app.py

import streamlit as st
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch

def generate_summary(model, tokenizer, dialogue):
    # Tokenize input dialogue
    inputs = tokenizer(dialogue, return_tensors="pt", max_length=1024, truncation=True)

    # Generate summary
    with torch.no_grad():
        summary_ids = model.generate(inputs["input_ids"], max_length=150, length_penalty=0.8, num_beams=4)

    # Decode and return the summary
    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)
    return summary

# Set page title and favicon
st.set_page_config(
    page_title="Dialog Summarizer App",
    page_icon=":memo:",  # You can set your own emoji or use an image URL
)

# Add a logo at the top middle of the app
logo_path = "ale.png"
logo_html = f'<div style="text-align:center;"><img src="{logo_path}" width="200"></div>'
st.markdown(logo_html, unsafe_allow_html=True)

# Display the app name below the logo
st.title("Dialog Summarizer App")

st.info("🖥️ Note: This application is running on CPU. Please be patient ⏳.")
st.markdown("This app summarizes dialogues. Enter a dialogue in the text area, and the generated summary will appear at the bottom. Keep the dialogues few sentences long for optimal results.")


# Create two columns layout using st.columns
col1, col2 = st.columns(2)

# User input on the left side with increased height
user_input = col1.text_area("Enter the dialog:", height=300)

# Add "Summarize" and "Clear" buttons
summarize_button = col1.button("Summarize")
clear_button = col1.button("Clear")

# If "Clear" button is clicked, clear the user input
if clear_button:
    user_input = ""

# If "Summarize" button is clicked and there is user input, generate and display summary on the right side
if summarize_button and user_input:
    # Load pre-trained Pegasus model and tokenizer
    model_name = "ale-dp/pegasus-finetuned-dialog-summarizer"
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

    # Generate summary
    summary = generate_summary(model, tokenizer, user_input)

    # Display the generated summary on the right side
    col2.subheader("Generated Summary:")
    col2.write(summary)