["information"][i]["analysis"]["hashtag"][hori[j]]["all_posts_type"][c][g][0]
+ tom[j].append(h)
+ else:
+ tom[j].append("")
+ else:
+ tom[j].append("{:.2f}".format(data["information"][i]["analysis"]["hashtag"][hori[j]]["all_posts_type"][c]))
+ if c=="averge_per_post":
+ avermax[0][j].append(data["information"][i]["analysis"]["hashtag"][hori[j]]["all_posts_type"][c])
+ elif c=="max_number":
+ avermax[1][j].append(data["information"][i]["analysis"]["hashtag"][hori[j]]["all_posts_type"][c])
+
+
+sheet4_1=pd.DataFrame(data={"شرکتها":company,"افق زمانی":time_horizon,"یک ماه اخیر":tom[0],"سه ماه اخیر":tom[1],"کل":tom[2]})
+capdia4=[]
+for i in range(2):
+ capdia4.append(pd.DataFrame(
+ {
+ "Company":international_company_name_fa,
+ "Last Month":avermax[i][0],
+ "Last Three Months":avermax[i][1],
+ "Overal":avermax[i][2]},
+ columns=['Company', 'Last Month', 'Last Three Months','Overal']
+ ))
+
+
+
+
+del h,j,c,t,i,kori,tom,time_horizon,temp,company,avermax
+
+
+
+
+
+
+############ sheet5 emoji
+
+company=[]
+temp=["میانگین تعداد اموجیها در هر پست","بیشترین تعداد اموجیها در یک پست","کم ترین تعداد اموجیها در یک پست","محبوب ترین اموجیهای استفاده شده"]
+time_horizon=[]
+tom=[[],[],[]]
+hori=["last_month","last_three_months","overal"]
+avermax=[[[],[],[]],[[],[],[]]]
+kori=["averge_per_post","max_number","min_number","most_common"]
+for i in national_company_name2:
+ for t in temp:
+ if temp.index(t)==0:
+ company.append(national_company_name_fa[national_company_name2.index(i)])
+ else:
+ company.append("")
+
+ time_horizon.append(t)
+ for j in range(3):
+ for c in kori:
+ if c=="most_common":
+
+ if len(data["information"][i]["analysis"]["emoji"][hori[j]]["all_posts_type"][c]) !=0 :
+ h=""
+ for g in range(len(data["information"][i]["analysis"]["emoji"][hori[j]]["all_posts_type"][c])):
+ h+=data["information"][i]["analysis"]["emoji"][hori[j]]["all_posts_type"][c][g][0]
+ tom[j].append(h)
+ else:
+ tom[j].append("")
+ else:
+ tom[j].append("{:.2f}".format(data["information"][i]["analysis"]["emoji"][hori[j]]["all_posts_type"][c]))
+ if c=="averge_per_post":
+ avermax[0][j].append(data["information"][i]["analysis"]["emoji"][hori[j]]["all_posts_type"][c])
+ elif c=="max_number":
+ avermax[1][j].append(data["information"][i]["analysis"]["emoji"][hori[j]]["all_posts_type"][c])
+
+sheet5=pd.DataFrame(data={"شرکتها":company,"افق زمانی":time_horizon,"یک ماه اخیر":tom[0],"سه ماه اخیر":tom[1],"کل":tom[2]})
+
+capdia50=[]
+for i in range(2):
+ capdia50.append(pd.DataFrame(
+ {
+ "Company":national_company_name_fa,
+ "Last Month":avermax[i][0],
+ "Last Three Months":avermax[i][1],
+ "Overal":avermax[i][2]},
+ columns=['Company', 'Last Month', 'Last Three Months','Overal']
+ ))
+
+
+
+del h,j,c,t,i,kori,tom,time_horizon,temp,company,avermax
+
+company=[]
+temp=["میانگین تعداد اموجیها در هر پست","بیشترین تعداد اموجیها در یک پست","کم ترین تعداد اموجیها در یک پست","محبوب ترین اموجیهای استفاده شده"]
+time_horizon=[]
+tom=[[],[],[]]
+hori=["last_month","last_three_months","overal"]
+avermax=[[[],[],[]],[[],[],[]]]
+kori=["averge_per_post","max_number","min_number","most_common"]
+for i in international_company_name2:
+ for t in temp:
+ if temp.index(t)==0:
