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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)
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