app.py

import streamlit as st
from transformers import pipeline
from tqdm import tqdm
import time


st.set_page_config(page_title='📜✂️ Chat Summarizer 📄🔍')
st.title('📜✂️ Chat Summarizer 📄🔍')

st.write("Check out the full notebook [here](https://www.kaggle.com/code/nicoladisabato/pegasus-fine-tuning-for-text-summarization)")


# Define available models and their corresponding names
models = {
    "nicoladisabato/pegasus-samsum": "nicoladisabato/pegasus-samsum"
}

selected_model = st.selectbox("Select a model", list(models.keys()))

predefined_text = """Alice: Hey, have you heard about the new movie coming out next week?
Bob: Yes, I saw the trailer. It looks really exciting.
Alice: I'm thinking of getting tickets for opening night. Are you interested?
Bob: I'd love to, but I might be busy with work that night.
Alice: Oh, that's a shame. It would have been fun to watch it together.
Bob: Definitely! Let me know how the movie is after you watch it.
Alice: Will do! I'm hoping it lives up to the hype.
Bob: Fingers crossed! Enjoy the movie if you go.
Alice: Thanks! I'll give you a spoiler-free review afterward.
Bob: Looking forward to it. Have a great time!
"""

text = st.text_area("Enter your chat", value=predefined_text, height=250)
button = st.button('Summarize')

if button:
    progress_text = "Loading the pipeline. It will take a minute :)"
    spinner = st.spinner(progress_text)
    with spinner:
        pipe = pipeline("summarization", model="nicoladisabato/pegasus-samsum")
    progress_text = "Summarization in progress."
    with st.spinner(progress_text):
        out = pipe(text)
        st.json(out)