Changes made to tf-keras

app.py

@@ -1,30 +1,20 @@
 import streamlit as st
 from transformers import BartTokenizer, TFBartForConditionalGeneration
-import tensorflow as tf
+model_name = 'facebook/bart-large-cnn'
+tokenizer = BartTokenizer.from_pretrained(model_name)
+model = TFBartForConditionalGeneration.from_pretrained(model_name)
 
-model_path = 'facebook/bart-large-cnn'
-tokenizer_path = model_path
-
-tokenizer = BartTokenizer.from_pretrained(tokenizer_path)
-model = TFBartForConditionalGeneration.from_pretrained(model_path)
-
-def summarize_text(text):
-    inputs = tokenizer.encode('summarize: ' + text, return_tensors='tf', max_length=1024, truncation=True)
-    summary_ids = model.generate(
-        inputs,
-        max_length=150,
-        min_length=40,
-        length_penalty=2.0,
-        num_beams=4,
-        early_stopping=True
-    )
+def summarize(text):
+    inputs = tokenizer.encode(text, return_tensors='tf', max_length=1024, truncation=True)
+    summary_ids = model.generate(inputs, max_length=150, min_length=30, length_penalty=2.0, num_beams=4, early_stopping=True)
     summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
     return summary
 
-st.title("Text Summarization")
-text = st.text_area("Enter text to summarize", height=200)
-
-if st.button("Summarize"):
-    summary = summarize_text(text)
-    st.write("Summary:")
-    st.write(summary)
+st.title('Text Summarizer')
+user_input = st.text_area("Enter text to summarize:", "")
+if st.button('Summarize'):
+    if user_input:
+        summary = summarize(user_input)
+        st.write(summary)
+    else:
+        st.write("Please enter some text to summarize.")

requirements.txt

@@ -1,3 +1,4 @@
-transformers
-tensorflow
-streamlit
+tensorflow==2.12.0
+transformers==4.29.0
+tf-keras==2.11.0
+streamlit==1.22.0