app.py

import streamlit as st 
import os
import base64
import time
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM 
from transformers import pipeline
import torch 
import textwrap 
from langchain.document_loaders import PyPDFLoader, DirectoryLoader, PDFMinerLoader 
from langchain.text_splitter import RecursiveCharacterTextSplitter 
from langchain.embeddings import SentenceTransformerEmbeddings 
from langchain.vectorstores import Chroma 
from langchain.llms import HuggingFacePipeline
from langchain.chains import RetrievalQA 
from constants import CHROMA_SETTINGS
from streamlit_chat import message

st.set_page_config(layout="wide")

device = torch.device('cpu')

checkpoint = "MBZUAI/LaMini-T5-738M"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
base_model = AutoModelForSeq2SeqLM.from_pretrained(
    checkpoint,
    device_map=device,
    torch_dtype=torch.float32
)

persist_directory = "db"

@st.cache_resource
def data_ingestion():
    for root, dirs, files in os.walk("docs"):
        for file in files:
            if file.endswith(".pdf"):
                print(file)
                loader = PDFMinerLoader(os.path.join(root, file))
    
    documents = loader.load()
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=500)
    splits = text_splitter.split_documents(document)
    
    #create embeddings here
    embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
    vectordb = FAISS.from_documents(splits, embeddings)
    vectordb.save_local("faiss_index")

    
@st.cache_resource
def qa_llm():
    pipe = pipeline(
        'text2text-generation',
        model = base_model,
        tokenizer = tokenizer,
        max_length = 256,
        do_sample = True,
        temperature = 0.3,
        top_p= 0.95,
        device=device
    )
    
    llm = HuggingFacePipeline(pipeline=pipe)
    embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
    
    vectordb = FAISS.load_local("faiss_index", embeddings)
    retriever = db.as_retriever()
    
    # Build a QA chain
    qa_chain = RetrievalQA.from_chain_type(
        llm=llm,
        chain_type="stuff",
        retriever=vectordb.as_retriever(),
    )
    return qa_chain

def process_answer(instruction):
    response = ''
    instruction = instruction
    qa_chain = qa_llm()
    
    generated_text = qa_chain.run(instruction)
    return generated_text

def get_file_size(file):
    file.seek(0, os.SEEK_END)
    file_size = file.tell()
    file.seek(0)
    return file_size

@st.cache_data
#function to display the PDF of a given file 
def displayPDF(file):
    # Opening file from file path
    with open(file, "rb") as f:
        base64_pdf = base64.b64encode(f.read()).decode('utf-8')

    # Embedding PDF in HTML
    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)

# Display conversation history using Streamlit messages
def display_conversation(history):
    for i in range(len(history["generated"])):
        message(history["past"][i], is_user=True, key=str(i) + "_user")
        message(history["generated"][i],key=str(i))

def main():
    st.markdown("<h1 style='text-align: center; color: blue;'>Chat with your PDF 🦜📄 </h1>", unsafe_allow_html=True)
    st.markdown("<h3 style='text-align: center; color: grey;'>Built by <a href='https://github.com/AIAnytime'>AI Anytime with ❤️ </a></h3>", unsafe_allow_html=True)

    st.markdown("<h2 style='text-align: center; color:red;'>Upload your PDF 👇</h2>", unsafe_allow_html=True)

    uploaded_file = st.file_uploader("", type=["pdf"])

    if uploaded_file is not None:
        file_details = {
            "Filename": uploaded_file.name,
            "File size": get_file_size(uploaded_file)
        }
        filepath = "docs/"+uploaded_file.name
        with open(filepath, "wb") as temp_file:
                temp_file.write(uploaded_file.read())

        col1, col2= st.columns([1,2])
        with col1:
            st.markdown("<h4 style color:black;'>File details</h4>", unsafe_allow_html=True)
            st.json(file_details)
            st.markdown("<h4 style color:black;'>File preview</h4>", unsafe_allow_html=True)
            pdf_view = displayPDF(filepath)

        with col2:
            with st.spinner('Embeddings are in process...'):
                ingested_data = data_ingestion()
            st.success('Embeddings are created successfully!')
            st.markdown("<h4 style color:black;'>Chat Here</h4>", unsafe_allow_html=True)


            user_input = st.text_input("", key="input")

            # Initialize session state for generated responses and past messages
            if "generated" not in st.session_state:
                st.session_state["generated"] = ["I am ready to help you"]
            if "past" not in st.session_state:
                st.session_state["past"] = ["Hey there!"]
                
            # Search the database for a response based on user input and update session state
            if user_input:
                answer = process_answer({'query': user_input})
                st.session_state["past"].append(user_input)
                response = answer
                st.session_state["generated"].append(response)

            # Display conversation history using Streamlit messages
            if st.session_state["generated"]:
                display_conversation(st.session_state)
        

if __name__ == "__main__":
    main()