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# -*- coding: utf-8 -*-
"""
Created on Fri Nov 11 16:01:08 2022
@author: Santiago Moreno
"""
import os
import gradio as gr
import sys
import json
default_path = os.path.dirname(os.path.abspath(__file__))
#default_path = default_path.replace('\\', '/')
os.chdir(default_path)
sys.path.insert(0, default_path+'/../scripts')
from src.scripts.functionsner import use_model, tag_sentence, json_to_txt, training_model, characterize_data, upsampling_data, usage_cuda, copy_data
from src.scripts.functionsrc import use_model_rc, training_model_rc, usage_cuda_rc
models_NER = os.listdir(default_path+'/../../models/NER')
models_rc = os.listdir(default_path+'/../../models/RC')
#-------------------------------------------Functions-----------------------------------------------
#--------------------------------------NER-----------------------------------
def Trainer(fast, model_name, standard, input_dir, Upsampling, Cuda):
if fast: epochs = 1
else: epochs = 20
if Cuda:
cuda_info = usage_cuda(True)
else:
cuda_info = usage_cuda(False)
if standard:
copy_data(input_dir)
else:
Error = json_to_txt(input_dir)
if type(Error)==int:
yield 'Error processing the input documents, code error {}'.format(Error)
if Upsampling:
yield cuda_info+'\n'+'-'*20+'Upsampling'+'-'*20
entities_dict=characterize_data()
entities = list(entities_dict.keys())
entities_to_upsample = [entities[i] for i,value in enumerate(entities_dict.values()) if value < 200]
upsampling_data(entities_to_upsample, 0.8, entities)
yield '-'*20+'Training'+'-'*20
else:
yield cuda_info+'\n'+'-'*20+'Training'+'-'*20
Error = training_model(model_name, epochs)
if type(Error)==int:
yield 'Error training the model, code error {}'.format(Error)
else:
yield 'Training complete, model {} could be found at models/{}'.format(model_name,model_name)
def Tagger_sentence(Model, Sentence, Cuda):
if Cuda: cuda_info = usage_cuda(True)
else: cuda_info = usage_cuda(False)
yield cuda_info+'\n'+'-'*20+'Tagging'+'-'*20
results = tag_sentence(Sentence, Model)
if type(results)==int:
yield "Error {}, see documentation".format(results)
else:
yield results['Highligth']
def Tagger_json(Model, Input_file, Output_file, Cuda):
if Cuda: cuda_info = usage_cuda(True)
else: cuda_info = usage_cuda(False)
with open(Output_file, "w", encoding='utf-8') as write_file:
json.dump({'error':'error'}, write_file)
yield cuda_info+'\n'+'-'*20+'Tagging'+'-'*20, {}, Output_file
results = use_model(Model, Input_file.name, Output_file)
if type(results)==int:
error_dict = {}
yield "Error {}, see documentation".format(results), error_dict, Output_file
else:
yield { "text" : results['text'], 'entities': results['entities']}, results, Output_file
#--------------------RC-------------------------------
def Trainer_RC(fast, model_name, input_file, rel2id_file, Cuda):
if fast: epochs = 1
else: epochs = 200
if Cuda:
cuda_info = usage_cuda_rc(True)
else:
cuda_info = usage_cuda_rc(False)
yield cuda_info+'\n'+'-'*20+'Training'+'-'*20
Error = training_model_rc(model_name, input_file.name, rel2id_file.name ,epochs)
if type(Error)==int:
yield 'Error training the model, code error {}'.format(Error)
else:
yield 'Training complete, model {} could be found at models/{}'.format(model_name,model_name)
def Tagger_document_RC(Model, Input_file, Output_file, Cuda):
if Cuda: cuda_info = usage_cuda_rc(True)
else: cuda_info = usage_cuda_rc(False)
with open(Output_file, "w", encoding='utf-8') as write_file:
json.dump({'error':'error'}, write_file)
yield {'cuda':cuda_info}, Output_file
results = use_model_rc(Model, Input_file.name, Output_file)
if type(results)==int:
error_dict = {}
yield error_dict, Output_file
else:
yield results, Output_file
#---------------------------------GUI-------------------------------------
def execute_GUI():
global models_NER
with gr.Blocks(title='NER', css="#title {font-size: 150% } #sub {font-size: 120% } ") as demo:
gr.Markdown("Named Entity Recognition(NER) and Relation Classification (RC) by GITA and Pratec Group S.A.S.",elem_id="title")
gr.Markdown("Software developed by Santiago Moreno, Daniel Escobar, and Rafael Orozco",elem_id="sub")
gr.Markdown("Named Entity Recognition(NER) and Relation Classification (RC) System.")
