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app (2).py

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+from transformers import pipeline
+import streamlit as st
+
+# Load pre-trained BART model for summarization
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+
+# Summarization function
+def summarize_text(text, max_length=150):
+    """
+    Summarizes the given text using the pre-trained BART model.
+    Args:
+    - text (str): The input text to be summarized.
+    - max_length (int): Maximum length of the summary.
+    
+    Returns:
+    - summary_text (str): The summarized text.
+    """
+    summary = summarizer(text, max_length=max_length, min_length=50, do_sample=False)
+    return summary[0]['summary_text']
+
+# Streamlit UI
+def run_streamlit_app():
+    """
+    This function runs the Streamlit app for text summarization.
+    """
+    st.title("Text Summarizer")
+    st.write("Enter your article and document below to get a summary.")
+
+    # Text input field for user
+    input_text = st.text_area("Enter the Text", height=220)
+
+    # Button to generate summary
+    if st.button("Summarize"):
+        if input_text.strip():
+            with st.spinner('Summarizing...'):
+                summary = summarize_text(input_text)
+                st.subheader("Summary:")
+                st.write(summary)
+        else:
+            st.warning("Please enter some text to summarize.")
+
+# If this script is being run locally or in an environment where Streamlit is supported, 
+# this block will start the Streamlit app
+if __name__ == "__main__":
+    run_streamlit_app()

requirements (2).txt

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+transformers==4.35.0
+torch==2.1.0
+streamlit==1.23.0