dataset_extraction.py

from typing import List, Tuple
import torch
import nltk
from SciAssist import DatasetExtraction

device = "gpu" if torch.cuda.is_available() else "cpu"
de_pipeline = DatasetExtraction(os_name="nt")


def de_for_str(input):
    list_input = nltk.sent_tokenize(input)
    results = de_pipeline.extract(list_input, type="str", save_results=False)

    # output = []
    # for res in results["dataset_mentions"]:
    #     output.append(f"{res}\n\n")
    # return "".join(output)

    output = []
    for mention_pair in results["dataset_mentions"]:
        output.append((mention_pair[0], mention_pair[1]))
        output.append(("\n\n", None))
    return output

def de_for_file(input):
    if input == None:
        return None
    filename = input.name
    # Identify the format of input and parse reference strings
    if filename[-4:] == ".txt":
        results = de_pipeline.extract(filename, type="txt", save_results=False)
    elif filename[-4:] == ".pdf":
        results = de_pipeline.extract(filename, type="pdf", save_results=False)
    else:
        return [("File Format Error !", None)]

    output = []
    for mention_pair in results["dataset_mentions"]:
        output.append((mention_pair[0], mention_pair[1]))
        output.append(("\n\n", None))
    return output


de_str_example = "BAKIS incorporates information derived from the bank balance sheets and supervisory reports of all German banks ."