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import streamlit as st
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch
import sys
st.title("Dialog Summarizer App")
# User input
user_input = st.text_area("Enter the dialog:")
# Add "Summarize" and "Clear" buttons
summarize_button = st.button("Summarize")
clear_button = st.button("Clear")
# If "Clear" button is clicked, clear the user input
if clear_button:
user_input = ""
# "Summarize" button and user input, generate and display summary
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
st.subheader("Generated Summary:")
st.write(summary)
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
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