Update app.py

app.py

@@ -1,10 +1,65 @@
 import gradio as gr
-# from transformers import BertForQuestionAnswering
+from transformers import BertForQuestionAnswering
 
-# model = BertForQuestionAnswering.from_pretrained("bert-base-uncased")
+model = BertForQuestionAnswering.from_pretrained("bert-base-uncased")
 
-def greet(name):
-    return "Hello " + name + "!!"
+def get_prediction(context, question):
+  inputs = tokenizer.encode_plus(question, context, return_tensors='pt').to(device)
+  outputs = model(**inputs)
+  
+  answer_start = torch.argmax(outputs[0])  
+  answer_end = torch.argmax(outputs[1]) + 1 
+  
+  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)
+  return prediction
+    
+def greet(texts):
+    # for question, answer in texts:
+    #     question_answer(context, question)
+    return texts
 
 iface = gr.Interface(fn=greet, inputs="text", outputs="text")
 iface.launch()