Create app.py

app.py

@@ -0,0 +1,22 @@
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Load the model and tokenizer from Hugging Face Model Hub
+model_name = "ASaboor/Saboors_Bart_samsum"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+# Streamlit App
+st.title("Summarization App")
+st.write("This app uses a fine-tuned model to summarize text.")
+
+# Text input
+text = st.text_area("Enter text to summarize")
+
+# Summarize button
+if st.button("Summarize"):
+    inputs = tokenizer.encode("summarize: " + text, return_tensors="pt", max_length=512, 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)
+    st.write("Summary:")
+    st.write(summary)