import streamlit as st import pandas as pd import streamlit.components.v1 as components import textwrap as tw #st.set_page_config( initial_sidebar_state="expanded", margin_top = 20, margin_left = 20, margin_right = 10, margin_bottom=50, footer_text = "Creative Commons ... " ) st.set_page_config(page_title='Portparser', layout="wide") ##9bc2d1,#9bc2d1,#2f76a3 page_bg_img = f""" <style> [data-testid="stAppViewContainer"] > .main {{ background: linear-gradient(180deg, #88bbcf,#f1f1f1,#f1f1f1,#f1f1f1); /**#ccebff 10%, #f1f1f1 90% #0088be); padding-left:4rem; padding-right:4rem; background-image: url("img/nilc.png"); background-repeat: repeat; background-position: center center; background-attachment: local; /**#f1f1f1/** #008fb3/**#accad2;/**#b3b3ff;**/ /**background-image: url("https://i.postimg.cc/4xgNnkfX/Untitled-design.png"); background-position: center center; background-repeat: no-repeat; background-attachment: local;**/ }} [data-testid="stForm"] {{ background-color: #9bc2d1;/**#7ebac9;/**#0086b3;**/ }} .appview-container .main .block-container {{ padding-top: 1rem; padding-bottom: 3rem; }} h1 {{ color:#003d66;/**#143350**/; padding-left:1rem; padding-right:1rem; }} [class="css-1n543e5 e1ewe7hr5"] {{ background-color: #ffffff /**#000066; /**#9bc2d1;/**#7ebac9;/**#0086b3;**/ }} [class="css-1n543e5 e1ewe7hr5"]:hover {{ background-color: #8080ff; /**#9bc2d1;/**#7ebac9;/**#0086b3;**/ color:white; border: solid 1px #000066; }} a:link{{ color:#0088be; }} a:hover {{ color: #7733ff/**#8080ff**/; }} button{{ padding-left:1rem; padding-right:1rem; border-radius: 15%; }} button:hover {{ color:#7733ff; border:solid 1px #7733ff; }} </style> """ # head style head_css = """ <style> [class="css-ocqkz7 esravye3"] { /**background-color: #9bc2d1;**/ } [class="css-ocqkz7 esravye3"]{ /**row1**/ margin:0px 0px 0px 0px; padding:0; } .stApp { background-image: url("portparser_brasil1.jpg"); background-repeat: repeat; background-position: center; } </style> """ #class="css-o7kwkx esravye0"] a = """ <style> div.css-10r1649 esravye0 { background-color: red; } </style> """ custom_html = """ <div class="banner" style="background-color:#0088be; color:white"> <h1>PortParser</h1> <!--<img src="https://img.freepik.com/premium-photo/wide-banner-with-many-random-square-hexagons-charcoal-dark-black-color_105589-1820.jpg" alt="Banner Image">--> </div> <style> .banner { width: 160%; height: 200px; overflow: hidden; } .banner img { width: 100%; object-fit: cover; } </style> """ #<div width="449" data-testid="stVerticalBlock" class="css-10r1649 esravye0"> #components.html(custom_html) st.markdown(page_bg_img, unsafe_allow_html=True) st.markdown(head_css, unsafe_allow_html=True) row2 = st.columns([6,2,3]) with row2[0]: st.markdown("<p style='padding-bottom:25px; padding-top:50px'><b style='font-size:calc(40px + 2vw); color:#003d66;line-height: 40px'><i>Portparser</i></b><br><b style='font-size:18px;color:#266087;line-height:4px'>\ A parsing model for Brazilian Portuguese</b></p>",unsafe_allow_html=True) st.write('This is Portparser, a parsing model for Brazilian Portuguese that follows the Universal Dependencies (UD) framework.\ We built our model by using a recently released manually annotated corpus, the Porttinari-base, \ and we explored different parsing methods and parameters for training. We also test multiple embedding models and parsing methods. \ Portparse is the result of the best combination achieved in our experiments.') st.write('Our model is explained in the paper https://aclanthology.org/2024.propor-1.41.pdf, and all datasets and full instructions to reproduce our experiments \ freely available at https://github.com/LuceleneL/Portparser. More details about this work may also be found at \ the POeTiSA project webpage at https://sites.google.com/icmc.usp.br/poetisa/.') with st.expander('How to cite?', expanded=False): st.code(""" @inproceedings{lopes2024towards, title={Towards Portparser-a highly accurate parsing system for Brazilian Portuguese following the Universal Dependencies framework}, author={Lopes, Lucelene and Pardo, Thiago}, booktitle={Proceedings of the 16th International Conference on Computational Processing of Portuguese}, pages={401--410}, year={2024} }""") with row2[2]: st.image('img/wordcloud_brasil5.png') #wordcloud_vertical1.png #st.markdown('##### Write a sentence and run to parse:') #with st.sidebar: # st.header("About Portparser") # with st.expander('How was Portparser developed?'): # st.write('We build our model by using a recently released manually annotated corpus, the