app.py

import os
import gradio as gr
import numpy as np
import wikipediaapi as wk
from transformers import (
    TokenClassificationPipeline,
    AutoModelForTokenClassification,
    AutoTokenizer,
    BertForQuestionAnswering,
    BertTokenizer
)
from transformers.pipelines import AggregationStrategy
import torch

# =====[ DEFINE PIPELINE ]===== #
class KeyphraseExtractionPipeline(TokenClassificationPipeline):
    def __init__(self, model, *args, **kwargs):
        super().__init__(
            model=AutoModelForTokenClassification.from_pretrained(model),
            tokenizer=AutoTokenizer.from_pretrained(model),
            *args,
            **kwargs
        )

    def postprocess(self, model_outputs):
        results = super().postprocess(
            model_outputs=model_outputs,
            aggregation_strategy=AggregationStrategy.SIMPLE,
        )
        return np.unique([result.get("word").strip() for result in results])

# =====[ LOAD PIPELINE ]===== #
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:
    keyphrases = extractor(text)
    return keyphrases

def wikipedia_search(input: str) -> str:
    input = input.replace("\n", " ")
    keyphrases = keyphrases_extraction(input)
    wiki = wk.Wikipedia('en')
    
    try :
        #TODO: add better extraction and search
        keyphrase_index = 0
        page = wiki.page(keyphrases[keyphrase_index])

        while not ('.' in page.summary) or not page.exists():
            keyphrase_index += 1
            if keyphrase_index == len(keyphrases):
                raise Exception
            page = wiki.page(keyphrases[keyphrase_index])
        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 Conversational Agent"
examples = [
    ["Where is the Eiffel Tower?"],
    ["What is the population of France?"]
]

demo = gr.Interface(
    title = title,

    fn=answer_question,
    inputs = "text", 
    outputs = "text",

    examples=examples,
    )

if __name__ == "__main__":
    demo.launch()