app.py

import streamlit as st
import os
from streamlit_chat import message
import numpy as np
import pandas as pd
from io import StringIO
import io
import PyPDF2
import pymupdf
import tempfile
import base64
# from tqdm.auto import tqdm
import math
# from transformers import pipeline


from collections import Counter
import nltk

nltk.download('stopwords')
from nltk.corpus import stopwords
import re

from streamlit_image_zoom import image_zoom
from PIL import Image


from sentence_transformers import SentenceTransformer
import torch
from langchain_community.llms.ollama import Ollama
from langchain.prompts import ChatPromptTemplate
from langchain_community.vectorstores import FAISS

from langchain_community.llms import HuggingFaceHub
# from langchain.vectorstores import faiss
# from langchain.vectorstores import FAISS

import time
from time import sleep
from stqdm import stqdm
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")


device = 'cuda' if torch.cuda.is_available() else 'cpu'

# if device != 'cuda':
#     st.markdown(f"you are using {device}. This is much slower than using "
#     "a CUDA-enabled GPU. If on colab you can change this by "
#     "clicking Runtime > change runtime type > GPU.")
st.set_page_config(page_title="Vedic Scriptures",page_icon='📝')
model = SentenceTransformer("sentence-transformers/all-mpnet-base-v2", device=device)
def display_title():
    selected_value = st.session_state["value"]

    st.header(f'Vedic Scriptures: {selected_value} :blue[book] :books:')

question = "ask anything about scriptures"
def open_chat():
    question = st.session_state["faq"]

    

if "value" not in st.session_state:
    st.session_state["value"] = None

if "faq" not in st.session_state:
    st.session_state["faq"] = None

url1 = "https://vedabase.io/en/library/bg/"
url2 = "https://docs.google.com/file/d/0B5WZMlc4xl-8NThSSDJnTmE5N2M/view?resourcekey=0-CupZPMHFLx-54g_UDTOTYA"
st.write("👈🏻 :rainbow[slide to ask bhagvatgeetha questions]")
st.write("choose FAQ or ask your own doubts")
st.markdown(":rainbow[checkout source reference]: :blue-background[ISKCON] [1](%s), [2](%s) — :tulip::cherry_blossom::rose::hibiscus::sunflower::blossom:" % (url1, url2))

# st.divider()

def upload_file():
    uploaded_file = st.file_uploader("Upload a file", type=["pdf"])
    if uploaded_file is not None:
        st.write(uploaded_file.name)
        return uploaded_file.name

def create_pickle_file(filepath):

    from langchain_community.document_loaders import PyMuPDFLoader
    loader = PyMuPDFLoader(filepath)
    pages = loader.load()

    # Load a pre-trained sentence transformer model
    model_name = "sentence-transformers/all-mpnet-base-v2"
    model_kwargs = {'device': 'cpu'}
    encode_kwargs = {'normalize_embeddings': False}

    # Create a HuggingFaceEmbeddings object
    from langchain_community.embeddings import HuggingFaceEmbeddings
    embeddings = HuggingFaceEmbeddings(model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs)

    # from pathlib import Path

    # path = Path(filepath)

    filename = filepath.split(".")

    print(filename[0])

    filename = filename[0]

    from datetime import datetime

    # Get current date and time
    now = datetime.now()

    # Format as string with milliseconds
    formatted_datetime = now.strftime("%Y-%m-%d_%H:%M:%S.%f")[:-3]

    print(formatted_datetime)

    # Create FAISS index with the HuggingFace embeddings
    faiss_index = FAISS.from_documents(pages, embeddings)
    with open(f"./{filename}_{formatted_datetime}.pkl", "wb") as f:
        pickle.dump(faiss_index, f)


# uploaded_file_name = upload_file()
# if uploaded_file_name is not None:
#     create_pickle_file(uploaded_file_name)

def highlight_pdf(file_path, text_to_highlight, page_numbers):
    # Open the original PDF
    doc = pymupdf.open(file_path)
    pages_to_display = [doc.load_page(page_number - 1) for page_number in page_numbers]

