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Word_Paraphraser_Seq-2-Seq

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Update app.py

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	# -- coding: utf-8 --
	"""Ai Re-Phraser.py

	Automatically generated by Colaboratory.

	Original file is located at
	https://colab.research.google.com/drive/18bvmXQqMIkk7G0gY_1dUolI08RK6Ajrf

	"""

	# importing the libraries
	import os
	import pandas as pd
	from parrot import Parrot #Parrot offers knobs to control Adequacy, Fluency and Diversity metrics
	import torch
	import warnings
	import nltk
	import warnings
	warnings.filterwarnings("ignore")


	from sentence_splitter import SentenceSplitter, split_text_into_sentences
	splitter = SentenceSplitter(language='en')
	from parrot import Parrot
	from transformers import PegasusForConditionalGeneration, PegasusTokenizer
	from transformers import AutoTokenizer
	from transformers import AutoModelForSeq2SeqLM
	from parrot.filters import Adequacy
	from parrot.filters import Fluency
	from parrot.filters import Diversity

	# Adding the metrics

	adequacy_score = Adequacy()
	fluency_score = Fluency()
	diversity_score= Diversity()
	device= "cuda:0"
	adequacy_threshold = 0.99
	fluency_threshold = 0.90 # Fluency (Is the paraphrase fluent English?)
	diversity_ranker="levenshtein"
	do_diverse=True # Diversity (Lexical / Phrasal / Syntactical) (How much has the paraphrase changed the original sentence?)
	#num_beam_groups (int) — Number of groups to divide num_beams into in order to ensure diversity among different groups of beams
	# adding the model

	model_name = 'tuner007/pegasus_paraphrase'
	torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
	tokenizer = PegasusTokenizer.from_pretrained(model_name)
	model_pegasus = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device)

	def get_max_str(lst):
	return max(lst, key=len)
	def get_response(input_text,num_return_sequences=10,num_beams=10):
	batch = tokenizer.prepare_seq2seq_batch([input_text],truncation=True,padding='longest',max_length=90, return_tensors='pt').to(torch_device)
	translated = model_pegasus.generate(**batch,max_length=90,num_beams=num_beams, num_return_sequences=num_return_sequences, temperature=1.5)
	#num_beam_groups=num_beams, diversity_penalty=0.5
	tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
	try:
	adequacy_filtered_phrases = adequacy_score.filter(input_text,tgt_text, adequacy_threshold, device)
	if len(adequacy_filtered_phrases) > 0 :
	fluency_filtered_phrases = fluency_score.filter(adequacy_filtered_phrases, fluency_threshold, device )
	if len(fluency_filtered_phrases) > 0 :
	diversity_scored_phrases = diversity_score.rank(input_text, fluency_filtered_phrases, diversity_ranker)
	return get_max_str(diversity_scored_phrases)
	else:
	return get_max_str(fluency_filtered_phrases)
	else:
	return get_max_str(adequacy_filtered_phrases)
	except:
	return(get_max_str(tgt_text))

	# Deploying the model

	import gradio as gr

	def get_fun(txt):
	tokens = splitter.split(text=txt)

	txt_paraphrase=''
	for phrase in tokens:
	tmp=get_response(phrase,num_return_sequences=30,num_beams=30)
	txt_paraphrase=txt_paraphrase+' '+tmp
	return txt_paraphrase

	iface = gr.Interface(fn=get_fun, inputs="text", outputs="text", title = " Ai Re-Phraser Q'Hackday")
	iface.launch(inline=False)

	"""# New Section"""