Create app.py

app.py

@@ -0,0 +1,44 @@
+import streamlit as st
+from transformers import pipeline, AutoTokenizer
+
+# Define the model
+model = "facebook/bart-large-cnn"
+
+@st.cache_resource
+def load_summarizer():
+    return pipeline("summarization", model=model)
+
+def get_model_max_length(model_name):
+    try:
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        return tokenizer.model_max_length
+    except:
+        return 1024  # default value if unable to determine
+
+def generate_summary(text):
+    summarizer = load_summarizer()
+    max_input_length = min(summarizer.tokenizer.model_max_length, 1024)
+    truncated_text = summarizer.tokenizer.decode(
+        summarizer.tokenizer.encode(text, truncation=True, max_length=max_input_length)
+    )
+    summary = summarizer(truncated_text, max_new_tokens=150, min_new_tokens=40, do_sample=False)[0]['summary_text']
+    return summary
+
+st.title("Text Summarization Tool")
+
+st.write("This tool uses the BART-large-CNN model to generate summaries of input text.")
+
+input_text = st.text_area("Enter the text you want to summarize:", height=200)
+
+if st.button("Generate Summary"):
+    if input_text:
+        with st.spinner("Generating summary..."):
+            summary = generate_summary(input_text)
+        st.subheader("Summary:")
+        st.write(summary)
+    else:
+        st.warning("Please enter some text to summarize.")
+
+st.write("---")
+st.write("Model: facebook/bart-large-cnn")
+st.write(f"Max input length: {get_model_max_length(model)}")