    # Tokenize the text into words
    words = text_to_highlight.split()

    # Remove stopwords
    stop_words = set(stopwords.words("english"))
    words = [word for word in words if word.lower() not in stop_words]

    # Highlight the specified words on the canvas
    for page in pages_to_display:
        for word in words:
            highlight_rects = page.search_for(word, quads=True)
            for rect in highlight_rects:
                page.add_highlight_annot(rect)

    # Create a new document with only the specified pages
    new_doc = pymupdf.open()
    new_page_numbers = []

    for page in pages_to_display:
        new_doc.insert_pdf(doc, from_page=page.number, to_page=page.number)
        new_page_numbers.append(new_doc.page_count)  # Keep track of new page numbers

    # Save the modified PDF to a temporary file
    with tempfile.NamedTemporaryFile(suffix=".pdf", delete=False) as temp_file:
        temp_pdf_path = temp_file.name
        new_doc.save(temp_file.name)

    new_doc.save("example_highlighted.pdf")

    return temp_pdf_path, new_page_numbers

file_path = "Bhagavad-Gita-As-It-Is.pdf"
text_to_highlight = ""
sources = []

def pdf_to_images(pdf_path, page_numbers):
    doc = pymupdf.open(pdf_path)
    images = []
    for page_number in page_numbers:
        page = doc.load_page(page_number - 1)
        pix = page.get_pixmap()
        img = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
        images.append(img)
    return images

# Function to display PDF in Streamlit
def display_highlighted_pdf(file_path, text_to_highlight, sources):
    # pdf_path = "../Transformers/Bhagavad-Gita-As-It-Is.pdf"
    # sources = [7,8]
    # response_text = "I offer my respectful obeisances unto the lotus feet of my spiritual master and unto the feet of all Vaiñëavas. I offer my respectful"
    
    highlighted_pdf_path, new_page_numbers = highlight_pdf(file_path=file_path, text_to_highlight=text_to_highlight, page_numbers=sources)

    images = pdf_to_images(highlighted_pdf_path, new_page_numbers)

    # Calculate the number of rows and columns based on the number of pages
    num_pages = len(new_page_numbers)
    num_cols = 2  # Number of columns
    num_rows = (num_pages + num_cols - 1) // num_cols  # Number of rows

    # Display images in a grid layout with spacing
    for row in range(num_rows):
        cols = st.columns(num_cols)
        for col in range(num_cols):
            idx = row * num_cols + col
            if idx < num_pages:
                img = images[idx]
                if isinstance(img, Image.Image):
                    with cols[col]:
                        st.image(img, use_column_width=True)
                        st.write("")  # Add spacing
                else:
                    st.error("The provided image is not a valid Pillow Image object.")

# Creating a Index(Pinecone Vector Database)
import os
# import pinecone

import pickle
@st.cache_data
def get_faiss_semantic_index():
    try:
        index_path = "./HuggingFaceEmbeddings.pkl"
        print(index_path)
        # Load embeddings from the pickle file
        for _ in stqdm(range(5)):
            with open(index_path, "rb") as f:
                faiss_index = pickle.load(f)
                sleep(0.1)
        # st.write("Embeddings loaded successfully.")
        return faiss_index
    except Exception as e:
        st.error(f"Error loading embeddings: {e}")
        return None
faiss_index = get_faiss_semantic_index()
print(faiss_index)

# def promt_engineer(text):
PROMPT_TEMPLATE = """
Instructions:
-------------------------------------------------------------------------------------------------------------------------------
Answer the question only based on the below context:
- You're a Vedic AI expert in the Hindu Vedic scriptures.
- Questions with out-of-context replay with The question is out of context. 
- Always try to provide Keep it simple answers in nice format without incomplete sentence.
- Give the answer atleast 5 seperate lines addition to the title info.
- Only If question is relevent to context provide Title: <title> Chapter: <chapter> Text No: <textnumber> Page No: <pagenumber> 
-------------------------------------------------------------------------------------------------------------------------------

{context}

-------------------------------------------------------------------------------------------------------------------------------

Answer the question based on the above context: {question}
"""
    # # Load the summarization pipeline with the specified model
    # summarizer = pipeline("summarization", model="facebook/bart-large-cnn")

