Update app.py

app.py

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