Update app.py

app.py

@@ -1,7 +1,6 @@
 #%%writefile debias_app.py
 import streamlit as st
 from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoModelForTokenClassification, pipeline
-import pandas as pd
 
 # Define the BiasPipeline class with text processing methods
 class BiasPipeline:
@@ -35,13 +34,7 @@ pipeline = BiasPipeline()
 
 # Streamlit interface
 st.title('UnBIAS App')
-# List of preloaded example sentences
-example_sentences = [
-    "Women are just too emotional to be leaders.",
-    # More examples...
-]
-
-# Dropdown for selecting an example sentence or entering your own
+example_sentences = ["Women are just too emotional to be leaders.", "All young people are lazy and addicted to their phones."]
 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)
 
@@ -52,16 +45,10 @@ if st.button("Process Text"):
         label = classification_results[0]['label']
         score = classification_results[0]['score']
         st.write(f"**Classification:** {label} (Confidence: {score:.2f})")
-
-        # Extract biased words from NER results
         biased_words = [result['word'] for result in ner_results if result['entity'].startswith('B-BIAS')]
-        st.write("**Biased Words Identified:**")
-        st.write(", ".join(biased_words))
+        st.write("**Biased Words Identified:**", ", ".join(biased_words))
     else:
         st.write("Please enter some text to process.")
 
 # Disclaimer
-st.info("Disclaimer: Please note that while this tool aims to identify and highlight biased language, no automated system is perfect. \\
-The detection of bias depends on various factors, including the context, the training data used for the models, \\
-and the inherent limitations of natural language processing technologies. As such, some biases may not be detected, \\
-and all results should be reviewed critically by human users.")
+st.info("Disclaimer: Please note that while this tool aims to identify and highlight biased language, no automated system is perfect. The detection of bias depends on various factors, including the context, the training data used for the models, and the inherent limitations of natural language processing technologies. As such, some biases may not be detected, and all results should be reviewed critically by human users.")