+ company.append(international_company_name_fa[international_company_name2.index(i)])
+ else:
+ company.append("")
+
+ time_horizon.append(t)
+ for j in range(3):
+ for c in kori:
+ if c=="most_common":
+
+ if len(data["information"][i]["analysis"]["emoji"][hori[j]]["all_posts_type"][c]) !=0 :
+ h=""
+ for g in range(len(data["information"][i]["analysis"]["emoji"][hori[j]]["all_posts_type"][c])):
+ h+=data["information"][i]["analysis"]["emoji"][hori[j]]["all_posts_type"][c][g][0]
+ tom[j].append(h)
+ else:
+ tom[j].append("")
+ else:
+ tom[j].append("{:.2f}".format(data["information"][i]["analysis"]["emoji"][hori[j]]["all_posts_type"][c]))
+ if c=="averge_per_post":
+ avermax[0][j].append(data["information"][i]["analysis"]["emoji"][hori[j]]["all_posts_type"][c])
+ elif c=="max_number":
+ avermax[1][j].append(data["information"][i]["analysis"]["emoji"][hori[j]]["all_posts_type"][c])
+
+sheet5_1=pd.DataFrame(data={"شرکتها":company,"افق زمانی":time_horizon,"یک ماه اخیر":tom[0],"سه ماه اخیر":tom[1],"کل":tom[2]})
+
+capdia5=[]
+for i in range(2):
+ capdia5.append(pd.DataFrame(
+ {
+ "Company":international_company_name_fa,
+ "Last Month":avermax[i][0],
+ "Last Three Months":avermax[i][1],
+ "Overal":avermax[i][2]},
+ columns=['Company', 'Last Month', 'Last Three Months','Overal']
+ ))
+
+
+del h,j,c,t,i,kori,tom,time_horizon,temp,company,avermax
+
+
+
+
+
+
+
+######### sheet 6 common words
+
+company=[]
+temp=["میانگین تعداد لغات در هر پست","بیشترین تعداد لغات در یک پست","کم ترین تعداد لغات در یک پست","محبوب ترین لغات استفاده شده"]
+time_horizon=[]
+tom=[[],[],[]]
+hori=["last_month","last_three_months","overal"]
+avermax=[[[],[],[]],[[],[],[]]]
+kori=["averge_per_post","max_number","min_number","most_common"]
+for i in national_company_name2:
+ for t in temp:
+ if temp.index(t)==0:
+ company.append(national_company_name_fa[national_company_name2.index(i)])
+ else:
+ company.append("")
+
+ time_horizon.append(t)
+ for j in range(3):
+ for c in kori:
+ if c=="most_common":
+
+ if len(data["information"][i]["analysis"]["caption"][hori[j]]["all_posts_type"][c]) !=0 :
+ h=""
+ for g in range(len(data["information"][i]["analysis"]["caption"][hori[j]]["all_posts_type"][c])):
+ if g!=len(data["information"][i]["analysis"]["caption"][hori[j]]["all_posts_type"][c])-1:
+ h=h+data["information"][i]["analysis"]["caption"][hori[j]]["all_posts_type"][c][g][0]+"، "
+ else:
+ h+=data["information"][i]["analysis"]["caption"][hori[j]]["all_posts_type"][c][g][0]
+
+ tom[j].append(h)
+ else:
+ tom[j].append("")
+ else:
+ tom[j].append("{:.2f}".format(data["information"][i]["analysis"]["caption"][hori[j]]["all_posts_type"][c]))
+ if c=="averge_per_post":
+ avermax[0][j].append(data["information"][i]["analysis"]["caption"][hori[j]]["all_posts_type"][c])
+ elif c=="max_number":
+ avermax[1][j].append(data["information"][i]["analysis"]["caption"][hori[j]]["all_posts_type"][c])
+
+sheet6=pd.DataFrame(data={"شرکتها":company,"افق زمانی":time_horizon,"یک ماه اخیر":tom[0],"سه ماه اخیر":tom[1],"کل":tom[2]})
+
+
+
+capdia0=[]
+for i in range(2):
+ capdia0.append(pd.DataFrame(
+ {
+ "Company":national_company_name_fa,
+ "Last Month":avermax[i][0],
+ "Last Three Months":avermax[i][1],
