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Runtime error
| import gradio as gr | |
| import numpy as np | |
| import pytesseract as pt | |
| import pdf2image | |
| from fpdf import FPDF | |
| import re | |
| import nltk | |
| from nltk.tokenize import sent_tokenize | |
| from nltk.tokenize import word_tokenize | |
| import os | |
| import pdfkit | |
| import yake | |
| from summarizer import Summarizer,TransformerSummarizer | |
| from transformers import pipelines | |
| nltk.download('punkt') | |
| from transformers import AutoTokenizer, AutoModelForPreTraining, AutoConfig, AutoModel | |
| # model_name = 'distilbert-base-uncased' | |
| model_name = 'nlpaueb/legal-bert-base-uncased' | |
| #model_name = 'laxya007/gpt2_legal' | |
| # model_name = 'facebook/bart-large-cnn' | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| tokenizer = AutoTokenizer.from_pretrained("laxya007/gpt2_BSA_Legal_Initiproject_OE_OS_BRM") | |
| model = AutoModelForCausalLM.from_pretrained("laxya007/gpt2_BSA_Legal_Initiproject_OE_OS_BRM") | |
| bert_legal_model = Summarizer(custom_model= model, custom_tokenizer= tokenizer) | |
| print('Using model {}\n'.format(model_name)) | |
| def lincoln(input_text): | |
| output_text= bert_legal_model(input_text, min_length = 8, ratio = 0.05) | |
| iface = gr.Interface( | |
| lincoln, | |
| "text", | |
| "text" | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch(share=False) |