shainaraza
commited on
Commit
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380b45b
1
Parent(s):
58098ff
Update app.py
Browse files
app.py
CHANGED
@@ -1,3 +1,4 @@
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import streamlit as st
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoModelForTokenClassification, pipeline
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@@ -45,7 +46,32 @@ pipeline = BiasPipeline()
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# Streamlit interface
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st.title('UnBIAS App')
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if st.button("Process Text"):
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if input_text:
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cleaned_text = pipeline.clean_text(input_text)
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%%writefile debias_app.py
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import streamlit as st
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoModelForTokenClassification, pipeline
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# Streamlit interface
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st.title('UnBIAS App')
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# List of preloaded example sentences
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example_sentences = [
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"Women are just too emotional to be leaders.",
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"All young people are lazy and addicted to their phones.",
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"People from that country are always dishonest and corrupt.",
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"Men are inherently better at science and math than women.",
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"The elderly are just burdens to society; they contribute nothing.",
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"All poor people are lazy and don't want to work.",
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"People who follow that religion are all terrorists.",
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"Immigrants are taking all our jobs and ruining the country.",
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"All wealthy people achieved success only through manipulation and deceit.",
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"People with tattoos are not professional.",
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"All politicians are liars and cannot be trusted.",
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"Blue-collar workers don't have any real skills.",
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"Anyone who doesn't attend college is unintelligent.",
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"Only people from the city are sophisticated.",
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"Rural folks have backward mindsets and don't understand progress.",
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"All artists are always broke and unreliable.",
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"Anyone from that region is uneducated and narrow-minded.",
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"People without jobs are simply not trying hard enough."
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]
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# Dropdown for selecting an example sentence or entering your own
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selected_sentence = st.selectbox("Choose an example or type your own below:", [""] + example_sentences)
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input_text = st.text_area("Enter text:", selected_sentence, height=150)
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if st.button("Process Text"):
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if input_text:
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cleaned_text = pipeline.clean_text(input_text)
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