├── .RData ├── .Rhistory ├── Procfile ├── README.md ├── __pycache__ ├── Rating.cpython-38.pyc ├── add.cpython-38.pyc ├── addapple.cpython-38.pyc ├── app.cpython-38.pyc └── wsgi.cpython-38.pyc ├── add.py ├── app.py ├── app.r ├── apples.csv ├── banana.csv ├── beef.csv ├── butter.csv ├── cheese.csv ├── chicken.csv ├── crabs.csv ├── curd.csv ├── foreapple.csv ├── forebanana.csv ├── forebeef.csv ├── forebutter.csv ├── forecheese.csv ├── forechicken.csv ├── forecrabs.csv ├── forecurd.csv ├── foregrapes.csv ├── foremango.csv ├── foremilk.csv ├── foremutton.csv ├── forepaneer.csv ├── forepine.csv ├── forepomfret.csv ├── forepork.csv ├── foreprawns.csv ├── forerohu.csv ├── forewatermelon.csv ├── grapes.csv ├── gunicorn.cfg ├── mango.csv ├── milk.csv ├── mutton.csv ├── paneer.csv ├── pomfret.csv ├── pork.csv ├── prawns.csv ├── requirements.txt ├── rohu.csv ├── runtime.txt ├── static ├── css │ ├── bootstrap.min.css │ ├── font-awesome.min.css │ ├── news_scroll.css │ ├── nivo-lightbox.css │ ├── nivo_themes │ │ └── default │ │ │ ├── close.png │ │ │ ├── close@2x.png │ │ │ ├── default.css │ │ │ ├── loading.gif │ │ │ ├── loading@2x.gif │ │ │ ├── next.png │ │ │ ├── next@2x.png │ │ │ ├── prev.png │ │ │ └── prev@2x.png │ └── templatemo-style.css ├── fonts │ ├── FontAwesome.otf │ ├── fontawesome-webfont.eot │ ├── fontawesome-webfont.svg │ ├── fontawesome-webfont.ttf │ ├── fontawesome-webfont.woff │ ├── fontawesome-webfont.woff2 │ ├── glyphicons-halflings-regular.eot │ ├── glyphicons-halflings-regular.svg │ ├── glyphicons-halflings-regular.ttf │ ├── glyphicons-halflings-regular.woff │ └── glyphicons-halflings-regular.woff2 ├── images │ ├── IMG1.png │ ├── IMG_0490.png │ ├── a.jpg │ ├── about-header.jpg │ ├── about-img(1).jpg │ ├── about-img(2).jpg │ ├── about-img.jpg │ ├── accurate.jpg │ ├── admin(1).png │ ├── admin(2).png │ ├── admin.png │ ├── ap.png │ ├── ap1(1).jpg │ ├── ap1(2).jpg │ ├── ap1.jpg │ ├── apples(1).png │ ├── apples(2).png │ ├── apples.png │ ├── b.jpg │ ├── b1.jpg │ ├── b3.png │ ├── banana.png │ ├── be.jpg │ ├── beef.png │ ├── begin.jpg │ ├── bf.jpg │ ├── bf1.jpg │ ├── blog-10.jpg │ ├── bt.jpg │ ├── bt1.jpg │ ├── bu.jpg │ ├── butter.png │ ├── c.jpg │ ├── c1.jpg │ ├── ca.jpg │ ├── cb.jpg │ ├── cb1.jpg │ ├── ce.jpg │ ├── ce1.jpg │ ├── ch.jpg │ ├── che.jpg │ ├── cheese.png │ ├── chicken.png │ ├── ck.jpg │ ├── ck1.jpg │ ├── clue.jpg │ ├── college-map.png │ ├── contact-header.jpg │ ├── cr.jpg │ ├── crabs.png │ ├── ct.jpg │ ├── cu.jpg │ ├── curd.png │ ├── daily.png │ ├── dairy.jpg │ ├── dairy.png │ ├── data.jpg │ ├── easy.png │ ├── f1.jpg │ ├── f2.jpg │ ├── f3.jpg │ ├── f4.jpg │ ├── farm.png │ ├── finallogo.png │ ├── fish.jpg │ ├── fish.png │ ├── fr1.jpg │ ├── fruit.jpeg │ ├── fruits.jpg │ ├── g.jpg │ ├── goal.jpg │ ├── gr.jpg │ ├── gr1.jpg │ ├── grapes.png │ ├── home-bg.jpg │ ├── kunal.jpeg │ ├── logo.png │ ├── m.jpg │ ├── m1.jpg │ ├── m2.jpg │ ├── man.jpg │ ├── man1.jpg │ ├── mango.png │ ├── me1.jpg │ ├── meat.jpg │ ├── meat.png │ ├── meats.png │ ├── mi.jpg │ ├── milk.jpg │ ├── milk.png │ ├── mk.jpg │ ├── mk1.jpg │ ├── mt.jpg │ ├── mt1.jpg │ ├── mu.jpg │ ├── multi.jpg │ ├── mutton.png │ ├── notend.jpg │ ├── pa.jpg │ ├── paneer.png │ ├── pf.jpg │ ├── pf1.jpg │ ├── pk.jpg │ ├── pk1.jpg │ ├── pn.jpg │ ├── pn1.jpg │ ├── po.jpg │ ├── pom.jpg │ ├── pomfret.png │ ├── pork.png │ ├── portfolio-header.jpg │ ├── pr.jpg │ ├── pra.jpg │ ├── pratik.jpeg │ ├── prawns.png │ ├── prob.jpg │ ├── rh.jpg │ ├── ro.jpg │ ├── rohu.png │ ├── sas1.png │ ├── saswat.jpeg │ ├── shraddha.jpeg │ ├── tackle.jpg │ ├── timser.gif │ ├── tu.jpg │ ├── w.jpg │ ├── w1.jpg │ ├── wa.jpg │ ├── wa1.jpg │ ├── ware.jpg │ ├── ware3.jpg │ ├── wastage (2).jpg │ ├── wastage.jpg │ ├── waste.jpg │ └── watermelon.png ├── js │ ├── bootstrap.min.js │ ├── custom.js │ ├── jquery.js │ ├── nivo-lightbox.min.js │ └── sample_news_scroll.js ├── nivo_themes │ └── default │ │ ├── close.png │ │ ├── close@2x.png │ │ ├── default.css │ │ ├── loading.gif │ │ ├── loading@2x.gif │ │ ├── next.png │ │ ├── next@2x.png │ │ ├── prev.png │ │ └── prev@2x.png ├── timser.gif ├── ware.jpg └── ware3.jpg ├── templates ├── about.html ├── addapple.html ├── addbanana.html ├── addbeef.html ├── addbutter.html ├── addcheese.html ├── addchicken.html ├── addcrabs.html ├── addcurd.html ├── addgrapes.html ├── addmango.html ├── addmilk.html ├── addmutton.html ├── addpaneer.html ├── addpomfret.html ├── addpork.html ├── addprawn.html ├── addrohu.html ├── addwatermelon.html ├── apple.html ├── appletable.html ├── banana.html ├── bananatable.html ├── beef.html ├── beeftable.html ├── blog.html ├── butter.html ├── buttertable.html ├── catfish.html ├── checkstock.html ├── cheese.html ├── cheesetable.html ├── chicken.html ├── chickentable.html ├── contact.html ├── crabs.html ├── crabtable.html ├── credits.txt ├── curd.html ├── curdtable.html ├── dairy.html ├── fish.html ├── fruit.html ├── grapes.html ├── grapestable.html ├── home.html ├── index.html ├── login.html ├── mango.html ├── mangotable.html ├── meat.html ├── milk.html ├── milktable.html ├── mutton.html ├── muttontable.html ├── new.html ├── newstock.html ├── paneer.html ├── paneertable.html ├── pomfret.html ├── pomfrettable.html ├── pork.html ├── porktable.html ├── prawns.html ├── prawntable.html ├── rohu.html ├── rohutable.html ├── success.html ├── successupdate.html ├── vapple.html ├── vbanana.html ├── vbeef.html ├── vbutter.html ├── vcatfish.html ├── vcheese.html ├── vchicken.html ├── vcrabs.html ├── vcurd.html ├── vdairy.html ├── vfish.html ├── vfruit.html ├── vgrapes.html ├── vhome.html ├── vmango.html ├── vmeat.html ├── vmilk.html ├── vmutton.html ├── vpaneer.html ├── vpomfret.html ├── vpork.html ├── vprawns.html ├── vrohu.html ├── vwatermelon.html ├── waste.html ├── watermelon.html └── watermelontable.html ├── watermelon.csv └── wsgi.py /.RData: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/saswat555/intercessor/5491c1c2456298dbb35abc6d0f79812a95703114/.RData -------------------------------------------------------------------------------- /Procfile: -------------------------------------------------------------------------------- 1 | web: gunicorn app:app 2 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Intercessor 2 | ## _The Smart Warehouse Management system 3 | 4 | 5 | 6 | 7 | Intercessor is a smart warehouse management tool that uses machine learning to reduce the wastage of perishable goods. The main goal is to make a system that can forecast the upcoming sales for a warehouse and then use the same to restock according to the predicted sales. So this is the main idea behind the project. 8 | 9 | ## Tech Stack and major libraries used 10 | 11 | - MongoDb for managing and storing the csv files 12 | - Flask web framework for webapp 13 | - Tensorflow for using ARIMA model 14 | - R programming for estimating the prediction variables 15 | - Pandas and numpy for other miscellaneous 16 | 17 | ## To deploy the app, follow the steps 18 | 19 | - Customize the data present in the CSV files as per your choice 20 | - Adjust the variable of forecast using the R file. 21 | - Change the variables for the particular commodity in the app.py file 22 | - Change the mongodb credentials with your credentials in the app.py file 23 | - To run the app, use flask run after installing the requirements 24 | - Please raise an issue if you face any issue in running the app or forecasting 25 | 26 | ## The app is currently deployed on heroku server- https://intercessor555.herokuapp.com/ 27 | 28 | 29 | 30 | -------------------------------------------------------------------------------- /__pycache__/Rating.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/saswat555/intercessor/5491c1c2456298dbb35abc6d0f79812a95703114/__pycache__/Rating.cpython-38.pyc -------------------------------------------------------------------------------- /__pycache__/add.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/saswat555/intercessor/5491c1c2456298dbb35abc6d0f79812a95703114/__pycache__/add.cpython-38.pyc -------------------------------------------------------------------------------- /__pycache__/addapple.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/saswat555/intercessor/5491c1c2456298dbb35abc6d0f79812a95703114/__pycache__/addapple.cpython-38.pyc -------------------------------------------------------------------------------- /__pycache__/app.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/saswat555/intercessor/5491c1c2456298dbb35abc6d0f79812a95703114/__pycache__/app.cpython-38.pyc -------------------------------------------------------------------------------- /__pycache__/wsgi.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/saswat555/intercessor/5491c1c2456298dbb35abc6d0f79812a95703114/__pycache__/wsgi.cpython-38.pyc -------------------------------------------------------------------------------- /app.r: -------------------------------------------------------------------------------- 1 | 2 | library(ggplot2) 3 | library(forecast) 4 | library(tseries) 5 | library(highcharter) 6 | d=read.csv('beef.csv', header=TRUE, stringsAsFactors=FALSE) 7 | attach(d) 8 | 9 | # Plotting the Demand 10 | GDP=ts(GDP,frequency=1) 11 | plot(GDP,col="blue",lwd=2,main="Sales",xlab="Year",ylab="Date") 12 | 13 | # ACF and PACF Plot 14 | par(mfrow=c(1,2)) 15 | Acf(GDP,main="",col="red",lwd=2) 16 | Pacf(GDP,main="",col="red",lwd=2) 17 | 18 | # Stationarity Check 19 | # Formal Tests 20 | adf.test(GDP, alternative = "stationary") 21 | kpss.test(GDP) 22 | 23 | # First Difference 24 | GDP_1=diff(GDP,1) 25 | 26 | # ACF and PACF Plot of First Difference 27 | par(mfrow=c(1,2)) 28 | Acf(GDP_1,main="",col="red",lwd=2) 29 | Pacf(GDP_1,main="",col="red",lwd=2) 30 | 31 | adf.test(GDP_1, alternative = "stationary") 32 | kpss.test(GDP_1) 33 | 34 | 35 | # Model Fitting 36 | fit=auto.arima(GDP,seasonal=TRUE) 37 | 38 | res=residuals(fit) 39 | par(mfrow=c(1,2)) 40 | Acf(res,main="ACF",col="red",lwd=2) 41 | Pacf(res,main="PACF",col="red",lwd=2) 42 | 43 | Box.test(res,fitdf=2,lag=10) 44 | Box.test(res,fitdf=2,lag=10,type="Lj") 45 | 46 | par(mfrow=c(1,1)) 47 | 48 | 49 | 50 | library(shiny) 51 | ui <- fluidPage( 52 | 53 | 54 | mainPanel(highchartOutput("hcontainer",height = "500px"))) 55 | server <- function(input,output) { 56 | output$hcontainer <- renderHighchart({ 57 | par(mfrow=c(1,1)) 58 | hchart(forecast(fit,w=1),lwd=2) 59 | }) 60 | 61 | 62 | 63 | 64 | 65 | } 66 | 67 | shinyApp(ui = ui, server = server) -------------------------------------------------------------------------------- /butter.csv: -------------------------------------------------------------------------------- 1 | Date,GDP 2 | 2019-09-10,505 3 | 2019-09-11,506 4 | 2019-09-12,509 5 | 2019-09-13,508 6 | 