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")

# Specify the device
device = torch.device('cpu')

checkpoint = "MBZUAI/LaMini-T5-738M"
print(f"Checkpoint path: {checkpoint}")  # Add this line for debugging
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
base_model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)

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)
    texts = text_splitter.split_documents(documents)
    #create embeddings here
    embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
    #create vector store here
    db = Chroma.from_documents(texts, embeddings, persist_directory=persist_directory, client_settings=CHROMA_SETTINGS)
    db.persist()
    db=None

@st.cache_resource
def llm_pipeline():
    pipe = pipeline(
        'text2text-generation',
        model = base_model,
        tokenizer = tokenizer,
        max_length = 256,
        do_sample = True,
        temperature = 0.3,
        top_p= 0.95,
        device=device
    )
    local_llm = HuggingFacePipeline(pipeline=pipe)
    return local_llm

@st.cache_resource
def qa_llm():
    llm = llm_pipeline()
    embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
    db = Chroma(persist_directory="db", embedding_function = embeddings, client_settings=CHROMA_SETTINGS)
    retriever = db.as_retriever()
    qa = RetrievalQA.from_chain_type(
        llm = llm,
        chain_type = "stuff",
        retriever = retriever,
        return_source_documents=True
    )
    return qa

def process_answer(instruction):
    response = ''
    instruction = instruction
    qa = qa_llm()
    generated_text = qa(instruction)
    answer = generated_text['result']
    return answer

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

# Specify the path to your PDF document directly
filepath = "removed_null.pdf"

@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;'>JurioSync📄 </h1>", unsafe_allow_html=True)
    st.markdown("<h3 style='text-align: center; color: grey;'>Ai Powered Legal Document Assistant</h3>", unsafe_allow_html=True)
    st.markdown("<h4 style color:black;'>File details</h4>", unsafe_allow_html=True)
        # You can display any additional file details here if needed
    st.markdown("<h4 style color:black;'>File preview</h4>", unsafe_allow_html=True)
    pdf_view = displayPDF(filepath)
    ingested_data = data_ingestion()
    st.success('Embeddings are created successfully!')
       

        # 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()