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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
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)
def keyphrases_out(input): #Not used but might be useful
input = input.replace("\n", " ")
keyphrases = extractor(input)
out = "The Key Phrases in your text are:\n\n"
for k in keyphrases:
out += k + "\n"
return keyphrases
def wikipedia_search(input):
input = input.replace("\n", " ")
keyphrases = extractor(input)
wiki = wk.Wikipedia('en')
for k in keyphrases:
page = wiki.page(k)
if page.exists():
break
return page.summary
demo = gr.Interface(fn=wikipedia_search, inputs = "text", outputs = "text")
demo.launch()
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