2019-09-14,508 7 | 2019-09-15,505 8 | 2019-09-16,505 9 | 2019-09-17,507 10 | 2019-09-18,505 11 | 2019-09-19,506 12 | 2019-09-20,505 13 | 2019-09-21,506 14 | 2019-09-22,505 15 | 2019-09-23,508 16 | 2019-09-24,509 17 | 2019-09-25,505 18 | 2019-09-26,505 19 | 2019-09-27,505 20 | 2019-09-28,506 21 | 2019-09-29,505 22 | 2019-09-30,510 23 | 2019-10-01,506 24 | 2019-10-02,505 25 | 2019-10-03,505 26 | 2019-10-04,501 27 | 2019-10-05,504 28 | 2019-10-06,505 29 | 2019-10-07,502 30 | 2019-10-08,502 31 | 2019-10-09,503 32 | 2019-10-10,505 33 | 2019-10-11,501 34 | 2019-10-12,505 35 | 2019-10-13,501 36 | 2019-10-14,505 37 | 2019-10-15,501 38 | 2019-10-16,503 39 | 2019-10-17,502 40 | 2019-10-18,502 41 | 2019-10-19,504 42 | 2019-10-20,503 43 | 2019-10-21,502 44 | 2019-10-22,500 45 | 2019-10-23,500 46 | 2019-10-24,502 47 | 2019-10-25,500 48 | 2019-10-26,521 49 | 2019-10-27,522 50 | 2019-10-28,520 51 | 2019-10-29,521 52 | 2019-10-30,522 53 | 2019-10-31,523 54 | 2019-11-01,522 55 | 2019-11-02,524 56 | 2019-11-03,525 57 | 2019-11-04,521 58 | 2019-11-05,523 59 | 2019-11-06,524 60 | 2019-11-07,522 61 | 2019-11-08,523 62 | 2019-11-09,524 63 | 2019-11-10,525 64 | 2019-11-11,521 65 | 2019-11-12,521 66 | 2019-11-13,522 67 | 2019-11-14,520 68 | 2019-11-15,523 69 | 2019-11-16,525 70 | 2019-11-17,525 71 | 2019-11-18,522 72 | 2019-11-19,530 73 | 2019-11-20,530 74 | 2019-11-21,532 75 | 2019-11-22,532 76 | 2019-11-23,531 77 | 2019-11-24,534 78 | 2019-11-25,530 79 | 2019-11-26,532 80 | 2019-11-27,532 81 | 2019-11-28,534 82 | 2019-11-29,534 83 | 2019-11-30,535 84 | 2019-12-01,535 85 | 2019-12-02,532 86 | 2019-12-03,530 87 | 2019-12-04,533 88 | 2019-12-05,532 89 | 2019-12-06,532 90 | 2019-12-07,531 91 | 2019-12-08,534 92 | 2019-12-09,530 93 | 2019-12-10,535 94 | 2019-12-11,531 95 | 2019-12-12,532 96 | 2019-12-13,533 97 | 2019-12-14,534 98 | 2019-12-15,533 99 | 2019-12-16,535 100 | 2019-12-17,533 101 | 2019-12-18,530 102 | 2019-12-19,541 103 | 2019-12-20,544 104 | 2019-12-21,544 105 | 2019-12-22,545 106 | 2019-12-23,542 107 | 2019-12-24,541 108 | 2019-12-25,541 109 | 2019-12-26,542 110 | 2019-12-27,541 111 | 2019-12-28,542 112 | 2019-12-29,544 113 | 2019-12-30,540 114 | 2019-12-31,545 115 | 2020-01-01,540 116 | 2020-01-02,541 117 | 2020-01-03,540 118 | 2020-01-04,543 119 | 2020-01-05,542 120 | 2020-01-06,545 121 | 2020-01-07,544 122 | 2020-01-08,540 123 | 2020-01-09,542 124 | 2020-01-10,544 125 | 2020-01-11,542 126 | 2020-01-12,543 127 | 2020-01-13,543 128 | 2020-01-14,543 129 | 2020-01-15,540 130 | 2020-01-16,542 131 | 2020-01-17,544 132 | 2020-01-18,544 133 | 2020-01-19,544 134 | 2020-01-20,542 135 | 2020-01-21,541 136 | 2020-01-22,542 137 | 2020-01-23,543 138 | 2020-01-24,545 139 | 2020-01-25,540 140 | 2020-01-26,561 141 | 2020-01-27,564 142 | 2020-01-28,565 143 | 2020-01-29,564 144 | 2020-01-30,563 145 | 2020-01-31,564 146 | 2020-02-01,562 147 | 2020-02-02,565 148 | 2020-02-03,565 149 | 2020-02-04,560 150 | 2020-02-05,560 151 | 2020-02-06,565 152 | 2020-02-07,561 153 | 2020-02-08,562 154 | 2020-02-09,563 155 | 2020-02-10,560 156 | 2020-02-11,562 157 | 2020-02-12,561 158 | 2020-02-13,565 159 | 2020-02-14,560 160 | 2020-02-15,563 161 | 2020-02-16,561 162 | 2020-02-17,564 163 | 2020-02-18,565 164 | 2020-02-19,560 165 | 2020-02-20,560 166 | 2020-02-21,560 167 | 2020-02-22,565 168 | 2020-02-23,564 169 | 2020-02-24,563 170 | 2020-02-25,560 171 | 2020-02-26,565 172 | 2020-02-27,564 173 | 2020-02-28,560 174 | 2020-02-29,562 175 | 2020-03-01,562 176 | 2020-03-02,561 177 | 2020-03-03,562 178 | 2020-03-04,560 179 | 2020-03-05,565 180 | 2020-03-06,564 181 | 2020-03-07,562 182 | 2020-03-08,563 183 | 2020-03-09,562 184 | 2020-03-10,561 185 | 2020-03-11,562 186 | 2020-03-12,565 187 | 2020-03-13,560 188 | 2020-03-14,561 189 | 2020-03-15,561 190 | 2020-03-16,562 191 | 2020-03-17,563 192 | 2020-03-18,565 193 | 2020-03-19,562 194 | 2020-03-20,561 195 | 2020-03-21,563 196 | 2020-03-22,561 197 | 2020-03-23,565 198 | 2020-03-24,563 199 | 2020-03-25,560 200 | 2020-03-26,560 201 | 2020-03-27,564 202 | 2020-03-28,561 203 | 2020-03-29,572 204 | 2020-03-30,570 205 | 2020-03-31,571 206 | 2020-04-01,572 207 | 2020-04-02,571 208 | 2020-04-03,571 209 | 2020-04-04,575 210 | 2020-04-05,571 211 | 2020-04-06,575 212 | 2020-04-07,575 213 | 2020-04-08,572 214 | 2020-04-09,575 215 | 2020-04-10,573 216 | 2020-04-11,573 217 | 2020-04-12,575 218 | 2020-04-13,573 219 | 2020-04-14,575 220 | 2020-04-15,575 221 | 2020-04-16,575 222 | 2020-04-17,573 223 | 2020-04-18,570 224 | 2020-04-19,573 225 | 2020-04-20,574 226 | 2020-04-21,595 227 | 2020-04-22,594 228 | 2020-04-23,595 229 | 2020-04-24,595 230 | 2020-04-25,592 231 | 2020-04-26,593 232 | 2020-04-27,591 233 | 2020-04-28,591 234 | 2020-04-29,594 235 | 2020-04-30,592 236 | 2020-05-01,590 237 | 2020-05-02,591 238 | 2020-05-03,594 239 | 2020-05-04,593 240 | 2020-05-05,591 241 | 2020-05-06,594 242 | 2020-05-07,595 243 | 2020-05-08,595 244 | 2020-05-09,592 245 | 2020-05-10,595 246 | 2020-05-11,590 247 | 2020-05-12,590 248 | 2020-05-13,594 249 | 2020-05-14,594 250 | 2020-05-15,591 251 | 2020-05-16,595 252 | 2020-05-17,590 253 | 2020-05-18,593 254 | 2020-05-19,591 255 | 2020-05-20,590 256 | 2020-05-21,591 257 | 2020-05-22,593 258 | 2020-05-23,590 259 | 2020-05-24,590 260 | 2020-05-25,593 261 | 2020-05-26,595 262 | 2020-05-27,590 263 | 2020-05-28,595 264 | 2020-05-29,592 265 | 