+ "Overal":avermax[i][2]},
+ columns=['Company', 'Last Month', 'Last Three Months','Overal']
+ ))
+
+
+
+del h,j,c,t,i,kori,tom,time_horizon,temp,company,avermax
+
+company=[]
+temp=["میانگین تعداد لغات در هر پست","بیشترین تعداد لغات در یک پست","کم ترین تعداد لغات در یک پست","محبوب ترین لغات استفاده شده"]
+time_horizon=[]
+tom=[[],[],[]]
+hori=["last_month","last_three_months","overal"]
+avermax=[[[],[],[]],[[],[],[]]]
+kori=["averge_per_post","max_number","min_number","most_common"]
+for i in international_company_name2:
+ for t in temp:
+ if temp.index(t)==0:
+ company.append(international_company_name_fa[international_company_name2.index(i)])
+ else:
+ company.append("")
+
+ time_horizon.append(t)
+ for j in range(3):
+ for c in kori:
+ if c=="most_common":
+
+ if len(data["information"][i]["analysis"]["caption"][hori[j]]["all_posts_type"][c]) !=0 :
+ h=""
+ for g in range(len(data["information"][i]["analysis"]["caption"][hori[j]]["all_posts_type"][c])):
+ if g!=len(data["information"][i]["analysis"]["caption"][hori[j]]["all_posts_type"][c])-1:
+ h=h+data["information"][i]["analysis"]["caption"][hori[j]]["all_posts_type"][c][g][0]+"، "
+ else:
+ h+=data["information"][i]["analysis"]["caption"][hori[j]]["all_posts_type"][c][g][0]
+ tom[j].append(h)
+ else:
+ tom[j].append("")
+ else:
+ tom[j].append("{:.2f}".format(data["information"][i]["analysis"]["caption"][hori[j]]["all_posts_type"][c]))
+ if c=="averge_per_post":
+ avermax[0][j].append(data["information"][i]["analysis"]["caption"][hori[j]]["all_posts_type"][c])
+ elif c=="max_number":
+ avermax[1][j].append(data["information"][i]["analysis"]["caption"][hori[j]]["all_posts_type"][c])
+
+sheet6_1=pd.DataFrame(data={"شرکتها":company,"افق زمانی":time_horizon,"یک ماه اخیر":tom[0],"سه ماه اخیر":tom[1],"کل":tom[2]})
+
+
+
+
+capdia=[]
+for i in range(2):
+ capdia.append(pd.DataFrame(
+ {
+ "Company":international_company_name_fa,
+ "Last Month":avermax[i][0],
+ "Last Three Months":avermax[i][1],
+ "Overal":avermax[i][2]},
+ columns=['Company', 'Last Month', 'Last Three Months','Overal']
+ ))
+
+
+del h,j,c,t,i,kori,tom,time_horizon,temp,company,avermax
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+########## sheet7 populars
+
+
+
+
+
+
+
+
+company1=data["pop"]["companies"]["national"]["list"]
+follo=[sheet3followers[national_company_name.index(i)] for i in company1]
+company1_fa=[national_company_name_fa[national_company_name.index(i)] for i in company1]
+sheet7a=pd.DataFrame(data={"Companies Popularitites Based on Followers (sorted in decending order)":company1_fa,"Followers":follo})
+
+company1=data["pop"]["companies"]["international"]["list"]
+follo=[sheet3_1followers[international_company_name.index(i)] for i in company1]
+company1_fa=[international_company_name_fa[international_company_name.index(i)] for i in company1]
+sheet7a_1=pd.DataFrame(data={"Companies Popularitites Based on Followers (sorted in decending order)":company1_fa,"Followers":follo})
+del company1,follo
+
+
+
+time_horizon=["last_month","last_three_months","overal"]
+
+kind=["like","comment","view","engagement"]
+sheeta=[]
+
+sheeta_diagram=[]
+kinda12=["Like","Comment","View","Engagement"]
+for i in kind :
+ ranka=[[] for i in range(3)]
