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import streamlit as st
from transformers import pipeline
from huggingface_hub import login
from dotenv import load_dotenv
import os
# Load the environment variables from the .env file
load_dotenv()
# Retrieve the token from the .env file
huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
# Log in using the retrieved token
login(token=huggingface_token)
# Available models for summarization
models = {
"T5": "Sandaruth/T5_Full_Fine_Tuned_FINDSUM",
"BERT": "bert-base-uncased", # Note: BERT isn't designed for summarization; you can change this
"LongT5": "google/long-t5-local-base",
"Pegasus": "google/pegasus-xsum"
}
# Streamlit app layout
st.title("Summarization with Multiple Models")
# Dropdown to select the model
model_choice = st.selectbox("Select a model for summarization", models.keys())
# Text area for input
input_text = st.text_area("Enter the long text you want to summarize", height=300)
# Button to generate the summary
if st.button("Generate Summary"):
# Show a spinner while generating the summary
with st.spinner("Generating summary, please wait..."):
# Load the selected model and summarizer pipeline
summarizer = pipeline("summarization", model=models[model_choice])
# Log the model choice
st.write(f"Using model: **{model_choice}** for summarization.")
if input_text:
# Generate the summary
summary = summarizer(input_text, max_length=350, min_length=30, do_sample=False)
# Log the success message
st.success("Summary generated successfully!")
# Display the summary
st.subheader("Generated Summary")
st.write(summary[0]['summary_text'])
else:
st.warning("Please enter text to summarize!")
# Optionally, you can add a footer or additional instructions
st.markdown("---")
st.write("Provide a long text and select a model to see the summarization in action!")
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