Update qbmodel.py

qbmodel.py

@@ -1,52 +1,52 @@
-from typing import List, Tuple
-import nltk
-import sklearn
-from project.tfidf import TfidfWikiGuesser
-import numpy as np
-import pandas as pd
-
-
-class QuizBowlModel:
-
-    def __init__(self):
-        """
-        Load your model(s) and whatever else you need in this function.
-
-        Do NOT load your model or resources in the guess_and_buzz() function, 
-        as it will increase latency severely. 
-        """
-        
-        self.guesser = TfidfWikiGuesser() #can specify different wikidump if needed 
-        print("model loaded")
-
-
-
-    def guess_and_buzz(self, question_text: List[str]) -> List[Tuple[str, bool]]:
-        """
-        This function accepts a list of question strings, and returns a list of tuples containing
-        strings representing the guess and corresponding booleans representing 
-        whether or not to buzz. 
-
-        So, guess_and_buzz(["This is a question"]) should return [("answer", False)]
-
-        If you are using a deep learning model, try to use batched prediction instead of 
-        iterating using a for loop.
-        """
-
-        answers = []
-        top_guesses = 3 #guesser will return this amount guesses for each question (in sorted confidence)
-
-        for question in question_text:
-            guesses = self.guesser.make_guess(question, num_guesses=top_guesses)
-            #print(guesses)
-
-            #do the buzzing 
-
-            #make a tuple and add to answers list 
-            tup = (guesses[0], True)
-            answers.append(tup)
-
-        return answers
-
-
-        
+from typing import List, Tuple
+import nltk
+import sklearn
+from tfidf import TfidfWikiGuesser
+import numpy as np
+import pandas as pd
+
+
+class QuizBowlModel:
+
+    def __init__(self):
+        """
+        Load your model(s) and whatever else you need in this function.
+
+        Do NOT load your model or resources in the guess_and_buzz() function, 
+        as it will increase latency severely. 
+        """
+        
+        self.guesser = TfidfWikiGuesser() #can specify different wikidump if needed 
+        print("model loaded")
+
+
+
+    def guess_and_buzz(self, question_text: List[str]) -> List[Tuple[str, bool]]:
+        """
+        This function accepts a list of question strings, and returns a list of tuples containing
+        strings representing the guess and corresponding booleans representing 
+        whether or not to buzz. 
+
+        So, guess_and_buzz(["This is a question"]) should return [("answer", False)]
+
+        If you are using a deep learning model, try to use batched prediction instead of 
+        iterating using a for loop.
+        """
+
+        answers = []
+        top_guesses = 3 #guesser will return this amount guesses for each question (in sorted confidence)
+
+        for question in question_text:
+            guesses = self.guesser.make_guess(question, num_guesses=top_guesses)
+            #print(guesses)
+
+            #do the buzzing 
+
+            #make a tuple and add to answers list 
+            tup = (guesses[0], True)
+            answers.append(tup)
+
+        return answers
+
+
+