+ timie=[]
+ timie_fa=[]
+ for j in time_horizon:
+
+ timie_fa.append([national_company_name_fa[national_company_name.index(l)] for l in data["pop"]["national"][j][i]["list"]])
+ timie.append(data["pop"]["national"][j][i]["list"])
+ ranka[time_horizon.index(j)]=[(timie[time_horizon.index(j)].index(k))+1 for k in national_company_name ]
+
+ sheeta.append(pd.DataFrame(data={"یک ماه اخیر":timie_fa[0],"سه ماه اخیر":timie_fa[1],"کل":timie_fa[2]}))
+ sheeta_diagram.append(pd.DataFrame(
+ {
+ "Company":national_company_name_fa,
+ "Last Month":ranka[0],
+ "Last Three Months":ranka[1],
+ "Overal":ranka[2]
+ },
+ columns=["Company","Last Month","Last Three Months","Overal"]
+ ))
+
+
+
+sheeta_1=[]
+timie=[]
+sheeta_1diagram=[]
+
+for i in kind :
+ timie=[]
+ timie_fa=[]
+ ranka=[[] for i in range(3)]
+ for j in time_horizon:
+ timie.append(data["pop"]["international"][j][i]["list"])
+ timie_fa.append([international_company_name_fa[international_company_name.index(l)] for l in data["pop"]["international"][j][i]["list"]])
+ ranka[time_horizon.index(j)]=[(timie[time_horizon.index(j)].index(k))+1 for k in international_company_name ]
+ sheeta_1.append(pd.DataFrame(data={"یک ماه اخیر":timie_fa[0],"سه ماه اخیر":timie_fa[1],"کل":timie_fa[2]}))
+ sheeta_1diagram.append(pd.DataFrame(
+ {
+ "Company":international_company_name_fa,
+ "Last Month":ranka[0],
+ "Last Three Months":ranka[1],
+ "Overal":ranka[2]
+ },
+ columns=["Company","Last Month","Last Three Months","Overal"]
+ ))
+ sheeta_1diagram[kind.index(i)]= sheeta_1diagram[kind.index(i)].melt("Company",var_name="Time Horizon",value_name=kinda12[kind.index(i)])
+
+
+del time_horizon,i,j,timie ,timie_fa
+
+
+sheet7_0=pd.DataFrame(data={"Populars Social Medias":["Instagram","Telegram","Aparat","LinkdIn","Twitter","Facebook","Robica","iGap","Bale","Soroush","Whatsapp","Youtube","Pintrest"]})
+sheet7_0_1=pd.DataFrame(data={"Populars Social Medias":["Facebook","Instagram","Youtube","Twitter","Pintrest","LinkdIn","Whatsapp"]})
+
+
+#st.dataframe(sheet7_0)
+sheet7_0data=[19,10,8,5,4,3,0,0,0,0,0,0,0]
+she3=['19','10','8','5','4','3','0','0','0','0','0','0','0']
+num=["Instagram","Telegram","Aparat","LinkdIn","Twitter","Facebook","Robica","iGap","Bale","Soroush","Whatsapp","Youtube","Pintrest"]
+num_fa=["اینستاگرام","تلگرام","آپارات","لینکدین","توییتر","فیسبوک","روبیکا","آی گپ","بله","سروش","واتس اپ","یوتیوب","پینترست"]
+df23 = pd.DataFrame(data={"Social Medias":num,"The Number of Companies That Are Active In It":sheet7_0data})
+def left_align(s, props='text-align: left;'):
+ return props
+df12 = pd.DataFrame(data={"شبکههای اجتماعی":num_fa,"تعداد شرکتها در هر شبکهی اجتماعی":she3})
+
+
+#df12=df12.style.applymap(left_align)
+
+sheet7_0data=[12,12,11,7,4,2,0]
+she3=['12','12','11','7','4','2','0']
+num=["Facebook","Instagram","Youtube","Twitter","Pintrest","LinkdIn","Whatsapp"]
+num_fa=["فیسبوک","اینستاگرام","یوتیوب","توییتر","پینترست","لینکدین","واتس اپ"]
+
+df23_1 = pd.DataFrame(data={"Social Medias":num,"The Number of Companies That Are Active In It":sheet7_0data})
+def left_align(s, props='text-align: left;'):
+ return props
+df12_1 = pd.DataFrame(data={"Social Medias":num_fa,"The Number of Companies":she3})
