app.py

import streamlit as st
from transformers import pipeline

# Load the language model pipeline
# You can replace "gpt2" with another model or a locally fine-tuned model as desired
@st.cache_resource
def load_model():
    return pipeline("text-generation", model="gpt2")

llm = load_model()

# Set up Streamlit columns for layout
col1, col2 = st.columns(2)

with col1:
    # User input box for text input
    user_input = st.text_input("Enter your text:", "")
    
    # Static backend text to combine with user input
    backend_text = "Predefined text: "
    combined_text = backend_text + user_input

    # Button to trigger LLM generation
    if st.button("Generate"):
        if user_input.strip():  # Ensure input is not empty
            with st.spinner("Generating response..."):
                # Generate response from the LLM with some constraints
                response = llm(combined_text, max_length=100, num_return_sequences=1)
                # Extract generated text from LLM output
                output_text = response[0]['generated_text']
        else:
            output_text = "Please provide some input text."

with col2:
    # Display the output in a text area
    st.text_area("Output:", output_text, height=200, key="output_text")

    # Copy button (uses Streamlit Components to trigger copying)
    copy_script = """
    <script>
    function copyToClipboard(text) {
        navigator.clipboard.writeText(text).then(
            function() { console.log('Copying to clipboard succeeded'); },
            function(err) { console.error('Could not copy text: ', err); }
        );
    }
    </script>
    """
    # Add the script to the page
    st.markdown(copy_script, unsafe_allow_html=True)

    # Button to copy the output text
    if st.button("Copy Output"):
        # Display the output text in a way accessible to JS
        st.write(f'<button onclick="copyToClipboard(document.getElementById(\'output_text\').value)">Copy</button>', unsafe_allow_html=True)