2020-05-30,590 266 | 2020-05-31,591 267 | 2020-06-01,592 268 | 2020-06-02,592 269 | 2020-06-03,594 270 | 2020-06-04,595 271 | 2020-06-05,594 272 | 2020-06-06,592 273 | 2020-06-07,594 274 | 2020-06-08,595 275 | 2020-06-09,595 276 | 2020-06-10,591 277 | 2020-06-11,593 278 | 2020-06-12,591 279 | 2020-06-13,592 280 | 2020-06-14,593 281 | 2020-06-15,595 282 | 2020-06-16,594 283 | 2020-06-17,591 284 | 2020-06-18,590 285 | 2020-06-19,594 286 | 2020-06-20,589 287 | 2020-06-21,586 288 | 2020-06-22,587 289 | 2020-06-23,588 290 | 2020-06-24,587 291 | 2020-06-25,586 292 | 2020-06-26,586 293 | 2020-06-27,590 294 | 2020-06-28,585 295 | 2020-06-29,585 296 | 2020-06-30,587 297 | 2020-07-01,589 298 | 2020-07-02,590 299 | 2020-07-03,585 300 | 2020-07-04,585 301 | -------------------------------------------------------------------------------- /cheese.csv: -------------------------------------------------------------------------------- 1 | Date,GDP 2 | 2019-09-10,501 3 | 2019-09-11,501 4 | 2019-09-12,504 5 | 2019-09-13,505 6 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-------------------------------------------------------------------------------- /foreapple.csv: -------------------------------------------------------------------------------- 1 | ,Forecast 2 | 2020-06-22,720.2475832916678 3 | 2020-06-23,720.4793107435387 4 | 2020-06-24,720.6961978023903 5 | 2020-06-25,720.8991948833036 6 | 2020-06-26,721.0891915344525 7 | 2020-06-27,721.2670203351693 8 | 2020-06-28,721.4334605443682 9 | 2020-06-29,721.5892415153164 10 | 2020-06-30,721.7350458917128 11 | 2020-07-01,721.8715125990839 12 | 2020-07-02,721.9992396446019 13 | 2020-07-03,722.1187867375963 14 | 2020-07-04,722.230677742242 15 | 2020-07-05,722.33540297317 16 | 2020-07-06,722.4334213440627 17 | -------------------------------------------------------------------------------- /forebanana.csv: -------------------------------------------------------------------------------- 1 | ,Forecast 2 | 2020-06-22,399.8813537736372 3 | 2020-06-23,399.8795172797129 4 | 2020-06-24,399.8776807857886 5 | 2020-06-25,399.87584429186427 6 | 2020-06-26,399.87400779793995 7 | 2020-06-27,399.87217130401564 8 | 2020-06-28,399.8703348100913 9 | 2020-06-29,399.868498316167 10 | 2020-06-30,399.8666618222427 11 | 2020-07-01,399.8648253283184 12 | 2020-07-02,399.86298883439406 13 | 2020-07-03,399.86115234046974 14 | 2020-07-04,399.8593158465454 15 | 2020-07-05,399.8574793526211 16 | 2020-07-06,399.8556428586968 17 | -------------------------------------------------------------------------------- /forebeef.csv: -------------------------------------------------------------------------------- 1 | ,Forecast 2 | 2020-06-21,304.7989491160994 3 | 2020-06-22,304.71139297523484 4 | 2020-06-23,304.62333853217024 5 | 2020-06-24,304.53478578690556 6 | 2020-06-25,304.4457347394408 7 | 2020-06-26,304.35618538977593 8 | 2020-06-27,304.266137737911 9 | 2020-06-28,304.17559178384596 10 | 2020-06-29,304.08454752758087 11 | 2020-06-30,303.9930049691157 12 | 2020-07-01,303.9009641084504 13 | 2020-07-02,303.80842494558505 14 | 2020-07-03,303.7153874805196 15 | 2020-07-04,303.6218517132541 16 | 2020-07-05,303.5278176437885 17 | -------------------------------------------------------------------------------- /forebutter.csv: -------------------------------------------------------------------------------- 1 | ,Forecast 2 | 2020-02-23,585.756035784822 3 | 2020-02-24,586.0247797193758 4 | 2020-02-25,586.2935236539297 5 | 2020-02-26,586.5622675884836 6 | 2020-02-27,586.8310115230374 7 | 2020-02-28,587.0997554575913 8 | 2020-02-29,587.3684993921452 9 | 2020-03-01,587.637243326699 10 | 2020-03-02,587.9059872612529 11 | 2020-03-03,588.1747311958068 12 | 2020-03-04,588.4434751303606 13 | 2020-03-05,588.7122190649145 14 | 2020-03-06,588.9809629994684 15 | 2020-03-07,589.2497069340222 16 | 2020-03-08,589.5184508685761 17 | -------------------------------------------------------------------------------- /forecheese.csv: -------------------------------------------------------------------------------- 1 | ,Forecast 2 | 2020-02-23,521.1925218154649 3 | 2020-02-24,521.2578369616092 4 | 2020-02-25,521.3231521077535 5 | 2020-02-26,521.3884672538977 6 | 2020-02-27,521.453782400042 7 | 2020-02-28,521.5190975461862 8 | 2020-02-29,521.5844126923305 9 | 2020-03-01,521.6497278384747 10 | 2020-03-02,521.715042984619 11 | 2020-03-03,521.7803581307633 12 | 2020-03-04,521.8456732769075 13 | 2020-03-05,521.9109884230518 14 | 2020-03-06,521.976303569196 15 | 2020-03-07,522.0416187153403 16 | 2020-03-08,522.1069338614845 17 | -------------------------------------------------------------------------------- /forechicken.csv: -------------------------------------------------------------------------------- 1 | ,Forecast 2 | 299,448.9250836120401 3 | 300,447.8501672240802 4 | 301,446.77525083612034 5 | 302,445.70033444816045 6 | 303,444.62541806020056 7 | 304,443.55050167224067 8 | 305,442.4755852842808 9 | 306,441.4006688963209 10 | 307,440.325752508361 11 | 308,439.2508361204011 12 | 309,438.17591973244123 13 | 310,437.10100334448134 14 | 311,436.02608695652145 15 | 312,434.95117056856157 16 | 313,433.8762541806017 17 | -------------------------------------------------------------------------------- /forecrabs.csv: -------------------------------------------------------------------------------- 