+
+
+
+del num,she3,sheet7_0data
+
+
+
+############# sheet8 content Anlaysis
+
+
+maxi_national=[[0 for i in national_company_name2] for c in range(3)]
+content_national=[["Other" for i in national_company_name2] for c in range(3)]
+for i in national_company_name2:
+ ind=national_company_name2.index(i)
+ for f in ["last_month","last_three_months","overal"]:
+ inf=["last_month","last_three_months","overal"].index(f)
+ for j in data["information"][i]["analysis"]["caption"][f]["all_posts_type"]["anal"]:
+ if data["information"][i]["analysis"]["caption"][f]["all_posts_type"]["anal"][j]>maxi_national[inf][ind]:
+ maxi_national[inf][ind]= data["information"][i]["analysis"]["caption"][f]["all_posts_type"]["anal"][j]
+ content_national[inf][ind]=j.capitalize()
+
+
+
+maxi_international=[[0 for i in international_company_name2] for c in range(3)]
+content_international=[["Other" for i in international_company_name2] for c in range(3)]
+for i in international_company_name2:
+ ind=international_company_name2.index(i)
+ for f in ["last_month","last_three_months","overal"]:
+ inf=["last_month","last_three_months","overal"].index(f)
+ for j in data["information"][i]["analysis"]["caption"][f]["all_posts_type"]["anal"]:
+ if data["information"][i]["analysis"]["caption"][f]["all_posts_type"]["anal"][j]>maxi_international[inf][ind]:
+ maxi_international[inf][ind]= data["information"][i]["analysis"]["caption"][f]["all_posts_type"]["anal"][j]
+ content_international[inf][ind]=j.capitalize()
+
+def asem(a):
+ if isinstance(a,str):
+ if a=="Mahsol":
+ return "معرفی محصولات"
+ elif a=="Takhfif":
+ return "معرفی کالاهای تخفیف خورده"
+ elif a=="Jashn":
+ return "جشنوارهای فروش"
+ elif a=="Mosabeghe":
+ return "مسابقه"
+ elif a=="Other":
+ return "سایر محتواها"
+ else:
+ return a
+ else:
+ return a
+
+national_pop_content=stat.mode([i for i in content_national[0] if i!="Other"])
+international_pop_content=stat.mode([i for i in content_international[0] if i!="Other"])
+sheet8=pd.DataFrame(data={
+ "شرکت":national_company_name_fa,
+ "یک ماه اخیر": [asem(i) for i in content_national[0]],
+ "سه ماه اخیر":[asem(i) for i in content_national[1]],
+ "کل":[asem(i) for i in content_national[2]],
+ "ind":[i+1 for i in range(19)]
+ })
+
+sheet8diagram=pd.DataFrame(data={
+ "Content":[asem("Mosabeghe"),asem("Jashn"),asem("Takhfif"),asem("Mahsol")],
+ "Last Month":[content_national[0].count("Mosabeghe"),content_national[0].count("Jashn"),content_national[0].count("Takhfif"),content_national[0].count("Mahsol")],
+ "Last Three Months":[content_national[1].count("Mosabeghe"),content_national[1].count("Jashn"),content_national[1].count("Takhfif"),content_national[1].count("Mahsol")],
+ "Overal":[content_national[2].count("Mosabeghe"),content_national[2].count("Jashn"),content_national[2].count("Takhfif"),content_national[2].count("Mahsol")]
+ })
+
+sheet8_1diagram=pd.DataFrame(data={
+ "Content":[asem("Mosabeghe"),asem("Jashn"),asem("Takhfif"),asem("Mahsol")],
+ "Last Month":[content_international[0].count("Mosabeghe"),content_international[0].count("Jashn"),content_international[0].count("Takhfif"),content_international[0].count("Mahsol")],
