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 ."