1 | ,Forecast 2 | 2020-02-23,238.311881200318 3 | 2020-02-24,238.42002758060386 4 | 2020-02-25,238.52817396088972 5 | 2020-02-26,238.63632034117558 6 | 2020-02-27,238.74446672146144 7 | 2020-02-28,238.8526131017473 8 | 2020-02-29,238.96075948203315 9 | 2020-03-01,239.068905862319 10 | 2020-03-02,239.17705224260487 11 | 2020-03-03,239.28519862289073 12 | 2020-03-04,239.3933450031766 13 | 2020-03-05,239.50149138346245 14 | 2020-03-06,239.6096377637483 15 | 2020-03-07,239.71778414403417 16 | 2020-03-08,239.82593052432003 17 | -------------------------------------------------------------------------------- /forecurd.csv: -------------------------------------------------------------------------------- 1 | ,Forecast 2 | 2020-02-23,238.311881200318 3 | 2020-02-24,238.42002758060386 4 | 2020-02-25,238.52817396088972 5 | 2020-02-26,238.63632034117558 6 | 2020-02-27,238.74446672146144 7 | 2020-02-28,238.8526131017473 8 | 2020-02-29,238.96075948203315 9 | 2020-03-01,239.068905862319 10 | 2020-03-02,239.17705224260487 11 | 2020-03-03,239.28519862289073 12 | 2020-03-04,239.3933450031766 13 | 2020-03-05,239.50149138346245 14 | 2020-03-06,239.6096377637483 15 | 2020-03-07,239.71778414403417 16 | 2020-03-08,239.82593052432003 17 | -------------------------------------------------------------------------------- /foregrapes.csv: -------------------------------------------------------------------------------- 1 | ,Forecast 2 | 2020-06-21,304.88468326405285 3 | 2020-06-22,304.88806191041584 4 | 2020-06-23,304.87715169924184 5 | 2020-06-24,304.8679616177455 6 | 2020-06-25,304.8585644625835 7 | 2020-06-26,304.8491922354857 8 | 2020-06-27,304.83981700748296 9 | 2020-06-28,304.8304421407369 10 | 2020-06-29,304.82106723050185 11 | 2020-06-30,304.8116923255021 12 | 2020-07-01,304.80231741987217 13 | 2020-07-02,304.79294251431804 14 | 2020-07-03,304.7835676087548 15 | 2020-07-04,304.7741927031927 16 | 2020-07-05,304.7648177976304 17 | -------------------------------------------------------------------------------- /foremango.csv: -------------------------------------------------------------------------------- 1 | ,Forecast 2 | 2020-06-21,639.5464932821708 3 | 2020-06-22,639.1453617303707 4 | 2020-06-23,639.2218416645529 5 | 2020-06-24,639.2983215987351 6 | 2020-06-25,639.3748015329173 7 | 2020-06-26,639.4512814670995 8 | 2020-06-27,639.5277614012817 9 | 2020-06-28,639.6042413354639 10 | 2020-06-29,639.6807212696461 11 | 2020-06-30,639.7572012038283 12 | 2020-07-01,639.8336811380105 13 | 2020-07-02,639.9101610721927 14 | 2020-07-03,639.986641006375 15 | 2020-07-04,640.0631209405572 16 | 2020-07-05,640.1396008747394 17 | -------------------------------------------------------------------------------- /foremilk.csv: -------------------------------------------------------------------------------- 1 | ,Forecast 2 | 2020-02-23,1511.9281318642184 3 | 2020-02-24,1511.954655779676 4 | 2020-02-25,1511.9811796951335 5 | 2020-02-26,1512.007703610591 6 | 2020-02-27,1512.0342275260487 7 | 2020-02-28,1512.0607514415062 8 | 2020-02-29,1512.0872753569638 9 | 2020-03-01,1512.1137992724214 10 | 2020-03-02,1512.140323187879 11 | 2020-03-03,1512.1668471033365 12 | 2020-03-04,1512.193371018794 13 | 2020-03-05,1512.2198949342517 14 | 2020-03-06,1512.2464188497092 15 | 2020-03-07,1512.2729427651668 16 | 2020-03-08,1512.2994666806244 17 | -------------------------------------------------------------------------------- /foremutton.csv: -------------------------------------------------------------------------------- 1 | ,Forecast 2 | 2020-02-22,1250.1649831649831 3 | 2020-02-23,1250.3299663299663 4 | 2020-02-24,1250.4949494949494 5 | 2020-02-25,1250.6599326599326 6 | 2020-02-26,1250.8249158249157 7 | 2020-02-27,1250.9898989898988 8 | 2020-02-28,1251.154882154882 9 | 2020-02-29,1251.3198653198651 10 | 2020-03-01,1251.4848484848483 11 | 2020-03-02,1251.6498316498314 12 | 2020-03-03,1251.8148148148146 13 | 2020-03-04,1251.9797979797977 14 | 2020-03-05,1252.1447811447808 15 | 2020-03-06,1252.309764309764 16 | 2020-03-07,1252.4747474747471 17 | -------------------------------------------------------------------------------- /forepaneer.csv: -------------------------------------------------------------------------------- 1 | ,Forecast 2 | 2020-02-23,1019.4303177087905 3 | 2020-02-24,1019.6112603664665 4 | 2020-02-25,1019.6464205401027 5 | 2020-02-26,1019.6815807137389 6 | 2020-02-27,1019.7167408873751 7 | 2020-02-28,1019.7519010610113 8 | 2020-02-29,1019.7870612346475 9 | 2020-03-01,1019.8222214082837 10 | 2020-03-02,1019.8573815819199 11 | 2020-03-03,1019.8925417555561 12 | 2020-03-04,1019.9277019291923 13 | 2020-03-05,1019.9628621028285 14 | 2020-03-06,1019.9980222764647 15 | 2020-03-07,1020.0331824501009 16 | 2020-03-08,1020.0683426237371 17 | -------------------------------------------------------------------------------- /forepine.