Upload app.py

app.py

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+# -*- 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)