Initial commit

app.py

@@ -0,0 +1,30 @@
+import streamlit as st
+from transformers import BartTokenizer, TFBartForConditionalGeneration
+
+# Load the model and tokenizer
+model_path = 'facebook/bart-large-cnn'
+tokenizer_path = 'facebook/bart-large-cnn'
+
+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
+    )
+    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)

requirements.txt

@@ -0,0 +1,3 @@
+transformers
+tensorflow
+streamlit