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import streamlit as st
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# Load the pre-trained model and tokenizer
model_name = "Vamsi/T5_Paraphrase_Paws" # Replace with your desired paraphrasing model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
# Function to perform paraphrasing
def paraphrase_text(input_text, max_length=200, num_beams=5):
"""
Paraphrases the input text using a Hugging Face T5 model.
Args:
input_text (str): The text to paraphrase.
max_length (int): Maximum length of the paraphrased text.
num_beams (int): Number of beams for beam search.
Returns:
str: The paraphrased text.
"""
# Add the paraphrasing prefix
input_text = f"paraphrase: {input_text}"
# Tokenize the input text
inputs = tokenizer.encode(input_text, return_tensors="pt", max_length=512, truncation=True)
# Generate paraphrased text
outputs = model.generate(
inputs,
max_length=max_length,
num_beams=num_beams,
early_stopping=True
)
# Decode and return the paraphrased text
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# Streamlit UI
st.title("Paraphrasing Tool")
st.write("Enter a paragraph below, and this tool will provide a human-like paraphrase.")
# Input text box
input_paragraph = st.text_area("Input Text:", placeholder="Type your text here...")
# Paraphrasing options
max_length = st.slider("Maximum Length of Paraphrased Text:", min_value=50, max_value=300, value=200)
num_beams = st.slider("Beam Search Width (Quality vs Speed):", min_value=1, max_value=10, value=5)
# Paraphrase button
if st.button("Paraphrase"):
if input_paragraph.strip(): # Check if input is not empty
with st.spinner("Paraphrasing in progress..."):
try:
paraphrased_text = paraphrase_text(input_paragraph, max_length=max_length, num_beams=num_beams)
st.subheader("Paraphrased Output:")
st.write(paraphrased_text)
except Exception as e:
st.error(f"An error occurred: {e}")
else:
st.warning("Please enter some text to paraphrase.")
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