Create app.py

app.py

@@ -0,0 +1,41 @@
+from pypdf import PdfReader
+import streamlit as st
+
+def get_pdf_text(pdf_docs):
+    text = ""
+    for pdf in pdf_docs:
+        pdf_reader = PdfReader(pdf)
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+    return text
+
+raw_text=""
+with st.sidebar:
+        st.title("Menu:")
+        pdf_docs = st.file_uploader(
+            "Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True
+        )
+        if st.button("Submit & Process"):
+         with st.spinner("Processing..."):
+            raw_text = get_pdf_text(pdf_docs)
+
+st.write(raw_text)
+
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Load the pre-trained tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-cnn")
+model = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large-cnn")
+
+# Tokenize the text
+inputs = tokenizer(raw_text, return_tensors="pt", max_length=1024, truncation=True)
+
+# Generate the summary
+summary_ids = model.generate(inputs["input_ids"], num_beams=4, min_length=30, max_length=200, early_stopping=True)
+
+# Decode the summary
+summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+
+print("\n\nSummary:\n", summary)
+print("\n\n\nOriginal text:\n", raw_text)
+