+ "Last Three Months":[content_international[1].count("Mosabeghe"),content_international[1].count("Jashn"),content_international[1].count("Takhfif"),content_international[1].count("Mahsol")],
+ "Overal":[content_international[2].count("Mosabeghe"),content_international[2].count("Jashn"),content_international[2].count("Takhfif"),content_international[2].count("Mahsol")]
+ })
+
+sheet8_1=pd.DataFrame(data={
+ "شرکت":international_company_name_fa,
+ "یک ماه اخیر": [asem(i) for i in content_international[0]],
+ "سه ماه اخیر":[asem(i) for i in content_international[1]],
+ "کل":[asem(i) for i in content_international[2]],
+ "ind":[i+1 for i in range(12)]
+ })
+del maxi_national,maxi_international,content_international,content_national
+
+
+
+################ sheet 9 popular posts
+
+
+
+
+############### ave posting per day
+ave_post_perday_national_last_month=0
+for i in data["pop"]["time"]["national"]["day"]["last_month"]:
+ ave_post_perday_national_last_month+=i
+ave_post_perday_national_last_month=ave_post_perday_national_last_month/(33*7*19)
+
+
+ave_post_perday_national_last_three_month=0
+for i in data["pop"]["time"]["national"]["day"]["last_three_months"]:
+ ave_post_perday_national_last_three_month+=i
+ave_post_perday_national_last_three_month=ave_post_perday_national_last_three_month/(99*7*19)
+
+
+ave_post_perday_international_last_month=0
+for i in data["pop"]["time"]["international"]["day"]["last_month"]:
+ ave_post_perday_international_last_month+=i
+ave_post_perday_international_last_month=ave_post_perday_international_last_month/(33*7*12)
+
+ave_post_perday_international_last_three_month=0
+for i in data["pop"]["time"]["international"]["day"]["last_three_months"]:
+ ave_post_perday_international_last_three_month+=i
+ave_post_perday_international_last_three_month=ave_post_perday_international_last_three_month/(99*7*12)
+
+
+
+
+
+
+
+############################ Sheet Data
+
+
+num_image_national,num_posts_national,num_video_national=[[],[],[]],[[],[],[]],[[],[],[]]
+
+for i in national_company_name2:
+ k=-1
+ for j in ["last_month","last_three_months","overal"]:
+ k+=1
+ num_posts_national[k].append(len(data["information"][i]["analysis"]["time"]["analayzed_post_time"][j]["all_posts_type"]))
+ num_image_national[k].append(len(data["information"][i]["analysis"]["time"]["analayzed_post_time"][j]["image"]))
+ num_video_national[k].append(len(data["information"][i]["analysis"]["time"]["analayzed_post_time"][j]["video"]))
+
+sheet_data=pd.DataFrame(data={
+ "Company":national_company_name_fa,
+ "Last Month Total Posts":num_posts_national[0],
+ "Last Month Image":num_image_national[0],
+ "Last Month Video":num_video_national[0],
+ "Last Three Months Total Posts":num_posts_national[1],
+ "Last Three Months Image":num_image_national[1],
+ "Last Three Months Video":num_video_national[1],
+ "Overal Total Posts":num_posts_national[2],
+ "Overal Image":num_image_national[2],
+ "Overal Video":num_video_national[2],
+
+ })
+
+
+
+
+
+num_image_national,num_posts_national,num_video_national=[[],[],[]],[[],[],[]],[[],[],[]]
+
+for i in international_company_name2:
+ k=-1
+ for j in ["last_month","last_three_months","overal"]:
+ k+=1
