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app.py

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+import streamlit as st
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.document_loaders import PyPDFLoader,DirectoryLoader
+from langchain.chains.summarize import load_summarize_chain
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+import base64
+
+#model and tokenizer
+# load the model & tokenizer
+#checkpoint = "LaMini-Flan-T5-248M"
+#tokenizer = T5Tokenizer.from_pretrained(checkpoint)
+#base_model = T5ForConditionalGeneration.from_pretrained(checkpoint, device_map='auto', torch_dtype=torch.float32)
+
+# Load model directly
+tokenizer = AutoTokenizer.from_pretrained("MBZUAI/LaMini-Flan-T5-248M")
+base_model = AutoModelForSeq2SeqLM.from_pretrained("MBZUAI/LaMini-Flan-T5-248M")
+
+#file loader and preprocessing
+def file_preprocessing(file):
+    loader =  PyPDFLoader(file)
+    pages = loader.load_and_split()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=200, chunk_overlap=50)
+    texts = text_splitter.split_documents(pages)
+    final_texts = ""
+    for text in texts:
+        final_texts = final_texts + text.page_content
+    return final_texts, len(final_texts)
+
+# LLM pipeline- using summarization pipleine
+def llm_pipeline(filepath):
+    input_text, input_length = file_preprocessing(filepath)
+    pipe_sum = pipeline(
+        'summarization',
+        model = base_model,
+        tokenizer = tokenizer,
+        max_length = input_length//5, 
+        min_length = 25)
+    result = pipe_sum(input_text)
+    result = result[0]['summary_text']
+    return result
+
+@st.cache_data      #to improve performance by caching
+def displayPDF(file):
+    # Opening file from file path as read binary
+    with open(file, "rb") as f:
+        base64_pdf = base64.b64encode(f.read()).decode('utf-8')
+
+    # Embedding PDF file in the web browser
+    pdf_display = F'<iframe src="data:application/pdf;base64,{base64_pdf}" width="100%" height="600" type="application/pdf"></iframe>'
+
+    # Displaying File
+    st.markdown(pdf_display, unsafe_allow_html=True)
+
+#streamlit code 
+st.set_page_config(page_title='pdf insight',layout="wide",page_icon="📃",initial_sidebar_state="expanded")
+def main():
+    st.title("PDF Insight")
+
+    uploaded_file = st.file_uploader("Upload the PDF", type=['pdf'])
+
+    if uploaded_file is not None:
+        if st.button("Summarize"):
+            col1, col2 = st.columns([0.4,0.6])
+            filepath = "uploaded_pdfs/"+uploaded_file.name
+
+            with open(filepath, "wb") as temp_file:
+                temp_file.write(uploaded_file.read())
+            
+            with col1:
+                st.info("Uploaded PDF")
+                pdf_view = displayPDF(filepath)
+
+            with col2:
+                summary = llm_pipeline(filepath)
+                st.info("Summarization")
+                st.success(summary)
+
+
+#initializing the app
+if __name__ == "__main__":
+    main()
+
+

requirements.txt

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+langchain
+sentence_transformers
+torch
+sentencepiece
+transformers
+accelerate
+chromadb
+pypdf
+tiktoken
+streamlit