Create app.py

app.py

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+import streamlit as st
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+from sentence_transformers import SentenceTransformer
+
+# Load the models
+embedding_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+summarization_model_name = 'facebook/bart-large-cnn'
+tokenizer = AutoTokenizer.from_pretrained(summarization_model_name)
+summarization_model = AutoModelForSeq2SeqLM.from_pretrained(summarization_model_name)
+
+# Define the summarization function
+def summarize_document(document):
+    inputs = tokenizer(document, return_tensors='pt', max_length=1024, truncation=True)
+    summary_ids = summarization_model.generate(inputs['input_ids'], max_length=150, min_length=30, length_penalty=2.0, num_beams=4, early_stopping=True)
+    return tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+
+# Streamlit app
+st.title("Content Summarizer")
+st.write("Enter a document below to get a summary.")
+
+document = st.text_area("Document")
+
+if st.button("Summarize"):
+    if document:
+        with st.spinner('Summarizing...'):
+            summary = summarize_document(document)
+            st.write("**Summary:**")
+            st.write(summary)
+    else:
+        st.write("Please enter a document to summarize.")