app.py

import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the merged model
model_name = "EmTpro01/gemma-paraphraser-4bit"  # Replace with your merged model path
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)  # Default device is CPU

# Streamlit UI
st.title("Text Paraphrasing ")
st.write("Provide a paragraph, and this AI will paraphrase it for you.")

# Input paragraph
paragraph = st.text_area("Enter a paragraph to paraphrase:", height=200)

if st.button("Paraphrase"):
    if paragraph.strip():
        with st.spinner("Paraphrasing..."):
            # Prepare the prompt
            alpaca_prompt = f"Below is a paragraph, paraphrase it.\n### paragraph: {paragraph}\n### paraphrased:"
            
            # Tokenize input and move to CPU
            inputs = tokenizer(alpaca_prompt, return_tensors="pt")
            
            # Generate paraphrased text
            output = model.generate(**inputs, max_new_tokens=200)
            paraphrased = tokenizer.decode(output[0], skip_special_tokens=True)
            
            # Extract the paraphrased portion
            result = paraphrased.split("### paraphrased:")[1].strip()
            st.text_area("Paraphrased Output:", result, height=200)
    else:
        st.warning("Please enter a paragraph to paraphrase.")