Upload app.py
Browse filesAdded app.py running in vm.
app.py
CHANGED
@@ -1,4 +1,274 @@
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import streamlit as st
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import streamlit as st
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import pandas as pd
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import streamlit.components.v1 as components
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import textwrap as tw
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#st.set_page_config( initial_sidebar_state="expanded", margin_top = 20, margin_left = 20, margin_right = 10, margin_bottom=50, footer_text = "Creative Commons ... " )
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st.set_page_config(page_title='Portparser', layout="wide")
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##9bc2d1,#9bc2d1,#2f76a3
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page_bg_img = f"""
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<style>
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[data-testid="stAppViewContainer"] > .main {{
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background: linear-gradient(180deg, #88bbcf,#f1f1f1,#f1f1f1,#f1f1f1); /**#ccebff 10%, #f1f1f1 90% #0088be);
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padding-left:4rem;
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padding-right:4rem;
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background-image: url("img/nilc.png");
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background-repeat: repeat;
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background-position: center center;
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background-attachment: local;
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/**#f1f1f1/** #008fb3/**#accad2;/**#b3b3ff;**/
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/**background-image: url("https://i.postimg.cc/4xgNnkfX/Untitled-design.png");
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background-position: center center;
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background-repeat: no-repeat;
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background-attachment: local;**/
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}}
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[data-testid="stForm"] {{
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background-color: #9bc2d1;/**#7ebac9;/**#0086b3;**/
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}}
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.appview-container .main .block-container {{
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padding-top: 1rem;
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padding-bottom: 3rem;
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}}
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h1 {{
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color:#003d66;/**#143350**/;
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padding-left:1rem;
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padding-right:1rem;
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}}
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[class="css-1n543e5 e1ewe7hr5"] {{
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background-color: #ffffff /**#000066; /**#9bc2d1;/**#7ebac9;/**#0086b3;**/
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}}
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[class="css-1n543e5 e1ewe7hr5"]:hover {{
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background-color: #8080ff; /**#9bc2d1;/**#7ebac9;/**#0086b3;**/
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color:white;
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border: solid 1px #000066;
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}}
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a:link{{
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color:#0088be;
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}}
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a:hover {{
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color: #7733ff/**#8080ff**/;
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}}
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button{{
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padding-left:1rem;
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padding-right:1rem;
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border-radius: 15%;
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}}
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button:hover {{
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color:#7733ff;
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border:solid 1px #7733ff;
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}}
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</style>
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"""
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# head style
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head_css = """
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<style>
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[class="css-ocqkz7 esravye3"] {
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/**background-color: #9bc2d1;**/
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}
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[class="css-ocqkz7 esravye3"]{
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/**row1**/
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margin:0px 0px 0px 0px;
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padding:0;
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}
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.stApp {
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background-image: url("portparser_brasil1.jpg");
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background-repeat: repeat;
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background-position: center;
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}
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</style>
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"""
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#class="css-o7kwkx esravye0"]
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a = """
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<style>
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div.css-10r1649 esravye0 {
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background-color: red;
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}
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</style>
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"""
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custom_html = """
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<div class="banner" style="background-color:#0088be; color:white">
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<h1>PortParser</h1>
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<!--<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">-->
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</div>
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<style>
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.banner {
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width: 160%;
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height: 200px;
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overflow: hidden;
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}
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.banner img {
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width: 100%;
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object-fit: cover;
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}
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</style>
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"""
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#<div width="449" data-testid="stVerticalBlock" class="css-10r1649 esravye0">
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#components.html(custom_html)
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st.markdown(page_bg_img, unsafe_allow_html=True)
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st.markdown(head_css, unsafe_allow_html=True)
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row2 = st.columns([6,2,3])
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with row2[0]:
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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'>\
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A parsing model for Brazilian Portuguese</b></p>",unsafe_allow_html=True)
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st.write('This is Portparser, a parsing model for Brazilian Portuguese that follows the Universal Dependencies (UD) framework.\
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We built our model by using a recently released manually annotated corpus, the Porttinari-base, \
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and we explored different parsing methods and parameters for training. We also test multiple embedding models and parsing methods. \
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Portparse is the result of the best combination achieved in our experiments.')
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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 \
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freely available at https://github.com/LuceleneL/Portparser. More details about this work may also be found at \
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the POeTiSA project webpage at https://sites.google.com/icmc.usp.br/poetisa/.')
