App global

global.R

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+library(shiny)
+library(shinydashboard)
+library(rsconnect)
+library(tidyverse)
+library(reticulate)
+library(purrr)
+library(stringr)
+
+# Create conda env if not exist
+if(!("foodbank_py37" %in% conda_list()$name)){
+  # conda_create("foodbank_py37", python_version = "3.7")
+  use_condaenv("foodbank_py37", required = TRUE)
+  # Set up python libraries for object detection
+  source_python("setup.py")
+}
+
+
+# Check python version
+# py_config()
+
+# Load OD model and prediction functions
+source_python("image_classification.py")
+
+# Function to convert scoring result to a df
+convert_scoring_rst <- function(df){
+  all_df = NULL
+  for(x in names(df)){
+    tmp_name = x
+    tmp_val = df[[x]]
+    tmp_val = as.numeric(as.character(tmp_val))
+    tmp_row = data.frame(tmp_name, tmp_val)
+    all_df = rbind(all_df, tmp_row)
+  }
+  colnames(all_df) = c("Category", "Prediction")
+
+  return(all_df)
+}
+
+# Load all image names from current directory
+ALL_IMGS = list.files("images/")
+ALL_CATS = unique(unlist(purrr::map(ALL_IMGS, function(x) str_split(x,"_")[[1]][1])))