added application file

app.py

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+import streamlit as st 
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.document_loaders import PyPDFLoader
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+from transformers import pipeline
+import base64
+from huggingface_hub import login
+import torch
+import fitz  # PyMuPDF
+
+
+# model and tokenizer loading
+checkpoint = "MBZUAI/LaMini-Flan-T5-248M"
+
+
+# Model and tokenizer loading
+# checkpoint = "model/google-flan-t5-base"
+tokenizer = T5Tokenizer.from_pretrained(checkpoint)
+base_model = T5ForConditionalGeneration.from_pretrained(checkpoint, device_map='auto', torch_dtype=torch.float32)
+
+# LLM pipeline
+def llm_pipeline(pdf_contents):
+    # Extract text from the PDF contents
+    pdf_document = fitz.open(stream=pdf_contents, filetype="pdf")
+    pdf_text = ""
+    for page_num in range(pdf_document.page_count):
+        page = pdf_document.load_page(page_num)
+        pdf_text += page.get_text()
+
+    # Use the pipeline to generate the summary
+    pipe_sum = pipeline(
+        'summarization',
+        model=base_model,
+        tokenizer=tokenizer,
+        max_length=500,
+        min_length=50
+    )
+
+    result = pipe_sum(pdf_text)
+    summary = result[0]['summary_text']
+    return summary
+
+# Streamlit code
+st.set_page_config(layout="wide")
+
+def main():
+    st.title("Document Summarization App using Language Model")
+
+    uploaded_file = st.file_uploader("Upload your PDF file", type=['pdf'])
+
+    if uploaded_file is not None:
+        if st.button("Summarize"):
+            summary = llm_pipeline(uploaded_file.read())
+
+            # Display the summary
+            st.info("Summarization Complete")
+            st.success(summary)
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+numpy
+pandas
+langchain
+sentence_transformers
+torch
+sentencepiece
+transformers
+accelerate
+chromadb
+pypdf
+tiktoken
+streamlit
+fastapi
+uvicorn
+python-multipart
+aiofiles
+pdfminer.six
+