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Summarization_Using_BART

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Rename aap.py to app.py

3bd8774 verified about 2 years ago

1.78 kB

import streamlit as st
import torch
from transformers import pipeline
from transformers import BartTokenizer, BartForConditionalGeneration

# Replace with your Hugging Face model repository path
model_repo_path = 'ASaboor/Bart_samsum'

# Load the model and tokenizer
model = BartForConditionalGeneration.from_pretrained(model_repo_path)
tokenizer = BartTokenizer.from_pretrained(model_repo_path)

# Initialize the summarization pipeline
summarizer = pipeline('summarization', model=model, tokenizer=tokenizer)

# Streamlit app layout
st.set_page_config(page_title="Text Summarization App", page_icon=":memo:", layout="wide")

st.title("Text Summarization App")
st.write("""
    This app uses a fine-tuned BART model to generate summaries of your input text.
    Enter the text you want to summarize in the box below and click "Summarize" to see the result.
""")

# User input
text_input = st.text_area("Enter text to summarize", height=300, placeholder="Paste your text here...")

# Summarize the text
if st.button("Summarize"):
    if text_input:
        with st.spinner("Generating summary..."):
            try:
                # Generate summary
                summary = summarizer(text_input, max_length=150, min_length=30, do_sample=False)
                
                # Display summary
                st.subheader("Summary")
                st.write(summary[0]['summary_text'])
            except Exception as e:
                st.error(f"An error occurred during summarization: {e}")
    else:
        st.warning("Please enter some text to summarize.")

# Optional: Add a footer or additional information
st.markdown("""
    ---
    Made with ❤️ using [Streamlit](https://streamlit.io) and [Hugging Face Transformers](https://huggingface.co/transformers/).
""")

	import streamlit as st
	import torch
	from transformers import pipeline
	from transformers import BartTokenizer, BartForConditionalGeneration

	# Replace with your Hugging Face model repository path
	model_repo_path = 'ASaboor/Bart_samsum'

	# Load the model and tokenizer
	model = BartForConditionalGeneration.from_pretrained(model_repo_path)
	tokenizer = BartTokenizer.from_pretrained(model_repo_path)

	# Initialize the summarization pipeline
	summarizer = pipeline('summarization', model=model, tokenizer=tokenizer)

	# Streamlit app layout
	st.set_page_config(page_title="Text Summarization App", page_icon=":memo:", layout="wide")

	st.title("Text Summarization App")
	st.write("""
	This app uses a fine-tuned BART model to generate summaries of your input text.
	Enter the text you want to summarize in the box below and click "Summarize" to see the result.
	""")

	# User input
	text_input = st.text_area("Enter text to summarize", height=300, placeholder="Paste your text here...")

	# Summarize the text
	if st.button("Summarize"):
	if text_input:
	with st.spinner("Generating summary..."):
	try:
	# Generate summary
	summary = summarizer(text_input, max_length=150, min_length=30, do_sample=False)

	# Display summary
	st.subheader("Summary")
	st.write(summary[0]['summary_text'])
	except Exception as e:
	st.error(f"An error occurred during summarization: {e}")
	else:
	st.warning("Please enter some text to summarize.")

	# Optional: Add a footer or additional information
	st.markdown("""
	---
	Made with ❤️ using [Streamlit](https://streamlit.io) and [Hugging Face Transformers](https://huggingface.co/transformers/).
	""")