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streamlit_sed.py
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streamlit_sed.py
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import streamlit as st
from experimental.sed.text.utils.pipeline import Inference
#inference tool
infer = Inference('language model')
# this is the main function in which we define our webpage
def main():
# giving the webpage a title
st.title("Experimental MaryNLP Tools")
# here we define some of the front end elements of the web page like
# the font and background color, the padding and the text to be displayed
html_temp = """
<div style ="padding:13px">
<h1 style ="color:green;text-align:center;">Mary Text Tools</h1>
</div>
"""
# this line allows us to display the front end aspects we have
# defined in the above code
st.markdown(html_temp, unsafe_allow_html = True)
# the following lines create text boxes in which the user can enter
# the data required to make the prediction
tokenization_text = st.text_input("Text to be tokenized", "maria anawezekana kuwa mtu mwenye Kiswahili kizuri kuliko wote")
result =""
if st.button("Tokenize"):
result = infer.tokenize(tokenization_text)
st.success(result)
example_word = st.text_input("Example Word", "atacheza")
example_analogy = st.text_input("Example Analogy", "alicheza")
word = st.text_input("Test Word", "ataenda")
result =""
if st.button("Get Analogy"):
result = infer.word_analogy(example_word, example_analogy, word)
st.success(result)
context = st.text_input("Context", "hapana siwezi kwenda nipe mda kabla ya")
# result =""
# if st.button("Predict Next Word"):
next_word = infer.get_preds(context)
st.success(next_word)
# the below line ensures that when the button called 'Predict' is clicked,
# the prediction function defined above is called to make the prediction
# and store it in the variable result
if __name__=='__main__':
main()