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/saswat555/intercessor/5491c1c2456298dbb35abc6d0f79812a95703114/forepine.csv -------------------------------------------------------------------------------- /forepomfret.csv: -------------------------------------------------------------------------------- 1 | ,Forecast 2 | 2020-02-23,502.4206151023991 3 | 2020-02-24,500.7960206399413 4 | 2020-02-25,499.5948702946661 5 | 2020-02-26,498.7245465153534 6 | 2020-02-27,498.112689467081 7 | 2020-02-28,497.7027661748247 8 | 2020-02-29,497.4506088033685 9 | 2020-03-01,497.32171009978896 10 | 2020-03-02,497.2891103886008 11 | 2020-03-03,497.3317467325749 12 | 2020-03-04,497.43316317232996 13 | 2020-03-05,497.5805030679675 14 | 2020-03-06,497.76372184015634 15 | 2020-03-07,497.9749719039333 16 | 2020-03-08,498.2081221324765 17 | -------------------------------------------------------------------------------- /forepork.csv: -------------------------------------------------------------------------------- 1 | ,Forecast 2 | 2020-02-23,59.43480251754778 3 | 2020-02-24,59.13620532272452 4 | 2020-02-25,58.875034548351934 5 | 2020-02-26,58.665113305451115 6 | 2020-02-27,58.494382964378005 7 | 2020-02-28,58.35576240493174 8 | 2020-02-29,58.24318524885923 9 | 2020-03-01,58.15176177020956 10 | 2020-03-02,58.07751674064481 11 | 2020-03-03,58.01722237709509 12 | 2020-03-04,57.968257339686375 13 | 2020-03-05,57.928492845761994 14 | 2020-03-06,57.89620011069859 15 | 2020-03-07,57.869975189050095 16 | 2020-03-08,57.848677937078435 17 | -------------------------------------------------------------------------------- /foreprawns.csv: -------------------------------------------------------------------------------- 1 | ,Forecast 2 | 2020-02-23,231.04953488665066 3 | 2020-02-24,230.96890056826018 4 | 2020-02-25,230.8882662498697 5 | 2020-02-26,230.80763193147922 6 | 2020-02-27,230.72699761308874 7 | 2020-02-28,230.64636329469826 8 | 2020-02-29,230.56572897630778 9 | 2020-03-01,230.4850946579173 10 | 2020-03-02,230.40446033952682 11 | 2020-03-03,230.32382602113634 12 | 2020-03-04,230.24319170274586 13 | 2020-03-05,230.16255738435538 14 | 2020-03-06,230.0819230659649 15 | 2020-03-07,230.00128874757442 16 | 2020-03-08,229.92065442918394 17 | -------------------------------------------------------------------------------- /forerohu.csv: -------------------------------------------------------------------------------- 1 | ,Forecast 2 | 2020-02-23,523.1931144978407 3 | 2020-02-24,523.4034064692407 4 | 2020-02-25,523.0919184772854 5 | 2020-02-26,523.2684900241013 6 | 2020-02-27,523.4002373799257 7 | 2020-02-28,523.4103613658556 8 | 2020-02-29,523.4816073054284 9 | 2020-03-01,523.5633175057335 10 | 2020-03-02,523.6234091363756 11 | 2020-03-03,523.6890995582903 12 | 2020-03-04,523.7587418483263 13 | 2020-03-05,523.8252147282136 14 | 2020-03-06,523.8918177149868 15 | 2020-03-07,523.9592744619956 16 | 2020-03-08,524.0263548724856 17 | -------------------------------------------------------------------------------- /forewatermelon.csv: -------------------------------------------------------------------------------- 1 | ,Forecast 2 | 2020-06-22,812.0915745041825 3 | 2020-06-23,811.248461980481 4 | 2020-06-24,810.4053494567795 5 | 2020-06-25,809.562236933078 6 | 2020-06-26,808.7191244093765 7 | 2020-06-27,807.876011885675 8 | 2020-06-28,807.0328993619735 9 | 2020-06-29,806.189786838272 10 | 2020-06-30,805.3466743145705 11 | 2020-07-01,804.503561790869 12 | 2020-07-02,803.6604492671675 13 | 2020-07-03,802.817336743466 14 | 2020-07-04,801.9742242197644 15 | 2020-07-05,801.1311116960629 16 | 2020-07-06,800.2879991723614 17 | -------------------------------------------------------------------------------- /gunicorn.cfg: -------------------------------------------------------------------------------- 1 | bind = "0.0.0.0:5000" 2 | workers = 3 3 | worker_class = "gevent" 4 | -------------------------------------------------------------------------------- /pomfret.csv: -------------------------------------------------------------------------------- 1 | Date,GDP 2 | 2019-09-10,459 3 | 2019-09-11,300 4 | 2019-09-12,400 5 | 2019-09-13,432 6 | 2019-09-14,345 7 | 2019-09-15,543 8 | 2019-09-16,555 9 | 2019-09-17,213 10 | 2019-09-18,210 11 | 2019-09-19,200 12 | 2019-09-20,200 13 | 2019-09-21,310 14 | 2019-09-22,320 15 | 2019-09-23,322 16 | 2019-09-24,432 17 | 2019-09-25,430 18 | 2019-09-26,450 19 | 2019-09-27,440 20 | 2019-09-28,210 21 | 2019-09-29,209 22 | 2019-09-30,309 23 | 2019-10-01,322 24 | 2019-10-02,345 25 | 2019-10-03,543 26 | 2019-10-04,550 27 | 2019-10-05,589 28 | 2019-10-06,567 29 | 2019-10-07,596 30 | 2019-10-08,543 31 | 2019-10-09,544 32 | 2019-10-10,230 33 | 2019-10-11,330 34 | 2019-10-12,450 35 | 2019-10-13,320 36 | 2019-10-14,320 37 | 2019-10-15,320 38 | 2019-10-16,430 39 | 2019-10-17,432 40 | 2019-10-18,429 41 | 2019-10-19,419 42 | 2019-10-20,456 43 | 2019-10-21,554 44 | 2019-10-22,553 45 | 2019-10-23,554 46 | 2019-10-24,543 47 | 2019-10-25,554 48 | 2019-10-26,321 49 | 2019-10-27,444 50 | 2019-10-28,432 51 | 2019-10-29,432 52 | 2019-10-30,453 53 | 2019-10-31,444 54 | 2019-11-01,321 55 | 2019-11-02,332 56 | 2019-11-03,312 57 | 2019-11-04,345 58 | 2019-11-05,453 59 | 2019-11-06,333 60 | 2019-11-07,321 61 | 2019-11-08,312 62 | 2019-11-09,345 63 | 2019-11-10,543 64 | 2019-11-11,365 65 | 2019-11-12,345 66 | 2019-11-13,321 67 | 2019-11-14,400 68 | 2019-11-15,410 69 | 2019-11-16,423 70 | 2019-11-17,444 71 | 2019-11-18,412 72 | 2019-11-19,421 73 | 2019-11-20,433 74 | 2019-11-21,442 75 | 2019-11-22,443 76 | 2019-11-23,445 77 | 2019-11-24,446 78 | 2019-11-25,432 79 | 2019-11-26,478 80 | 2019-11-27,467 81 | 2019-11-28,487 82 | 2019-11-29,466 83 | 2019-11-30,498 84 | 2019-12-01,488 85 | 2019-12-02,489 86 | 2019-12-03,467 87 | 2019-12-04,468 88 | 