    # # Generate the prompt
    # prompt = prompt_template.format(text=text)

    # # Generate the summary
    # summary = summarizer(prompt, max_length=1024, min_length=50)[0]["summary_text"]
    
    # with st.sidebar:
    #     st.divider()
    #     st.markdown("*:red[Text Summary Generation]* from above Top 5 **:green[similarity search results]**.")
    #     st.write(summary)
    #     st.divider()

def chat_actions():

    st.session_state["chat_history"].append(
        {"role": "user", "content": st.session_state["chat_input"]},
    )

    # query_embedding = model.encode(st.session_state["chat_input"])
    query = st.session_state["chat_input"]
    if faiss_index is not None:
        docs = faiss_index.similarity_search(query, k=6)
    else:
        st.error("Failed to load embeddings.")
    # docs = faiss_index.similarity_search(query, k=2)

    for doc in docs:
        print("\n")
        print(str(doc.metadata["page"]+1) + ":", doc.page_content)
    context_text = "\n\n---\n\n".join([doc.page_content for doc in docs])

    sources = [doc.metadata.get("page", None) for doc in docs]
    

    prompt_template = ChatPromptTemplate.from_template(PROMPT_TEMPLATE)
    prompt = prompt_template.format(context=context_text, question=query)
    response_text = ""
    result = ""
    try:
        llm = HuggingFaceHub(
            repo_id="meta-llama/Meta-Llama-3-8B-Instruct", model_kwargs={"temperature": 0.1, "max_new_tokens": 256, "task":"text-generation"}
        )
        response_text = llm.invoke(prompt)
        escaped_query = re.escape(query)
        result = re.split(f'Answer the question based on the above context: {escaped_query}\n',response_text)[-1]
        st.write(result)
    except Exception as e:
        st.error(f"Error invoke: {e}")


    formatted_response = f"Response: {result}\nSources: {sources}"
    print(formatted_response)

    st.session_state["chat_history"].append(
        {
            "role": "assistant",
            "content": f"{result}",
        },  # This can be replaced with your chat response logic
    )
        # break;
    # Example usage
    file_path = "Bhagavad-Gita-As-It-Is.pdf"
    text_to_highlight = context_text.strip()

    if "out of context" not in result:
        st.success("We found some helpful pages related to your question. Please refer to the highlighted sections below.")
        display_highlighted_pdf(file_path, result, sources)
    else:
        st.error("Unfortunately, the question is out of context, and we couldn't find relevant pages for you.")

with st.sidebar:
    option = st.selectbox(
    "Select Your Favorite Scriptures",
    ("Bhagvatgeetha", "Bhagavatham", "Ramayanam"),
    # index=None,
    # placeholder="Select scriptures...",
    key="value",
    on_change=display_title
    )

    st.write("You selected:", option)

    faq = st.selectbox(
    "Check FAQ'S",
    ("what is jeevathma and paramathma?",
     "who am I?",
     "who are you?",
     "what is this book all about?",
     "who is supreme god head and why?",
     "what is the most spoken topic by Krishna?",
     "What Krishna says to Arjuna?",
     "What are the key points from Krishna?",
     "Why does atheism exist even when all questions are answered in BhagavadGita?", 
     "Why don’t all souls surrender to Lord Krishna, although he has demonstrated that everyone is part and parcel of Him, and all can be liberated from all sufferings by surrendering to Him?",
     "Why do souls misuse their independence by rebelling against Lord Krishna?",
     "How do I put an end to my suffering in this world?",
     "what is the reason behind Krishna decided to go far battle?"),
    # index=None,
    # placeholder="Select scriptures...",
    key="faq",
    on_change=open_chat
    )
    st.write("You selected:", faq)
    st.write("Copy FAQ or ask your Query below👇🏻")
    

    if "chat_history" not in st.session_state:
        st.session_state["chat_history"] = []

    st.chat_input(question, on_submit=chat_actions, key="chat_input")
    st.write(":rainbow[side to read script] 👉🏻")

    # for i in st.session_state["chat_history"]:
    #     with st.chat_message(name=i["role"]):
    #         st.write(i["content"])