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Update app.py
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app.py
CHANGED
@@ -9,7 +9,7 @@ HISTORY_WEIGHT = 100 # set history weight (if found any keyword from history, it
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@st.cache(allow_output_mutation=True)
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def get_model(model):
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return pipeline("fill-mask", model=model, top_k=
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def main(nlp, semantic_model):
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data_load_state = st.text('Inference to model...')
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@@ -40,8 +40,13 @@ def main(nlp, semantic_model):
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if __name__ == '__main__':
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if st._is_running_with_streamlit:
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st.
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text = st.text_input("Enter a text for auto completion...", value='Where is Bill')
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@st.cache(allow_output_mutation=True)
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def get_model(model):
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return pipeline("fill-mask", model=model, top_k=20)#set the maximum of tokens to be retrieved after each inference to model
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def main(nlp, semantic_model):
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data_load_state = st.text('Inference to model...')
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if __name__ == '__main__':
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if st._is_running_with_streamlit:
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st.markdown("""
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# Introduction
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This is an example of an auto-complete approach where the next token suggested based on users's history Keyword match & Semantic similarity of users's history (log).
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The next token is predicted per probability and a weight if it is appeared in keyword user's history or there is a similarity to semantic user's history
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""")
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history_keyword_text = st.text_input("Enter users's history <keywords match> (optional, i.e., 'Gates')", value="")
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text = st.text_input("Enter a text for auto completion...", value='Where is Bill')
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