Porttinari-base, \ # and explored different parsing methods and parameters for training. We also test multiple embedding models and parsing methods. \ # Portparse is the result of the best combination achieved in our experiments.' ) print('---------------------------') st.markdown(""" <script language="JavaScript" type="text/javascript" src="arborator-draft.js"></script> <script language="JavaScript" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/d3/4.10.0/d3.js"></script> <script src="https://code.jquery.com/jquery-3.2.1.min.js" integrity="sha256-hwg4gsxgFZhOsEEamdOYGBf13FyQuiTwlAQgxVSNgt4=" crossorigin="anonymous"></script> <link rel="stylesheet" href="arborator-draft.css" type="text/css" /> <script src="d3.js"></script> <script src="jquery-3.2.1.min.js"></script> <script> new ArboratorDraft(); </script>""" ,unsafe_allow_html=True) def make_conllu(path_text, path_input): try: os.system(f'python portTokenizer/portTok.py -o {path_input} -m -t -s S0000 {path_text}') return 'Converti o texto para conllu.' #st.text(open(path_input,'r',encoding='utf-8').read()) except Exception as e: return str(e) def make_embedding(path_input, path_embedding, model_selected): try: os.system(f'python ./wembedding_service/compute_wembeddings.py {path_input} {path_embedding} --model {model_selected}') return 'Fiz as embeddings.' except Exception as e: return str(e) def make_predictions(path_input, path_prediction): try: os.system(f'python ./udpipe2/udpipe2.py Portparser_model --predict --predict_input {path_input} --predict_output {path_prediction}') return f'Fiz a predição.'# {path_input}, {path_prediction}' except Exception as e: return str(e) def get_predictions(path_prediction): try: with open(path_prediction, 'r') as f: st.text(f.read()) except Exception as e: st.text('Resposta: '+e) st.write('Write a sentence and run to parse:') with st.form("parser"): text = st.text_input('Text: ') model = st.selectbox('Pick a model (Pick a embedding model:):', ['bert-base-portuguese-cased','bert-base-multilingual-uncased','robeczech-base','xlm-roberta-base']) model_selected = model+'-last4' submit = st.form_submit_button('Run') tab1, tab2, tab3, tab4 = st.tabs(["Running status" ,"Table", "Raw", "Tree"]) if submit: import sys, os print(type(text)) tab1.text('input: '+text) files = 'temp' input_text = 'text_input.txt' input_conllu = 'input.conllu' #'h2104_0_test.conllu' embedding_conllu = 'input.conllu.npz' #'h2104_0_test.conllu.npz' prediction_conllu = 'input_prediction.conllu' model = 'Portparser_model' path_text = os.path.join(files, input_text) path_input = os.path.join(files, input_conllu) path_prediction = os.path.join(files, prediction_conllu) path_embedding = os.path.join(files,embedding_conllu) with open(path_text,'w',encoding='utf-8') as f: f.write(text) import time with st.spinner('Transforming text into .conllu...'): #st.progress(0,text="Transformando texto para o formato .conllu"): time.sleep(3) tab1.text(make_conllu(path_text, path_input)) with st.spinner('Processing embeddings...'): #st.progress(0,text="Processando embeddings"): time.sleep(6) tab1.text(make_embedding(path_input, path_embedding, model_selected)) with st.spinner('Making predictions...'): #st.progress(0,text="Realizando a predição"): time.sleep(6) tab1.text(make_predictions(path_input, path_prediction)) try: with open(path_prediction, 'r', encoding='utf-8') as f: content = f.read() tab3.text(content) #tab4.markdown(f'<conll>{content[4:]}</conll>',unsafe_allow_html=True) content = content.split('\n') #tab2.text(content[:4]) table = pd.DataFrame([line.split('\t') for line in content[4:]]) table.columns = ['ID','FORM','LEMMA','UPOS','XPOS','FEATS','HEAD','DEPREL','DEPS','MISC'] tab2.dataframe(table, use_container_width=True) except Exception as e: st.text('Não deu certo a predição.'+str(e)+repr(e)) row1 = st.columns([18,3,4,4]) with row1[1]: st.image('img/nilc-removebg.png') with row1[2]: st.image('img/poetisa2.png') with row1[3]: st.image('img/icmc.png') st.markdown(""" <script language="JavaScript" type="text/javascript" src="arborator-draft.js"></script> <script language="JavaScript" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/d3/4.10.0/d3.js"></script> <script src="https://code.jquery.com/jquery-3.2.1.min.js" integrity="sha256-hwg4gsxgFZhOsEEamdOYGBf13FyQuiTwlAQgxVSNgt4=" crossorigin="anonymous"></script> <link rel="stylesheet" href="arborator-draft.css" type="text/css" /> <script src="d3.js"></script> <script src="jquery-3.2.1.min.js"></script> <script> new ArboratorDraft(); </script>""" ,unsafe_allow_html=True)