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import streamlit as st
from PIL import Image
from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer
from streamlit_extras.app_logo import add_logo
def logo():
add_logo("vocali_logo.jpg", height=300)
def get_result_text_es_pt (list_entity, text, lang):
result_words = []
if lang == "es":
punc_tags = ['¿', '?', '¡', '!', ',', '.', ':']
else:
punc_tags = ['?', '!', ',', '.', ':']
for entity in 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 punc_in != "":
word = result_words[-1].replace(punc_in, "") + text[start:end]
else:
word = result_words[-1] + 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 = ['?', '!', ',', '.', ':']
for entity in 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 punc_in != "":
word = result_words[-1].replace(punc_in, "") + text[start:end]
else:
word = result_words[-1] + text[start:end]
else:
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)
if __name__ == "__main__":
logo()
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 = ModelForTokenClassification.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_ca, tokenizer=tokenizer_ca)
input_text = st.selectbox(
label = "Choose an language",
options = ["Spanish", "Portuguese", "Catalan"]
)
st.subheader("Enter the text to be analyzed.")
text = st.text_input('Enter text') #text is stored in this variable
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)
out = get_prediction(text, input_text)
st.markdown(out, unsafe_allow_html=True)
text = ""
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