app.py

import streamlit as st
from PIL import Image
from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer
from htbuilder import HtmlElement, div, ul, li, br, hr, a, p, img, styles, classes, fonts
from htbuilder.units import percent, px
from htbuilder.funcs import rgba, rgb
from pathlib import Path

def clear_text():
    st.session_state.text = st.session_state.widget
    st.session_state.widget = ""

    
def get_result_text_es_pt (list_entity, text, lang):
    result_words = []
    tmp_word = ""
    if lang == "es":
        punc_tags = ['¿', '?', '¡', '!', ',', '.', ':']
    else:
        punc_tags = ['?', '!', ',', '.', ':']
    
    for idx, entity in enumerate(list_entity): 
        tag = entity["entity"]
        word = entity["word"]
        start = entity["start"]
        end = entity["end"]
        
        # check punctuation
        punc_in = next((p for p in punc_tags if p in tag), "")
                
        subword = False
        # check subwords
        if word[0] == "#": 
            subword = True
            if tmp_word == "":
                p_s = list_entity[idx-1]["start"]
                p_e = list_entity[idx-1]["end"]
                tmp_word = text[p_s:p_e] + text[start:end]
            else: 
                tmp_word = tmp_word + text[start:end]
            word = tmp_word
        else:
            tmp_word = ""
            word = text[start:end]
            
        if tag == "l": 
            word = word 
        elif tag == "u":
            word = word.capitalize()
        # case with punctuation
        else:
            if tag[-1] == "l":
                word = (punc_in + word) if punc_in in ["¿", "¡"] else (word + punc_in)
            elif tag[-1] == "u":
                word = (punc_in + word.capitalize()) if punc_in in ["¿", "¡"] else (word.capitalize() + punc_in)     
        
        if tag != "l":
            word = '<span style="font-weight:bold; color:rgb(142, 208, 129);">' + word + '</span>'
		
        if subword == True: 
            result_words[-1] = word
        else:
            result_words.append(word)

    return " ".join(result_words)
            


def get_result_text_ca (list_entity, text):
    result_words = []
    punc_tags = ['?', '!', ',', '.', ':']
    tmp_word = ""
    for idx, entity in enumerate(list_entity): 
        start = entity["start"]
        end = entity["end"]
        tag = entity["entity"]
        word = entity["word"]
        
        # check punctuation
        punc_in = next((p for p in punc_tags if p in tag), "")
                
        subword = False
        # check subwords
        if word[0] != "Ġ": 
            subword = True
            if tmp_word == "":
                p_s = list_entity[idx-1]["start"]
                p_e = list_entity[idx-1]["end"]
                tmp_word = text[p_s:p_e] + text[start:end]
            else: 
                tmp_word = tmp_word + text[start:end]
            word = tmp_word
        else:
            tmp_word = ""
            word = text[start:end]
        
        if tag == "l": 
            word = word 
        elif tag == "u":
            word = word.capitalize()
        # case with punctuation
        else:
            if tag[-1] == "l":
                word = (punc_in + word) if punc_in in ["¿", "¡"] else (word + punc_in)
            elif tag[-1] == "u":
                word = (punc_in + word.capitalize()) if punc_in in ["¿", "¡"] else (word.capitalize() + punc_in)     
        
        if tag != "l":
            word = '<span style="font-weight:bold; color:rgb(142, 208, 129);">' + word + '</span>'
			
        if subword == True: 
            result_words[-1] = word
        else:
            result_words.append(word)

    return " ".join(result_words)


def image(src_as_string, **style):
    return img(src=src_as_string, style=styles(**style))


def link(link, text, **style):
    return a(_href=link, _target="_blank", style=styles(**style))(text)

def layout(*args):

    style = """
    <style>
      # MainMenu {visibility: hidden;}
      footer {visibility: hidden;}
     .stApp { bottom: 105px; }
    </style>
    """

    style_div = styles(
        position="fixed",
        left=0,
        bottom=0,
        margin=px(0, 0, 0, 0),
        width=percent(100),
        color="black",
        text_align="center",
        height="auto",
        opacity=1
    )

    style_hr = styles(
        display="block",
        margin=px(8, 8, "auto", "auto"),
        border_style="inset",
        border_width=px(2)
    )

    body = p()
    foot = div(
        style=style_div
    )(
        hr(
            style=style_hr
        ),
        body
    )

    st.markdown(style, unsafe_allow_html=True)

    for arg in args:
        if isinstance(arg, str):
            body(arg)

        elif isinstance(arg, HtmlElement):
            body(arg)

    st.markdown(str(foot), unsafe_allow_html=True)


def footer():
    logo_path = Path(__file__).with_name("vocali_logo.jpg").parent.absolute() + "/vocali_logo.jpg"
    funding_path = Path(__file__).with_name("logo_funding.png").parent.absolute() + "/logo_funding.png"
    
    myargs = [
        "Made in ",
        image(str(logo_path), width=px(50), height=px(50)),
        link("https://vocali.net/", "VÓCALI"),
        " with funding ",
        image(str(funding_path), height=px(50), width=px(200)),
        br(),
        "This work was funded by the Spanish Government, the Spanish Ministry of Economy and Digital Transformation through the Digital Transformation through the 'Recovery, Transformation and Resilience Plan' and also funded by the European Union NextGenerationEU/PRTR through the research project 2021/C005/0015007",
    ]
    layout(*myargs)

    
if __name__ == "__main__":

    if "text" not in st.session_state:
        st.session_state.text = ""
    
    st.title('Sanivert Punctuation And Capitalization Restoration')
    model_es = AutoModelForTokenClassification.from_pretrained("VOCALINLP/spanish_capitalization_punctuation_restoration_sanivert")
    tokenizer_es = AutoTokenizer.from_pretrained("VOCALINLP/spanish_capitalization_punctuation_restoration_sanivert")
    pipe_es = pipeline("token-classification", model=model_es, tokenizer=tokenizer_es)
	
    model_ca = AutoModelForTokenClassification.from_pretrained("VOCALINLP/catalan_capitalization_punctuation_restoration_sanivert")
    tokenizer_ca = AutoTokenizer.from_pretrained("VOCALINLP/catalan_capitalization_punctuation_restoration_sanivert")
    pipe_ca = pipeline("token-classification", model=model_ca, tokenizer=tokenizer_ca)
	
    model_pt = AutoModelForTokenClassification.from_pretrained("VOCALINLP/portuguese_capitalization_punctuation_restoration_sanivert")
    tokenizer_pt = AutoTokenizer.from_pretrained("VOCALINLP/portuguese_capitalization_punctuation_restoration_sanivert")
    pipe_pt = pipeline("token-classification", model=model_pt, tokenizer=tokenizer_pt)

    
    input_text = st.selectbox(
      label = "Choose an language",
      options = ["Spanish", "Portuguese", "Catalan"]
	)

    st.subheader("Enter the text to be analyzed.")
    st.text_input('Enter text', key='widget', on_change=clear_text) #text is stored in this variable 
    text = st.session_state.text
    print(text)
    if input_text == "Spanish": 
        result_pipe = pipe_es(text)
        out = get_result_text_es_pt(result_pipe, text, "es")
    elif input_text == "Portuguese": 
        result_pipe = pipe_pt(text)
        out = get_result_text_es_pt(result_pipe, text, "pt")
    elif input_text == "Catalan": 
        result_pipe = pipe_ca(text)
        out = get_result_text_ca(result_pipe, text)

    st.markdown(out, unsafe_allow_html=True)
    footer()