2019-12-05,469 89 | 2019-12-06,458 90 | 2019-12-07,496 91 | 2019-12-08,450 92 | 2019-12-09,322 93 | 2019-12-10,367 94 | 2019-12-11,355 95 | 2019-12-12,346 96 | 2019-12-13,378 97 | 2019-12-14,387 98 | 2019-12-15,368 99 | 2019-12-16,378 100 | 2019-12-17,398 101 | 2019-12-18,366 102 | 2019-12-19,355 103 | 2019-12-20,399 104 | 2019-12-21,320 105 | 2019-12-22,432 106 | 2019-12-23,413 107 | 2019-12-24,411 108 | 2019-12-25,415 109 | 2019-12-26,419 110 | 2019-12-27,449 111 | 2019-12-28,448 112 | 2019-12-29,443 113 | 2019-12-30,409 114 | 2019-12-31,406 115 | 2020-01-01,309 116 | 2020-01-02,308 117 | 2020-01-03,550 118 | 2020-01-04,506 119 | 2020-01-05,577 120 | 2020-01-06,589 121 | 2020-01-07,554 122 | 2020-01-08,553 123 | 2020-01-09,509 124 | 2020-01-10,508 125 | 2020-01-11,567 126 | 2020-01-12,543 127 | 2020-01-13,613 128 | 2020-01-14,623 129 | 2020-01-15,645 130 | 2020-01-16,667 131 | 2020-01-17,622 132 | 2020-01-18,648 133 | 2020-01-19,654 134 | 2020-01-20,660 135 | 2020-01-21,550 136 | 2020-01-22,543 137 | 2020-01-23,552 138 | 2020-01-24,537 139 | 2020-01-25,567 140 | 2020-01-26,543 141 | 2020-01-27,523 142 | 2020-01-28,557 143 | 2020-01-29,587 144 | 2020-01-30,568 145 | 2020-01-31,589 146 | 2020-02-01,543 147 | 2020-02-02,559 148 | 2020-02-03,557 149 | 2020-02-04,544 150 | 2020-02-05,564 151 | 2020-02-06,459 152 | 2020-02-07,300 153 | 2020-02-08,400 154 | 2020-02-09,432 155 | 2020-02-10,345 156 | 2020-02-11,543 157 | 2020-02-12,555 158 | 2020-02-13,213 159 | 2020-02-14,210 160 | 2020-02-15,200 161 | 2020-02-16,200 162 | 2020-02-17,310 163 | 2020-02-18,320 164 | 2020-02-19,322 165 | 2020-02-20,432 166 | 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2019-10-13,255 36 | 2019-10-14,252 37 | 2019-10-15,258 38 | 2019-10-16,253 39 | 2019-10-17,255 40 | 2019-10-18,252 41 | 2019-10-19,253 42 | 2019-10-20,252 43 | 2019-10-21,252 44 | 2019-10-22,253 45 | 2019-10-23,254 46 | 2019-10-24,253 47 | 2019-10-25,252 48 | 2019-10-26,253 49 | 2019-10-27,250 50 | 2019-10-28,256 51 | 2019-10-29,252 52 | 2019-10-30,253 53 | 2019-10-31,251 54 | 2019-11-01,260 55 | 2019-11-02,256 56 | 2019-11-03,280 57 | 2019-11-04,272 58 | 2019-11-05,280 59 | 2019-11-06,280 60 | 2019-11-07,272 61 | 2019-11-08,270 62 | 2019-11-09,278 63 | 2019-11-10,275 64 | 2019-11-11,278 65 | 2019-11-12,277 66 | 2019-11-13,271 67 | 2019-11-14,277 68 | 2019-11-15,272 69 | 2019-11-16,276 70 | 2019-11-17,272 71 | 2019-11-18,277 72 | 2019-11-19,278 73 | 2019-11-20,274 74 | 2019-11-21,278 75 | 2019-11-22,276 76 | 2019-11-23,279 77 | 2019-11-24,276 78 | 2019-11-25,273 79 | 2019-11-26,278 80 | 2019-11-27,276 81 | 2019-11-28,274 82 | 2019-11-29,271 83 | 2019-11-30,270 84 | 2019-12-01,271 85 | 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} 55 | 56 | /* FONTS & PADDING */ 57 | 58 | .scroll-text-if { 59 | color: #666666; 60 | font: normal 12px "Droid Sans", arial, sans-serif; 61 | text-align: left; 62 | padding: 8px 7px 0px 7px; 63 | } 64 | 65 | .scroll-title-if { 66 | color: #000000; 67 | font: bold 15px "Droid Sans", arial, sans-serif; 68 | text-align: left; 69 | border-bottom: #666666 0px solid; 70 | } 71 | 72 | /* EMBEDDED NEWS PAGE LINK COLORS */ 73 | 74 | .news-scroll a:link { color: #0033FF; text-decoration: none; } 75 | 76 | .news-scroll a:visited { color: #6633FF; text-decoration: none; } 77 | 78 | .news-scroll a:active { color: #0033FF; text-decoration: none; } 79 | 80 | .news-scroll a:hover { color: #6699FF; text-decoration: underline; } 81 | 82 | 83 | 84 | -------------------------------------------------------------------------------- /static/css/nivo_themes/default/close.png: -------------------------------------------------------------------------------- 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11 | background: #666; 12 | background: rgba(0,0,0,0.6); 13 | } 14 | .nivo-lightbox-theme-default .nivo-lightbox-content.nivo-lightbox-loading { background: url(loading.gif) no-repeat 50% 50%; } 15 | 16 | .nivo-lightbox-theme-default .nivo-lightbox-nav { 17 | top: 10%; 18 | width: 8%; 19 | height: 80%; 20 | text-indent: -9999px; 21 | background-repeat: no-repeat; 22 | background-position: 50% 50%; 23 | opacity: 0.5; 24 | } 25 | .nivo-lightbox-theme-default .nivo-lightbox-nav:hover { 26 | opacity: 1; 27 | background-color: rgba(0,0,0,0.5); 28 | } 29 | .nivo-lightbox-theme-default .nivo-lightbox-prev { 30 | background-image: url(prev.png); 31 | border-radius: 0 3px 3px 0; 32 | } 33 | .nivo-lightbox-theme-default .nivo-lightbox-next { 34 | background-image: url(next.png); 35 | border-radius: 3px 0 0 3px; 36 | } 37 | 38 | .nivo-lightbox-theme-default .nivo-lightbox-close { 39 | display: block; 40 | background: url(close.png) no-repeat; 41 | width: 48px; 42 | height: 48px; 43 | text-indent: -9999px; 44 | padding: 5px; 45 | opacity: 0.5; 46 | } 47 | .nivo-lightbox-theme-default .nivo-lightbox-close:hover { opacity: 1; } 48 | 49 | .nivo-lightbox-theme-default .nivo-lightbox-title-wrap { bottom: -7%; } 50 | .nivo-lightbox-theme-default .nivo-lightbox-title { 51 | font: 14px/20px 'Helvetica Neue', Helvetica, Arial, sans-serif; 52 | font-style: normal; 53 | font-weight: normal; 54 | background: #000; 55 | color: #fff; 56 | padding: 7px 15px; 57 | border-radius: 30px; 58 | } 59 | 60 | .nivo-lightbox-theme-default .nivo-lightbox-image img { 61 | background: #fff; 62 | -webkit-box-shadow: 0px 1px 