# 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 
# import os


# 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) ## write file to temporary
#             temp_file_path = temp_file.name  # Get the temporary file path

#         # temp_file_path = os.path.join("/tmp", uploaded_file.name)
#         # with open(temp_file_path, "wb") as f:
#         #     f.write(uploaded_file.read())

#         # st.write(f"Uploaded files: {[file.name for file in uploaded_file]}")

            
            
#             # 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.")



# # def process_pdf(uploaded_files):
# #     all_documents = []
# #     main_placeholder = st.empty()
# #     main_placeholder.text("Data Loading...Started...✅✅✅")

# #     for uploaded_file in uploaded_files:
# #         temp_file_path = os.path.join("/tmp", uploaded_file.name)
# #         with open(temp_file_path, "wb") as f:
# #             f.write(uploaded_file.read())

# #         st.write(f"Uploaded files: {[file.name for file in uploaded_files]}")

# #         loader = PyPDFLoader(temp_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)
# #         all_documents.extend(final_documents)

# #     if all_documents:
# #         main_placeholder.text("Embedding Vector Started Building...✅✅✅")
        
# #         # ⏬ Move embedding initialization here
# #         st.session_state.embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
# #         st.session_state.vectors = FAISS.from_documents(all_documents, st.session_state.embeddings)
# #         st.session_state.docs = all_documents

# #         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.")



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 
import os


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

            output_path = "/tmp/faiss_index.bin"  # or another writable path
            faiss.write_index(faiss_index, output_path)
            
            main_placeholder.text("Vector database created!...✅✅✅")   
            
        else:
            st.error("No documents found after processing the uploaded files or the pdf is corrupted / unsupported.")