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import streamlit as st
from transformers import pipeline
# Function to initialize pipelines with caching
@st.cache_resource
def load_summarizer():
return pipeline("summarization", model="Falconsai/text_summarization")
@st.cache_resource
def load_translator():
return pipeline("translation", model="Helsinki-NLP/opus-mt-en-ur")
def summarize_text(input_text, summarizer):
summary = summarizer(input_text, max_length=700, min_length=100, do_sample=False)
return summary[0]['summary_text']
def translate_urdu(english_summary, translator):
urdu_text = translator(english_summary)
return urdu_text[0]['translation_text']
def main():
st.title("Text Summarization and Translation")
input_text = st.text_area("Enter text to summarize:", "")
if 'english_summary' not in st.session_state:
st.session_state.english_summary = ""
if 'urdu_translation' not in st.session_state:
st.session_state.urdu_translation = ""
summarizer = load_summarizer()
translator = load_translator()
if st.button("Summarize"):
if input_text:
english_summary = summarize_text(input_text, summarizer)
st.session_state.english_summary = english_summary
st.header("English Summary:",divider='gray')
st.write(english_summary)
if st.session_state.english_summary:
if st.button("Translate English to Urdu"):
urdu_translation = translate_urdu(st.session_state.english_summary, translator)
st.session_state.urdu_translation = urdu_translation
st.write("Urdu Translation:")
st.write(urdu_translation)
st.header("English Summary Text:",divider='gray')
st.write(st.session_state.english_summary)
if __name__ == "__main__":
main()
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