Create app.py

app.py

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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.")
+