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import streamlit as st
from transformers import AutoTokenizer, pipeline
import transformers
import torch
import pandas as pd
# Model setup
model = "newsmediabias/UnBIAS-LLama2-Debiaser-Chat-QLoRA"
tokenizer = AutoTokenizer.from_pretrained(model)
debias_pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
# Sample Instruction
instruction = ("Instruction: As a helpful, respectful and trustworthy debiasing assistant, your "
"task is to receive a text and return its unbiased version, without adding any unrelated content "
"or additional outputs.")
def get_debiased_sequence(prompt):
"""Generate a debiased version of the provided text using the debiasing pipeline."""
input_text = f"<s> <<SYS>> {instruction} <</SYS>> [INST]{prompt} [/INST]"
sequences = debias_pipeline(
input_text,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
max_length=len(prompt)+100,
)
res = sequences[0]['generated_text']
result_part = res.split('[/INST]')[-1]
clean_result = ''.join(c for c in result_part if c.isprintable())
return clean_result.strip()
# Streamlit interface
st.title('Text Debiasing App')
input_text = st.text_area("Enter text to debias:", height=150)
if st.button("Debias Text"):
if input_text:
debiased_text = get_debiased_sequence(input_text)
st.write("Debiased Text:", debiased_text)
else:
st.write("Please enter some text to debias.")
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