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import os
import gradio as gr
import wikipediaapi as wk
from transformers import (
TokenClassificationPipeline,
AutoModelForTokenClassification,
AutoTokenizer,
)
from transformers.pipelines import AggregationStrategy
import numpy as np
# =====[ 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 ]===== #
model_name = "ml6team/keyphrase-extraction-kbir-inspec"
extractor = KeyphraseExtractionPipeline(model=model_name)
#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
page = wiki.page(keyphrases[0])
return page.summary
except:
return "I cannot answer this question"
# =====[ DEFINE INTERFACE ]===== #'
title = "Azza Chatbot"
examples = [
["Where is the Eiffel Tower?"],
["What is the population of France?"]
]
demo = gr.Interface(
title = title,
fn=wikipedia_search,
inputs = "text",
outputs = "text",
examples=examples,
)
if __name__ == "__main__":
demo.launch(share=True)
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