Update app.py

app.py

@@ -1,19 +1,21 @@
+import streamlit as st
 from transformers import pipeline
 
+# Replace with your actual model URL
+model_url = "your_hf_username/your_model_name"
+
 # Load the model and tokenizer from Hugging Face
-summarizer = pipeline("summarization", model="ibrahimgiki/facebook_bart_base")
+summarizer = pipeline("summarization", model=model_url)
 
-# Define a function to summarize text
-def summarize_text(text, max_length=130, min_length=30, do_sample=False):
-    summary = summarizer(text, max_length=max_length, min_length=min_length, do_sample=do_sample)
-    return summary[0]['summary_text']
+# Streamlit application
+st.title("Text Summarization with BART")
 
-# Example usage
-text = """
-Your text here. This text should be a long paragraph that you want to summarize. 
-The pipeline will take this text and generate a shorter version of it.
-"""
+# Text input from the user
+user_input = st.text_area("Enter the text to summarize:")
 
-summary = summarize_text(text)
-print("Original Text:", text)
-print("Summary:", summary)
+# If user input is provided, summarize the text
+if user_input:
+    with st.spinner("Summarizing..."):
+        summary = summarizer(user_input, max_length=130, min_length=30, do_sample=False)
+        summarized_text = summary[0]['summary_text']
+        st.text_area("Summary:", summarized_text, height=200)