Delete application.py

application.py

@@ -1,26 +0,0 @@
-import streamlit as st
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-
-# Set page title and header
-st.set_page_config(page_title="Text Summarizer", page_icon=":memo:")
-st.header("Text Summarizer using Arjun9/bart_samsum")
-
-# Load model and tokenizer
-model_name = "Arjun9/bart_samsum"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-
-# Create text input area
-input_text = st.text_area("Enter the text you want to summarize:", "")
-
-# Create a function to generate summary
-def generate_summary(text):
-    inputs = tokenizer.encode_plus(text, return_tensors="pt", max_length=512, truncation=True)
-    outputs = model.generate(inputs["input_ids"], num_beams=4, max_length=128, early_stopping=True)
-    summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return summary
-
-# Display summary if input text is provided
-if input_text:
-    summary = generate_summary(input_text)
-    st.write("**Summary:**", summary)