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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 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(file_path): # Expecting file path string
st.session_state.embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
main_placeholder = st.empty()
main_placeholder.text("Data Loading...Started...✅✅✅")
# Load the PDF from the given file path
loader = PyPDFLoader(file_path)
doc = loader.load()
main_placeholder.text("Text Splitter...Started...✅✅✅")
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
final_documents = text_splitter.split_documents(doc)
if final_documents:
main_placeholder.text("Embedding Vector Started Building...✅✅✅")
st.session_state.vectors = FAISS.from_documents(final_documents, st.session_state.embeddings)
st.session_state.docs = final_documents
# Save FAISS vector store to disk
faiss_index = st.session_state.vectors.index
faiss.write_index(faiss_index, "faiss_index.bin")
main_placeholder.text("Vector database created!...✅✅✅")
else:
st.error("No documents found or the PDF is corrupted.")
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