Update app.py

app.py

@@ -1,18 +1,22 @@
-# Load model directly
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
 
+# Load model and tokenizer
 tokenizer = AutoTokenizer.from_pretrained("Chillyblast/Bart_Summarization")
 model = AutoModelForSeq2SeqLM.from_pretrained("Chillyblast/Bart_Summarization")
 
-from transformers import pipeline
-
 # Create a pipeline for text summarization
 summarizer = pipeline("summarization", model=model, tokenizer=tokenizer)
 
-# Example input for inference
-dialogue = input(str("Enter the input:"))
-# Perform inference
-summary = summarizer(dialogue, max_length=500, min_length=300, do_sample=False)
+# Streamlit app
+st.title("Text Summarization App")
+
+# Create a text input box for user input
+dialogue = st.text_area("Enter the input:")
 
-# Print the summary
-print("Summary:", summary[0]['summary_text'])
+if dialogue:
+    # Perform inference
+    summary = summarizer(dialogue, max_length=500, min_length=300, do_sample=False)
+    
+    # Display the summary
+    st.write("Summary:", summary[0]['summary_text'])