PyTorch utilized.

app.py

@@ -1,29 +1,32 @@
-import os
 import streamlit as st
-from transformers import BartTokenizer, TFBartForConditionalGeneration
+from transformers import BartTokenizer, BartForConditionalGeneration
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
-model_name = 'facebook-bart-large-cnn'
-tokenizer = BartTokenizer.from_pretrained(model_name)
-model = TFBartForConditionalGeneration.from_pretrained(model_name)
+@st.cache_resource
+def load_model():
+    model_name = "facebook/bart-large-cnn"
+    tokenizer = BartTokenizer.from_pretrained(model_name)
+    model = BartForConditionalGeneration.from_pretrained(model_name)
+    return tokenizer, model
 
-def summarize(text, style):
-    input_length = len(tokenizer.encode(text, return_tensors='tf', max_length=1024, truncation=True)[0])
+tokenizer, model = load_model()
 
-    if style == 'Normal':
+def summarize(text: str, style: str):
+    inputs = tokenizer.encode(text, return_tensors="pt", max_length=1024, truncation=True)
+    input_length = len(inputs[0])
+
+    if style == "Normal":
         max_length = int(input_length * 0.6)
         min_length = int(input_length * 0.5)
         length_penalty = 1.5
-    elif style == 'Precise':
+    elif style == "Precise":
         max_length = int(input_length * 0.45)
         min_length = int(input_length * 0.35)
         length_penalty = 1.2
-    else:
+    else:  # Accurate
         max_length = int(input_length * 0.4)
         min_length = int(input_length * 0.3)
         length_penalty = 1.0
 
-    inputs = tokenizer.encode(text, return_tensors='tf', max_length=1024, truncation=True)
     summary_ids = model.generate(
         inputs,
         max_length=max_length,
@@ -31,27 +34,26 @@ def summarize(text, style):
         length_penalty=length_penalty,
         num_beams=4,
         no_repeat_ngram_size=3,
-        early_stopping=True
+        early_stopping=True,
     )
-    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)
+    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
 
-    if not summary.endswith(('.', '!', '?')):
-        summary += '.'
+    if not summary.endswith((".", "!", "?")):
+        summary += "."
     return summary
 
-st.title('Text Summarizer')
-user_input = st.text_area("Enter text to summarize:", "")
-summary_style = st.selectbox(
-    'Choose summarization style:',
-    ('Normal', 'Precise', 'Accurate')
-)
-
-if st.button('Summarize'):
-    if user_input:
-        summary = summarize(user_input, summary_style)
-        st.write("Summary:")
+st.set_page_config(page_title="Text Summarizer", page_icon="📝", layout="centered")
+st.title("🧠 Text Summarizer")
+st.write("Summarize long text into concise and clear summaries using **BART**.")
+
+user_input = st.text_area("Enter text to summarize:", height=200)
+summary_style = st.selectbox("Choose summarization style:", ("Normal", "Precise", "Accurate"))
+
+if st.button("Summarize"):
+    if user_input.strip():
+        with st.spinner("Summarizing... ⏳"):
+            summary = summarize(user_input, summary_style)
+        st.subheader("Summary:")
         st.write(summary)
     else:
-        st.write("Please enter some text to summarize.")
-
-# End of program 2.0
+        st.warning("⚠️ Please enter some text to summarize.")