app.py

import streamlit as st
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Load tokenizer and model
model_identifier = "songhieng/khmer-mt5-summarization"
tokenizer = AutoTokenizer.from_pretrained(model_identifier, use_fast=False)
model = AutoModelForSeq2SeqLM.from_pretrained(model_identifier, use_fast=False)

# Set page configuration
st.set_page_config(page_title="Khmer Text Summarization", layout="wide")

# App title and description
st.title("Khmer Text Summarization")
st.write("Enter Khmer text below to generate a concise summary.")

# Text input
user_input = st.text_area("Input Text:", height=300)

# Summarization parameters
st.sidebar.header("Summarization Settings")
max_length = st.sidebar.slider("Maximum Summary Length", min_value=50, max_value=300, value=150, step=10)
min_length = st.sidebar.slider("Minimum Summary Length", min_value=10, max_value=100, value=30, step=5)
num_beams = st.sidebar.slider("Number of Beams", min_value=1, max_value=10, value=4, step=1)

# Summarize button
if st.button("Summarize"):
    if user_input.strip():
        try:
            # Tokenize input
            inputs = tokenizer.encode(user_input, return_tensors="pt", truncation=True)

            # Generate summary
            summary_ids = model.generate(
                inputs,
                max_length=max_length,
                min_length=min_length,
                num_beams=num_beams,
                length_penalty=2.0,
                early_stopping=True
            )
            summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)

            # Display summary
            st.subheader("Summary:")
            st.write(summary)
        except Exception as e:
            st.error(f"An error occurred during summarization: {e}")
    else:
        st.warning("Please enter some text to summarize.")