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with st.expander('How to cite?', expanded=False):
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st.code("""
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@inproceedings{lopes2024towards,
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title={Towards Portparser-a highly accurate parsing system for Brazilian Portuguese following the Universal Dependencies framework},
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author={Lopes, Lucelene and Pardo, Thiago},
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booktitle={Proceedings of the 16th International Conference on Computational Processing of Portuguese},
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pages={401--410},
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year={2024}
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}""")
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with row2[2]:
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st.image('img/wordcloud_brasil5.png')
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#wordcloud_vertical1.png
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#st.markdown('##### Write a sentence and run to parse:')
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#with st.sidebar:
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# st.header("About Portparser")
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# with st.expander('How was Portparser developed?'):
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# st.write('We build our model by using a recently released manually annotated corpus, the Porttinari-base, \
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# and explored different parsing methods and parameters for training. We also test multiple embedding models and parsing methods. \
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# Portparse is the result of the best combination achieved in our experiments.' )
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print('---------------------------')
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st.markdown("""
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<script language="JavaScript" type="text/javascript" src="arborator-draft.js"></script>
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<script language="JavaScript" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/d3/4.10.0/d3.js"></script>
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<script src="https://code.jquery.com/jquery-3.2.1.min.js" integrity="sha256-hwg4gsxgFZhOsEEamdOYGBf13FyQuiTwlAQgxVSNgt4=" crossorigin="anonymous"></script>
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<link rel="stylesheet" href="arborator-draft.css" type="text/css" />
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<script src="d3.js"></script>
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<script src="jquery-3.2.1.min.js"></script>
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<script>
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new ArboratorDraft();
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</script>"""
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,unsafe_allow_html=True)
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def make_conllu(path_text, path_input):
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try:
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os.system(f'python portTokenizer/portTok.py -o {path_input} -m -t -s S0000 {path_text}')
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return 'Converti o texto para conllu.'
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#st.text(open(path_input,'r',encoding='utf-8').read())
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except Exception as e:
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return str(e)
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def make_embedding(path_input, path_embedding, model_selected):
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try:
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os.system(f'python ./wembedding_service/compute_wembeddings.py {path_input} {path_embedding} --model {model_selected}')
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return 'Fiz as embeddings.'
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except Exception as e:
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return str(e)
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def make_predictions(path_input, path_prediction):
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try:
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os.system(f'python ./udpipe2/udpipe2.py Portparser_model --predict --predict_input {path_input} --predict_output {path_prediction}')
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return f'Fiz a predição.'# {path_input}, {path_prediction}'
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except Exception as e:
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return str(e)
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def get_predictions(path_prediction):
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try:
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with open(path_prediction, 'r') as f:
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st.text(f.read())
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except Exception as e:
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st.text('Resposta: '+e)
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st.write('Write a sentence and run to parse:')
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with st.form("parser"):
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text = st.text_input('Text: ')
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model = st.selectbox('Pick a model (Pick a embedding model:):', ['bert-base-portuguese-cased','bert-base-multilingual-uncased','robeczech-base','xlm-roberta-base'])
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model_selected = model+'-last4'
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submit = st.form_submit_button('Run')
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tab1, tab2, tab3, tab4 = st.tabs(["Running status" ,"Table", "Raw", "Tree"])
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if submit:
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import sys, os
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print(type(text))
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tab1.text('input: '+text)
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files = 'temp'
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input_text = 'text_input.txt'
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input_conllu = 'input.conllu' #'h2104_0_test.conllu'
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embedding_conllu = 'input.conllu.npz' #'h2104_0_test.conllu.npz'
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prediction_conllu = 'input_prediction.conllu'
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model = 'Portparser_model'
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path_text = os.path.join(files, input_text)
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path_input = os.path.join(files, input_conllu)
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path_prediction = os.path.join(files, prediction_conllu)
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path_embedding = os.path.join(files,embedding_conllu)
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with open(path_text,'w',encoding='utf-8') as f:
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f.write(text)
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import time
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with st.spinner('Transforming text into .conllu...'): #st.progress(0,text="Transformando texto para o formato .conllu"):
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time.sleep(3)
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tab1.text(make_conllu(path_text, path_input))
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with st.spinner('Processing embeddings...'): #st.progress(0,text="Processando embeddings"):
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time.sleep(6)
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tab1.text(make_embedding(path_input, path_embedding, model_selected))
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with st.spinner('Making predictions...'): #st.progress(0,text="Realizando a predição"):
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time.sleep(6)
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tab1.text(make_predictions(path_input, path_prediction))
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try:
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with open(path_prediction, 'r', encoding='utf-8') as f:
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content = f.read()
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tab3.text(content)
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#tab4.markdown(f'<conll>{content[4:]}</conll>',unsafe_allow_html=True)
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content = content.split('\n')
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#tab2.text(content[:4])
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table = pd.DataFrame([line.split('\t') for line in content[4:]])
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table.columns = ['ID','FORM','LEMMA','UPOS','XPOS','FEATS','HEAD','DEPREL','DEPS','MISC']
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tab2.dataframe(table, use_container_width=True)
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except Exception as e:
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st.text('Não deu certo a predição.'+str(e)+repr(e))
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row1 = st.columns([18,3,4,4])
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with row1[1]:
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st.image('img/nilc-removebg.png')
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with row1[2]:
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st.image('img/poetisa2.png')
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with row1[3]:
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st.image('img/icmc.png')
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st.markdown("""
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<script language="JavaScript" type="text/javascript" src="arborator-draft.js"></script>
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<script language="JavaScript" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/d3/4.10.0/d3.js"></script>
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<script src="https://code.jquery.com/jquery-3.2.1.min.js" integrity="sha256-hwg4gsxgFZhOsEEamdOYGBf13FyQuiTwlAQgxVSNgt4=" crossorigin="anonymous"></script>
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<link rel="stylesheet" href="arborator-draft.css" type="text/css" />
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<script src="d3.js"></script>
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<script src="jquery-3.2.1.min.js"></script>
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<script>
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new ArboratorDraft();
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</script>"""
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,unsafe_allow_html=True)
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