+ num_posts_national[k].append(len(data["information"][i]["analysis"]["time"]["analayzed_post_time"][j]["all_posts_type"]))
+ num_image_national[k].append(len(data["information"][i]["analysis"]["time"]["analayzed_post_time"][j]["image"]))
+ num_video_national[k].append(len(data["information"][i]["analysis"]["time"]["analayzed_post_time"][j]["video"]))
+
+sheet_1data=pd.DataFrame(data={
+ "Company":international_company_name_fa,
+ "Last Month Total Posts":num_posts_national[0],
+ "Last Month Image":num_image_national[0],
+ "Last Month Video":num_video_national[0],
+ "Last Three Months Total Posts":num_posts_national[1],
+ "Last Three Months Image":num_image_national[1],
+ "Last Three Months Video":num_video_national[1],
+ "Overal Total Posts":num_posts_national[2],
+ "Overal Image":num_image_national[2],
+ "Overal Video":num_video_national[2],
+
+ })
+del num_image_national,num_posts_national,num_video_national
+
+
+
+
+
+
+########### ranks
+
+
+
+rank=[i for i in sheet3followers]
+rank.sort()
+eer=len(sheet3followers)
+ranki=[(eer-rank.index(i)) for i in sheet3followers]
+eer=len(sheet3_1followers)
+rank=[i for i in sheet3_1followers]
+rank.sort()
+ranki_1=[(eer-rank.index(i)) for i in sheet3_1followers]
+del rank
+
+#########3 number of posts analysed
+number_posts_analysed=0
+number_posts_analysed_national=0
+number_posts_analysed_international=0
+max_national=0
+max_national_account=""
+min_national=90000000
+min_national_account=""
+max_international=0
+max_international_account=""
+min_international=90000000
+min_international_account=""
+for i in data["information"]:
+ if i in national_company_name2:
+ number_posts_analysed_national+=data["information"][i]["analysis"]["number_of_posts_analysed"]
+ if data["information"][i]["analysis"]["number_of_posts_analysed"]>=max_national:
+ max_national=data["information"][i]["analysis"]["number_of_posts_analysed"]
+ max_national_account=i
+ if data["information"][i]["analysis"]["number_of_posts_analysed"]<=min_national:
+ min_national=data["information"][i]["analysis"]["number_of_posts_analysed"]
+ min_national_account=i
+ else:
+ number_posts_analysed_international+=data["information"][i]["analysis"]["number_of_posts_analysed"]
+ if data["information"][i]["analysis"]["number_of_posts_analysed"]>=max_international:
+ max_international=data["information"][i]["analysis"]["number_of_posts_analysed"]
+ max_international_account=i
+ if data["information"][i]["analysis"]["number_of_posts_analysed"]<=min_international:
+ min_international=data["information"][i]["analysis"]["number_of_posts_analysed"]
+ min_international_account=i
+
+ number_posts_analysed+=data["information"][i]["analysis"]["number_of_posts_analysed"]
+
+dash_1=pd.DataFrame(data={"خلاصه":["تعداد شرکتهای داخلی مورد بررسی","تعداد شرکتهای بین المللی مورد بررسی","تعداد پست های مورد بررسی","تعداد پست های شرکتهای داخلی بررسی شده",
+ "تعداد پستهای شرکت های بین المللی بررسی شده","میانگین تعداد پستهای بررسی شده برای هر شرکت داخلی","میانگین تعداد پست های بررسی شده برای هر شرکت بین المللی",
+ "بیشترین تعداد پست های بررسی شده برای یک شرکت داخلی","حساب کاربری بیشترین تعداد پست های بررسی شده برای یک شرکت داخلی ",
+ "کم ترین میزان پست های بررسی شده برای یک شرکت داخلی","حساب کاربری کم ترین میزان پست های بررسی شده برای یک شرکت داخلی",