1px rgba(0,0,0,0.4); 63 | box-shadow: 0px 1px 1px rgba(0,0,0,0.4); 64 | } 65 | .nivo-lightbox-theme-default .nivo-lightbox-ajax, 66 | .nivo-lightbox-theme-default .nivo-lightbox-inline { 67 | background: #fff; 68 | padding: 40px; 69 | -webkit-box-shadow: 0px 1px 1px rgba(0,0,0,0.4); 70 | box-shadow: 0px 1px 1px rgba(0,0,0,0.4); 71 | } 72 | 73 | @media (-webkit-min-device-pixel-ratio: 1.3), 74 | (-o-min-device-pixel-ratio: 2.6/2), 75 | (min--moz-device-pixel-ratio: 1.3), 76 | (min-device-pixel-ratio: 1.3), 77 | (min-resolution: 1.3dppx) { 78 | 79 | .nivo-lightbox-theme-default .nivo-lightbox-content.nivo-lightbox-loading { 80 | background-image: url(loading@2x.gif); 81 | -webkit-background-size: 32px 32px; 82 | background-size: 32px 32px; 83 | } 84 | .nivo-lightbox-theme-default .nivo-lightbox-prev { 85 | background-image: url(prev@2x.png); 86 | -webkit-background-size: 48px 48px; 87 | background-size: 48px 48px; 88 | } 89 | .nivo-lightbox-theme-default .nivo-lightbox-next { 90 | background-image: url(next@2x.png); 91 | -webkit-background-size: 48px 48px; 92 | background-size: 48px 48px; 93 | } 94 | .nivo-lightbox-theme-default .nivo-lightbox-close { 95 | background-image: url(close@2x.png); 96 | -webkit-background-size: 16px 16px; 97 | background-size: 16px 16px; 98 | } 99 | 100 | } -------------------------------------------------------------------------------- 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30 | script.type = 'text/javascript'; 31 | script.src = 'https://maps.googleapis.com/maps/api/js?v=3.exp&sensor=false&' + 'callback=initialize'; 32 | document.body.appendChild(script); 33 | } 34 | 35 | $(function(){ 36 | loadGoogleMap(); 37 | }); 38 | 39 | // NIVO LIGHTBOX 40 | $('#portfolio a').nivoLightbox({ 41 | effect: 'fadeScale', 42 | }); 43 | 44 | // HIDE MOBILE MENU AFTER CLIKING ON A LINK 45 | $('.navbar-collapse a').click(function(){ 46 | $(".navbar-collapse").collapse('hide'); 47 | }); -------------------------------------------------------------------------------- /static/js/sample_news_scroll.js: -------------------------------------------------------------------------------- 1 | // START SCRIPT 2 | 3 | 4 | var SNshow = "yes" // SHOW NEWS IFRAME 5 | 6 | 7 | 8 | if (SNshow == "yes") { 9 | 10 | // START SCROLLING NEWS 11 | document.write('
We will fill our stock according to the given table. If you want to supply stock, please contact us.
33 |35 | {% for table in tables %} 36 | {{ table|safe }} 37 | {% endfor %} 38 |
39 |We will fill our stock according to the given table. If you want to supply stock, please contact us.
33 |35 | {% for table in tables %} 36 | {{ table|safe }} 37 | {% endfor %} 38 |
39 |Copyright © 2084 Team Umeed
112 |Logo Copyright © Reserved
113 |We will fill our stock according to the given table. If you want to supply stock, please contact us.
33 |35 | {% for table in tables %} 36 | {{ table|safe }} 37 | {% endfor %} 38 |
39 |We will fill our stock according to the given table. If you want to supply stock, please contact us.
33 |35 | {% for table in tables %} 36 | {{ table|safe }} 37 | {% endfor %} 38 |
39 | 40 |We will fill our stock according to the given table. If you want to supply stock, please contact us.
33 |35 | {% for table in tables %} 36 | {{ table|safe }} 37 | {% endfor %} 38 |
39 |Error: {{ error }}
100 | {% endif %} 101 |We will fill our stock according to the given table. If you want to supply stock, please contact us.
33 |35 | {% for table in tables %} 36 | {{ table|safe }} 37 | {% endfor %} 38 |
39 |We will fill our stock according to the given table. If you want to supply stock, please contact us.
33 |35 | {% for table in tables %} 36 | {{ table|safe }} 37 | {% endfor %} 38 |
39 |We will fill our stock according to the given table. If you want to supply stock, please contact us.
33 |35 | {% for table in tables %} 36 | {{ table|safe }} 37 | {% endfor %} 38 |
39 |Welcome
49 |We will fill our stock according to the given table. If you want to supply stock, please contact us.
33 |35 | {% for table in tables %} 36 | {{ table|safe }} 37 | {% endfor %} 38 |
39 |We will fill our stock according to the given table. If you want to supply stock, please contact us.
33 |35 | {% for table in tables %} 36 | {{ table|safe }} 37 | {% endfor %} 38 |
39 |We will fill our stock according to the given table. If you want to supply stock, please contact us.
33 |35 | {% for table in tables %} 36 | {{ table|safe }} 37 | {% endfor %} 38 |
39 |We will fill our stock according to the given table. If you want to supply stock, please contact us.
33 |35 | {% for table in tables %} 36 | {{ table|safe }} 37 | {% endfor %} 38 |
39 |The Intercessor
All the data has been successfully sent to the viewers
57 | -------------------------------------------------------------------------------- /templates/successupdate.html: -------------------------------------------------------------------------------- 1 | 2 | 3 |The Intercessor
All the data has been successfully updated
57 | -------------------------------------------------------------------------------- /templates/watermelontable.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 30 | 31 |We will fill our stock according to the given table. If you wnt to supply stock, please contact us.
33 |35 | {% for table in tables %} 36 | {{ table|safe }} 37 | {% endfor %} 38 |
39 |