enabled file sorting based on subject and match strength

main.py

@@ -3,46 +3,69 @@
 # most of this application adapted from the following walkthrough:
 # https://towardsdatascience.com/how-to-use-a-pre-trained-model-vgg-for-image-classification-8dd7c4a4a517
 
-import sys, os, json, time
+import sys, os, json
+from time import time
 from predict import predict
+from formatresult import format_result
 from keras.applications.vgg16 import VGG16
 
-print("\n\n\n")
-print("Imports successful! Running startup processes...")
+print("\n\nImage Sorting Utility\n")
+print("Script by Mikayla Dobson\n")
+print("\n\n")
+print("Begininning setup...\n\n")
 
-# generate current time for use in identifying outfiles
-cur_time = str(int(time.time()))
+############################## SETUP
+############################## SETUP
+############################## SETUP
 
 # create the target directory if it doesn't exist
 if (not os.path.exists("./predictions")):
-    print("Did not find predictions directory, creating...")
+    print("Did not find predictions directory, creating...\n\n")
     os.makedirs("./predictions")
 
-# declare model to be used for each prediction
-model = VGG16(weights='imagenet')
-
 # receive directory path as CLI argument and get a list of all files in path
-path = sys.argv[1]
-if (path[-1] != "/"):
-    path += "/"
+src_path = sys.argv[1]
 
-files = os.listdir(path)
+if (src_path[-1] != "/"):
+    src_path += "/"
+
+files = os.listdir(src_path)
+
+# generate current time for use in identifying outfiles
+cur_time = str(int(time()))
 
 # store all results in one list
 all_results = []
 
-print("Running image analysis. This may take some time")
+############################## ANALYSIS
+############################## ANALYSIS
+############################## ANALYSIS
+
+# declare model to be used for each prediction
+model = VGG16(weights='imagenet')
+
+print("Running image analysis. This may take some time...\n\n")
 
 # for each file in directory, append its prediction result to main list
 for file in files:
-    result = predict(model, path + file)
+    result = predict(model, src_path + file)
     if result is not None:
         all_results.append({ "path": file, "prediction": result })
 
-print("Analysis complete! Writing JSON to ./predictions/predictions" + cur_time + ".json")
+json_path = "./predictions/predictions" + cur_time + ".json"
+
+print("Writing analysis results to " + json_path + "\n\n")
 
 # convert object to JSON and write to JSON file
-with open("./predictions/predictions" + cur_time + ".json", "w") as outfile:
+with open(json_path, "w") as outfile:
     json.dump(all_results, outfile)
 
-print("Process complete!")
+print("Analysis complete! Beginning sort process...\n\n")
+
+############################## SORTING
+############################## SORTING
+############################## SORTING
+
+format_result(src_path, json_path)
+
+print("File sort successful! Process complete.")