with gr.Tab("NER"):
gr.Markdown("Use Tagger to apply NER from a pretrained model in a sentence or a given document in INPUT (.JSON) format.")
gr.Markdown("Use Trainer to train a new NER model from a directory of documents in PRATECH (.JSON) format.")
with gr.Tab("Tagger"):
with gr.Tab("Sentence"):
with gr.Row():
with gr.Column():
b = gr.Radio(list(models_NER), label='Model')
inputs =[
b,
gr.Textbox(placeholder="Enter sentence here...", label='Sentence'),
gr.Radio([True,False], label='CUDA', value=False),
]
tagger_sen = gr.Button("Tag")
output = gr.HighlightedText()
tagger_sen.click(Tagger_sentence, inputs=inputs, outputs=output)
b.change(fn=lambda value: gr.update(choices=list(os.listdir('../../models/NER'))), inputs=b, outputs=b)
gr.Examples(
examples=[
['CCC',"Camara de comercio de medell铆n. El ciudadano JAIME JARAMILLO VELEZ identificado con C.C. 12546987 ingres贸 al plantel el d铆a 1/01/2022"],
['CCC',"Raz贸n Social GASEOSAS GLACIAR S.A.S, ACTIVIDAD PRINCIPAL fabricaci贸n y distribuci贸n de bebidas endulzadas"]
],
inputs=inputs
)
with gr.Tab("Document"):
with gr.Row():
with gr.Column():
c = gr.Radio(list(models_NER), label='Model')
inputs =[
c,
gr.File(label='Input data file'),
gr.Textbox(placeholder="Enter path here...", label='Output data file path'), #value='../../data/Tagged/document_tagged.json'),
gr.Radio([True,False], label='CUDA', value=False),
]
tagger_json = gr.Button("Tag")
output = [
gr.HighlightedText(),
gr.JSON(),
gr.File(),
]
models_NER = os.listdir(default_path+'/../../models/NER')
tagger_json.click(Tagger_json, inputs=inputs, outputs=output)
c.change(fn=lambda value: gr.update(choices=list(os.listdir('../../models/NER'))), inputs=c, outputs=c)
with gr.Tab("Trainer"):
with gr.Row():
with gr.Column():
train_input = inputs =[
gr.Radio([True,False], label='Fast training', value=False),
gr.Textbox(placeholder="Enter model name here...", label='New model name'),
gr.Radio([True,False], label='Standard input', value=False),
gr.Textbox(placeholder="Enter path here...", label='Input data directory path'),
gr.Radio([True,False], label='Upsampling', value=False),
gr.Radio([True,False], label='CUDA', value=False),
]
trainer = gr.Button("Train")
train_output = gr.TextArea(placeholder="Output information", label='Output')
with gr.Tab("RC"):
gr.Markdown("Use Tagger to apply RC from a pretrained model in document in (.TXT) CONLL04 format.")
gr.Markdown("Use Trainer to train a new RC model from a file (.TXT) CONLL04 format and the rel2id file (.JSON).")
with gr.Tab("Tagger Document"):
with gr.Row():
with gr.Column():
c = gr.Radio(list(models_rc), label='Model')
inputs =[
c,
gr.File(label='Input data file'),
gr.Textbox(placeholder="Enter path here...", label='Output data file path (.JSON)'), #value='../../data/Tagged/document_tagged.json'),
gr.Radio([True,False], label='CUDA', value=False),
]
tagger_json = gr.Button("Tag")
output = [
gr.JSON(),
gr.File(),
]
tagger_json.click(Tagger_document_RC, inputs=inputs, outputs=output)
c.change(fn=lambda value: gr.update(choices=list(os.listdir('../../models/RC'))), inputs=c, outputs=c)
with gr.Tab("Trainer"):
with gr.Row():
with gr.Column():
train_input = inputs =[
gr.Radio([True,False], label='Fast training', value=True),
gr.Textbox(placeholder="Enter model name here...", label='New model name'),
gr.File(label='Input train file (.TXT)'),
gr.File(label='Input rel2id file (.JSON)'),
gr.Radio([True,False], label='CUDA', value=False),
]
trainer = gr.Button("Train")
train_output = gr.TextArea(placeholder="Output information", label='Output')
trainer.click(Trainer_RC, inputs=train_input, outputs=train_output)
demo.queue()
demo.launch()
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