Hotfix

test.py

@@ -1,147 +0,0 @@
-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
-from transformers import BertForQuestionAnswering
-from transformers import BertTokenizer
-
-# =====[ 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 Chatbot"
-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(share=True)