bert question answering model trained on squad added

app.py

@@ -1,13 +1,16 @@
 import os
 import gradio as gr
+import numpy as np
 import wikipediaapi as wk
 from transformers import (
     TokenClassificationPipeline,
     AutoModelForTokenClassification,
     AutoTokenizer,
 )
+import torch
 from transformers.pipelines import AggregationStrategy
-import numpy as np
+from transformers import BertForQuestionAnswering
+from transformers import BertTokenizer
 
 # =====[ DEFINE PIPELINE ]===== #
 class KeyphraseExtractionPipeline(TokenClassificationPipeline):
@@ -27,8 +30,10 @@ class KeyphraseExtractionPipeline(TokenClassificationPipeline):
         return np.unique([result.get("word").strip() for result in results])
 
 # =====[ LOAD PIPELINE ]===== #
-model_name = "ml6team/keyphrase-extraction-kbir-inspec"
-extractor = KeyphraseExtractionPipeline(model=model_name)
+keyPhraseExtractionModel = "ml6team/keyphrase-extraction-kbir-inspec"
+extractor = KeyphraseExtractionPipeline(model=keyPhraseExtractionModel)
+model = BertForQuestionAnswering.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad')
+tokenizer = BertTokenizer.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad')
 
 #TODO: add further preprocessing
 def keyphrases_extraction(text: str) -> str:
@@ -53,6 +58,71 @@ def wikipedia_search(input: str) -> str:
         return  page.summary
     except:
         return "I cannot answer this question"
+    
+def answer_question(question):
+
+    context = wikipedia_search(question)
+    if context == "I cannot answer this question":
+        return context
+
+    # ======== Tokenize ========
+    # Apply the tokenizer to the input text, treating them as a text-pair.
+    input_ids = tokenizer.encode(question, context)
+
+    # Report how long the input sequence is. if longer than 512 tokens, make it shorter
+    while(len(input_ids) > 512):
+        input_ids.pop()
+
+    print('Query has {:,} tokens.\n'.format(len(input_ids)))
+
+    # ======== Set Segment IDs ========
+    # Search the input_ids for the first instance of the `[SEP]` token.
+    sep_index = input_ids.index(tokenizer.sep_token_id)
+
+    # The number of segment A tokens includes the [SEP] token istelf.
+    num_seg_a = sep_index + 1
+
+    # The remainder are segment B.
+    num_seg_b = len(input_ids) - num_seg_a
+
+    # Construct the list of 0s and 1s.
+    segment_ids = [0]*num_seg_a + [1]*num_seg_b
+
+    # There should be a segment_id for every input token.
+    assert len(segment_ids) == len(input_ids)
+
+    # ======== Evaluate ========
+    # Run our example through the model.
+    outputs = model(torch.tensor([input_ids]), # The tokens representing our input text.
+                    token_type_ids=torch.tensor([segment_ids]), # The segment IDs to differentiate question from answer_text
+                    return_dict=True) 
+
+    start_scores = outputs.start_logits
+    end_scores = outputs.end_logits
+
+    # ======== Reconstruct Answer ========
+    # Find the tokens with the highest `start` and `end` scores.
+    answer_start = torch.argmax(start_scores)
+    answer_end = torch.argmax(end_scores)
+
+    # Get the string versions of the input tokens.
+    tokens = tokenizer.convert_ids_to_tokens(input_ids)
+
+    # Start with the first token.
+    answer = tokens[answer_start]
+
+    # Select the remaining answer tokens and join them with whitespace.
+    for i in range(answer_start + 1, answer_end + 1):
+        
+        # If it's a subword token, then recombine it with the previous token.
+        if tokens[i][0:2] == '##':
+            answer += tokens[i][2:]
+        
+        # Otherwise, add a space then the token.
+        else:
+            answer += ' ' + tokens[i]
+
+    return 'Answer: "' + answer + '"'
 
 # =====[ DEFINE INTERFACE ]===== #'
 title = "Azza Chatbot"
@@ -62,10 +132,11 @@ examples = [
 ]
 
 
+
 demo = gr.Interface(
     title = title,
 
-    fn=wikipedia_search,
+    fn=answer_question,
     inputs = "text", 
     outputs = "text",