Update app.py

app.py

@@ -17,44 +17,6 @@ def get_prediction(context, question):
   answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(inputs['input_ids'][0][answer_start:answer_end]))
   
   return answer
-
-def normalize_text(s):
-  """Removing articles and punctuation, and standardizing whitespace are all typical text processing steps."""
-  import string, re
-  def remove_articles(text):
-    regex = re.compile(r"\b(a|an|the)\b", re.UNICODE)
-    return re.sub(regex, " ", text)
-  def white_space_fix(text):
-    return " ".join(text.split())
-  def remove_punc(text):
-    exclude = set(string.punctuation)
-    return "".join(ch for ch in text if ch not in exclude)
-  def lower(text):
-    return text.lower()
-
-  return white_space_fix(remove_articles(remove_punc(lower(s))))
-
-def exact_match(prediction, truth):
-    return bool(normalize_text(prediction) == normalize_text(truth))
-
-def compute_f1(prediction, truth):
-  pred_tokens = normalize_text(prediction).split()
-  truth_tokens = normalize_text(truth).split()
-  
-  # if either the prediction or the truth is no-answer then f1 = 1 if they agree, 0 otherwise
-  if len(pred_tokens) == 0 or len(truth_tokens) == 0:
-    return int(pred_tokens == truth_tokens)
-  
-  common_tokens = set(pred_tokens) & set(truth_tokens)
-  
-  # if there are no common tokens then f1 = 0
-  if len(common_tokens) == 0:
-    return 0
-  
-  prec = len(common_tokens) / len(pred_tokens)
-  rec = len(common_tokens) / len(truth_tokens)
-  
-  return round(2 * (prec * rec) / (prec + rec), 2)
   
 def question_answer(context, question):
   prediction = get_prediction(context,question)
@@ -63,9 +25,9 @@ def question_answer(context, question):
 def greet(texts):
     question = texts[:len(texts)]
     answer = texts[len(texts):]
-    for question, answer in texts:
-        question_answer(context, question)
-    return texts
+    # for question, answer in texts:
+    #     question_answer(context, question)
+    return answer
 
 iface = gr.Interface(fn=greet, inputs="text", outputs="text")
 iface.launch()