+ "بیشترین تعداد پستهای بررسی شده برای یک شرکت بین المللی","حساب کاربری بیشترین تعداد پستهای بررسی شده برای یک شرکت بین المللی",
+ "کم ترین تعداد پست های بررسی شده برای یک شرکت بین المللی","حساب کاربری کم ترین میزان پست های بررسی شده برای یک شرکت بین المللی"
+ ],
+ "تعداد":[19,12,number_posts_analysed,number_posts_analysed_national,number_posts_analysed_international,"{:.1f}".format(number_posts_analysed_national/19),
+ "{:.1f}".format(number_posts_analysed_international/12),
+ max_national,max_national_account,min_national,min_national_account,max_international,max_international_account,min_international,min_international_account
+
+ ]
+
+ })
+
+del max_national,max_national_account,min_international,min_international_account,min_national,min_national_account,max_international,max_international_account
+
+
+
+if jkl:
+
+
+ ########## Sidebar
+ option1=["داشبورد","شرکتهای داخلی","شرکتهای بین المللی","آپارات","خلاصه"]
+
+ option2_fa=["شبکههای اجتماعی","جایگاه در شبکههای اجتماعی","ماهیت پست ها","تحلیل پست ها","تحلیل هشتگ ها","تحلیل اموجی ها","تحلیل متن ها","محبوب ها","تحلیل محتوای پستها","پیوند پستهای محبوب","تحلیل میزان تعامل شرکتها","زمان بارگذاری پستها"]
+ option3_fa=["شبکههای اجتماعی","تعداد دنبال کنندگان","پسندیده شدن","نظرات","بازدید","مشارکت"]
+ option2=["Social Meidas","Popularities","Form of Posts","Posts Analysis","Hashtags Analysis","Emoji Analysis","Caption Analysis","Populars","Content Analysis","Popular Posts Link","Interaction Analysis","Posting Time"]
+ option3=["Social Medias","Followers","Like","Comment","View","Engagement"]
+ option_aparat=["افق کوروش","دیجی کالا","رفاه"]
+ select1=st.sidebar.selectbox("انتخاب نوع شرکتها برای تحلیل",option1,key=1)
+
+ st.title("تحلیل بر خط شبکههای اجتماعی\n مهدی احمدی فرد")
+
+
+ ##### Aparat
+
+ if select1=="آپارات":
+ st.markdown("""
+ در اینجا شما میتوانید ویدیوهای محبوب در آپارات را مشاهده نمایید.
+ """)
+
+ select_apa=st.selectbox("شرکت را انتخاب کنید",option_aparat,key=29)
+ optionah=["Playlists","Populars"]
+ #select_optionah=st.sidebar.selectbox("Choose the info you want to see",optionah,key=229)
+ ip_aparat=["okclub","digikala","myrefah"]
+ insd=ip_aparat[option_aparat.index(select_apa)]
+
+ st.title("دسته بندی ویدیوها")
+ pllay=[i["title"].replace(""," ") for i in data["aparat"][insd]["playlist"]]
+ pllay=pd.DataFrame(data={"Playlist":pllay})
+ AgGrid(pllay,theme="streamlit")
+ #if select_optionah==optionah[1]:
+
+
+ df=pd.DataFrame(data={
+ "شرکت":select_apa,
+ "رتبه نخست": [f'1={data["aparat"][insd]["posts"][data["aparat"][insd]["sort_view"][0]]["frame"]}'],
+ "رتبه دوم":[f'2={data["aparat"][insd]["posts"][data["aparat"][insd]["sort_view"][1]]["frame"]}'],
+ "رتبه سوم":[f'3={data["aparat"][insd]["posts"][data["aparat"][insd]["sort_view"][2]]["frame"]}'],
+ "رتبه چهارم":[f'3={data["aparat"][insd]["posts"][data["aparat"][insd]["sort_view"][3]]["frame"]}']
+ })
+ for i in ["رتبه نخست","رتبه دوم","رتبه سوم","رتبه چهارم"]:
+ df[i]=df[i].apply(make_clickable)
+ df=df.to_html(escape=False)
+ st.header("برای مشاهده پستها بر روی اعداد کلیک کنید")
+ st.write(df,unsafe_allow_html=True)
+
+ st.title("مشاهده مستقیم ویدیوها")
+ st.header("پر بازدید ترین ویدیو")
+ st.write(f'''