app.py

# -*- coding: utf-8 -*-
"""ABSTRACTGEN_ES FINAL.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1XdfeMcdDbRuRmOGGiOmkiCP9Yih5JXyF

# installs
"""

import os 
os.system('pip install gpt_2_simple')
os.system('pip install os.system')
os.system('pip install gradio')
os.system('pip install huggingface_hub')
os.system('pip install easynmt')
os.system('pip install sentence-transformers')
os.system('curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash')
os.system('apt-get install git-lfs')
os.system('git lfs install')
os.system('git clone https://huggingface.co/franz96521/AbstractGeneratorES ')
#os.system('cd AbstractGeneratorES')
print(os.getcwd())
print(os.listdir())
# Commented out IPython magic to ensure Python compatibility.
# %cd '/content/AbstractGeneratorES'

"""# Init"""

import gpt_2_simple as gpt2
import os
import tensorflow as tf
import pandas as pd
import re

model_name = "124M"
if not os.path.isdir(os.path.join("models", model_name)):
	print(f"Downloading {model_name} model...")
	gpt2.download_gpt2(model_name=model_name)

path = os.getcwd()+'/AbstractGeneratorES/AbstractGenerator/'
checkpoint_dir =path+'weights/'
data_path = path+'TrainigData/'



file_name_en = 'en'
file_path_en = data_path+file_name_en

file_name_es = 'es'
file_path_es = data_path+file_name_es


prefix= '<|startoftext|>'
sufix ='<|endoftext|>'

import gradio as gr
import random
from easynmt import EasyNMT

from sentence_transformers import SentenceTransformer, util

def generateAbstract(text):
    tf.compat.v1.reset_default_graph()
    sess = gpt2.start_tf_sess()
    gpt2.load_gpt2(sess,checkpoint_dir=checkpoint_dir,run_name='run1')
    txt = gpt2.generate(sess,prefix=str(text)+"\nABSTRACT", return_as_list=True,truncate=sufix,checkpoint_dir=checkpoint_dir,nsamples=1)[0]
    return txt
def removeAbstract(text):
    p = text.find("Introducción")
    p2 = text.find("INTRODUCCIÓN")
    print(p,p2)
    if(p != -1):        
       return (text[:p] , text[p:] )  
    if(p2 != -1):        
       return (text[:p2] , text[p2:] )  

def generated_similarity(type_of_input, cn_text): 
  if(type_of_input == "English"):
    tf.compat.v1.reset_default_graph()
    model2 = EasyNMT('opus-mt')
    cn_text = model2.translate(cn_text, target_lang='es')


    print(cn_text)
    abstract_original , body  = removeAbstract(cn_text)
    tf.compat.v1.reset_default_graph()

    generated_Abstract = generateAbstract(body)

    sentences = [abstract_original, generated_Abstract]

    model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')  

    #Compute embedding for both lists
    embedding_1= model.encode(sentences[0], convert_to_tensor=True)
    embedding_2 = model.encode(sentences[1], convert_to_tensor=True)

    generated_similarity = util.pytorch_cos_sim(embedding_1, embedding_2)
    ## tensor([[0.6003]])
    return f'''TEXTO SIN ABSTRACT\n
        {body}\n
        ABSTRACT ORIGINAL\n
        {abstract_original}\n
        ABSTRACT GENERADO\n
        {generated_Abstract}\n
        SIMILARIDAD DE ABSTRACT: {float(round(generated_similarity.item()*100, 3))}%
        '''
  elif type_of_input == "Spanish":
    abstract_original , body  = removeAbstract(cn_text)
    tf.compat.v1.reset_default_graph()

    generated_Abstract = generateAbstract(body)

    sentences = [abstract_original, generated_Abstract]

    model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')  

    #Compute embedding for both lists
    embedding_1= model.encode(sentences[0], convert_to_tensor=True)
    embedding_2 = model.encode(sentences[1], convert_to_tensor=True)

    generated_similarity = util.pytorch_cos_sim(embedding_1, embedding_2)
    return f'''TEXTO SIN ABSTRACT\n
    {body}\n
    ABSTRACT ORIGINAL\n
    {abstract_original}\n
    ABSTRACT GENERADO\n
    {generated_Abstract}\n
    SIMILARIDAD DE ABSTRACT: {float(round(generated_similarity.item()*100, 3))}%
    '''
def generated_abstract(type_of_input, cn_text):
  if type_of_input == "English":
    tf.compat.v1.reset_default_graph()
    model2 = EasyNMT('opus-mt')
    cn_text = model2.translate(cn_text, target_lang='es')
    generated_Abstract = generateAbstract(cn_text)
    return f'''TEXTO SIN ABSTRACT\n
    {cn_text}\n
    ABSTRACT GENERADO\n
    {generated_Abstract}\n
    '''
  elif type_of_input == "Spanish":
    tf.compat.v1.reset_default_graph()
    generated_Abstract = generateAbstract(cn_text)
    return f'''TEXTO SIN ABSTRACT\n
    {cn_text}\n
    ABSTRACT GENERADO\n
    {generated_Abstract}\n
    '''

block = gr.Blocks(theme="dark")

with block:
    gr.Markdown('''ABSTRACTGEN_ES''')
    gr.Markdown('''An app that can generate abstracts in Spanish based on the text that you input via document text and if you already have an abstract and need a different idea, check how similar the new abstract is to the original one.
        ''')
    gr.Markdown(''' We used Blocks (beta), which allows you to build web-based demos in a flexible way using the gradio library. Blocks is a more low-level and flexible alternative to the core Interface class.
        The main problem with this library right now is that
        it doesn't support some functionality that Interface
        class has''')
    gr.Markdown('''To get more info about this project go to: https://sites.google.com/up.edu.mx/somos-pln-abstractgen-es/inicio?authuser=0''')
    with gr.Tab("Full text and text similarity"):
        gr.Markdown("Choose the language:")
        type_of_input = gr.inputs.Radio(["English", "Spanish"], label="Input Language")
        with gr.Row():
            cn_text = gr.inputs.Textbox(placeholder="Full text", lines=7)
        with gr.Row():
            cn_results1 = gr.outputs.Textbox(label="Abstract generado")
        cn_run = gr.Button("Run")
        cn_run.click(generated_similarity, inputs=[type_of_input, cn_text], outputs=[cn_results1])

    with gr.Tab("Only text with no abstract"):
        gr.Markdown("Choose the language:")
        type_of_input = gr.inputs.Radio(["English", "Spanish"], label="Input Language")
        with gr.Row():
            cn_text = gr.inputs.Textbox(placeholder="Text without abstract", lines=7)
        with gr.Row():
            cn_results1 = gr.outputs.Textbox(label="Abstract generado")
        cn_run = gr.Button("Run")
        cn_run.click(generated_abstract, inputs=[type_of_input, cn_text], outputs=cn_results1)

block.launch(debug = True)