app.py

# import os
# os.system('pip install streamlit transformers torch')

# import streamlit as st
# from transformers import BartTokenizer, BartForConditionalGeneration

# # Load the model and tokenizer
# model_name = 'facebook/bart-large-cnn'

# tokenizer = BartTokenizer.from_pretrained(model_name)
# model = BartForConditionalGeneration.from_pretrained(model_name)

# def summarize_text(text):
#     inputs = tokenizer(text, return_tensors="pt", truncation=True, padding="longest")
#     summary_ids = model.generate(
#         inputs["input_ids"],
#         max_length=150,
#         min_length=30,
#         length_penalty=2.0,
#         num_beams=4,
#         early_stopping=True
#     )
#     summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
#     return summary

# st.title("Text Summarization with Fine-Tuned Model")
# st.write("Enter text to generate a summary using the fine-tuned summarization model.")

# text = st.text_area("Input Text", height=200)
# if st.button("Summarize"):
#     if text:
#         with st.spinner("Summarizing..."):
#             summary = summarize_text(text)
#             st.success("Summary Generated")
#             st.write(summary)
#     else:
#         st.warning("Please enter some text to summarize.")

# if __name__ == "__main__":
#     st.set_option('deprecation.showfileUploaderEncoding', False)
#     st.markdown(
#         """
#         <style>
#         .reportview-container {
#             flex-direction: row;
#             justify-content: center.
#         }
#         </style>
#         """,
#         unsafe_allow_html=True
#     )
import os
os.system('pip install streamlit transformers torch')

import streamlit as st
from transformers import pipeline

# Load the summarization pipeline
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")

def summarize_text(text):
    summary = summarizer(text, max_length=150, min_length=30, length_penalty=2.0, num_beams=4, early_stopping=True)
    return summary[0]['summary_text']

st.title("Text Summarization with Fine-Tuned Model")
st.write("Enter text to generate a summary using the fine-tuned summarization model.")

text = st.text_area("Input Text", height=200)
if st.button("Summarize"):
    if text:
        with st.spinner("Summarizing..."):
            summary = summarize_text(text)
            st.success("Summary Generated")
            st.write(summary)
    else:
        st.warning("Please enter some text to summarize.")

if __name__ == "__main__":
    st.set_option('deprecation.showfileUploaderEncoding', False)
    st.markdown(
        """
        <style>
        .reportview-container {
            flex-direction: row;
            justify-content: center.
        }
        </style>
        """,
        unsafe_allow_html=True
    )