src/PDFprocess_sample.py

# import tempfile
# import streamlit as st
# import pickle
# from langchain_google_genai import GoogleGenerativeAIEmbeddings
# from langchain_community.document_loaders import PyPDFLoader
# from langchain.text_splitter import RecursiveCharacterTextSplitter
# from langchain_community.vectorstores import FAISS
# import faiss 


# # def process_pdf(uploaded_file):
    
# #     all_documents = []
# #     st.session_state.embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
    
# #     main_placeholder = st.empty()
# #     # Creating  a temporary file to store the uploaded PDF's
# #     main_placeholder.text("Data Loading...Started...✅✅✅")
# #     for uploaded_file in uploaded_file:
# #         with tempfile.NamedTemporaryFile(delete=False , suffix='.pdf') as temp_file:
# #             temp_file.write(uploaded_file.read()) ## write file to temporary
# #             temp_file_path = temp_file.name  # Get the temporary file path
            
            
# #             # Load the PDF's from the temporary file path
            
        
# #         loader = PyPDFLoader(temp_file_path) # Document loader
# #         doc= loader.load() # load Document 
# #         main_placeholder.text("Text Splitter...Started...✅✅✅")
# #         text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200) # Recursive Character String
# #         #final_documents = text_splitter.split_documents(doc)# splitting
# #         final_documents = text_splitter.split_documents(doc)
# #         all_documents.extend(final_documents)
        
        
# #         if all_documents:
# #             main_placeholder.text("Embedding Vector Started Building...✅✅✅")
# #             st.session_state.vectors = FAISS.from_documents(all_documents,st.session_state.embeddings)
# #             st.session_state.docs = all_documents 
            
# #             # Save FAISS vector store to disk
# #             faiss_index = st.session_state.vectors.index  # Extract FAISS index
# #             faiss.write_index(faiss_index, "faiss_index.bin")  # Save index to a binary file
# #             main_placeholder.text("Vector database created!...✅✅✅")   
            
# #         else:
# #             st.error("No documents found after processing the uploaded files or the pdf is corrupted / unsupported.")


import streamlit as st
import faiss
from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_google_genai import GoogleGenerativeAIEmbeddings
from langchain.vectorstores import FAISS

def process_pdf_from_path(file_path):
    """
    Processes a PDF from a given path by:
    - Loading the PDF
    - Splitting it into manageable chunks
    - Creating embeddings with Gemini
    - Saving the FAISS vector index to disk

    Parameters:
    file_path (str): Path to the uploaded PDF file
    """

    all_documents = []

    try:
        # Initialize embeddings model
        st.session_state.embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")

        main_placeholder = st.empty()
        main_placeholder.text("Loading PDF and preparing text... ✅")

        # Load PDF document
        loader = PyPDFLoader(file_path)
        documents = loader.load()

        # Split documents into smaller chunks
        main_placeholder.text("Splitting text into chunks... ✅")
        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
        final_documents = text_splitter.split_documents(documents)
        all_documents.extend(final_documents)

        if all_documents:
            main_placeholder.text("Creating vector embeddings... ✅")
            # Generate vector store
            st.session_state.vectors = FAISS.from_documents(all_documents, st.session_state.embeddings)
            st.session_state.docs = all_documents

            # Save FAISS index
            faiss_index = st.session_state.vectors.index
            faiss.write_index(faiss_index, "/tmp/faiss_index.bin")

            main_placeholder.text("Vector database created successfully! 🎉")
        else:
            st.error("No valid documents found in the uploaded PDF.")
    
    except Exception as e:
        st.error(f"An error occurred while processing the PDF: {e}")