app.py

import streamlit as st
from transformers import pipeline
from langchain_core.output_parsers import StrOutputParser

# Initialize the text generation pipeline
model_name = "Qwen/Qwen2.5-0.5B-Instruct"
pipe = pipeline("text-generation", model=model_name, device=-1)
parser = StrOutputParser()

# Streamlit app
st.title("Optimized Batch Text Generation with Qwen Model")

# Text input from the user
user_input = st.text_area("Enter your messages (one per line):", "Who are you?\nWhat is your purpose?")

# Generate text when the button is clicked
if st.button("Generate"):
    # Split input into multiple messages
    user_messages = user_input.splitlines()
    messages = [message.strip() for message in user_messages if message.strip()]

    # Process messages in a batch
    outputs = pipe(messages, max_new_tokens=50)  # Adjust max_new_tokens as needed

    # Display the generated text for each input message
    st.write("Generated Responses:")
    for i, output in enumerate(outputs):
        generated_text = output['generated_text']
        result = parser.invoke(generated_text)
        st.write(f"Input {i+1}: {messages[i]}")
        st.write(f"Response {i+1}: {result}\n")