import streamlit as st

from transformers import T5Tokenizer, TFAutoModelForSeq2SeqLM, pipeline

# Define the path to the saved model
model_path = '/T5_samsum-20240723T171755Z-001.zip'

# Load the tokenizer and model
tokenizer = T5Tokenizer.from_pretrained(model_path)
model = TFAutoModelForSeq2SeqLM.from_pretrained(model_path)

summarizer = pipeline("summarization", model=model, tokenizer=tokenizer)

# Set the title for the Streamlit app
st.title("T5 Summary Generator")

# Text input for the user
text = st.text_area("Enter your text: ")

def generate_summary(input_text):
    # Perform summarization
    summary = summarizer(input_text, max_length=200, min_length=40, do_sample=False)
    return summary[0]['summary_text']

if st.button("Generate"):
    if text:
        generated_summary = generate_summary(text)
        # Display the generated summary
        st.subheader("Generated Summary")
        st.write(generated_summary)
    else:
        st.warning("Please enter some text to generate a summary.")