add comp back

generic_ner.py

@@ -360,24 +360,29 @@ def extract_name_from_text(text, partial_name):
     words = tokenize(text)
     partial_words = partial_name.split()
 
-    print("text:", text)
-    print("partial_name:", partial_name)
+    if DEBUG:
+        print("text:", text)
+    if DEBUG:
+        print("partial_name:", partial_name)
 
     # Find the position of the partial name in the word list
     for i, word in enumerate(words):
-        print(words, "---", words[i : i + len(partial_words)])
+        if DEBUG:
+            print(words, "---", words[i : i + len(partial_words)])
         if words[i : i + len(partial_words)] == partial_words:
             # Initialize full name with the partial name
             full_name = partial_words[:]
 
-            print("full_name:", full_name)
+            if DEBUG:
+                print("full_name:", full_name)
 
             # Check previous words and only add capitalized words (skip lowercase words)
             j = i - 1
             while j >= 0 and words[j][0].isupper():
                 full_name.insert(0, words[j])
                 j -= 1
-                print("full_name:", full_name)
+                if DEBUG:
+                    print("full_name:", full_name)
 
             # Return only the full name up to the partial name (ignore words after the name)
             return " ".join(full_name).strip()  # Join the words to form the full name
@@ -714,7 +719,8 @@ class MultitaskTokenClassificationPipeline(Pipeline):
             # if key not in ["NE-COARSE-LIT"]:
             all_entities.extend(entities[key])
 
-        print(all_entities)
+        if DEBUG:
+            print(all_entities)
         # print("After remove_included_entities:")
         all_entities = remove_included_entities(all_entities)
         all_entities = remove_trailing_stopwords(all_entities)