Upload app.py

app.py

@@ -1,13 +1,127 @@
-import pymupdf  # PyMuPDF
+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
-from collections import Counter
+import re
+
 from streamlit_image_zoom import image_zoom
 from PIL import Image
-import streamlit as st
 
-nltk.download('stopwords')
+
+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.")
+
+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
+
+# 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
@@ -45,6 +159,10 @@ def highlight_pdf(file_path, text_to_highlight, page_numbers):
 
     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 = []
@@ -55,13 +173,13 @@ def pdf_to_images(pdf_path, page_numbers):
         images.append(img)
     return images
 
-def display_highlighted_pdf():
-    pdf_path = "Bhagavad-Gita-As-It-Is.pdf"
-    sources = [7, 8, 18, 20, 40, 66]
-    response_text = ("I offer my respectful obeisances unto the lotus feet of my spiritual master "
-                     "and unto the feet of all Vaishnavas. I offer my respectful")
-
-    highlighted_pdf_path, new_page_numbers = highlight_pdf(file_path=pdf_path, text_to_highlight=response_text, page_numbers=sources)
+# 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)
 
@@ -84,4 +202,154 @@ def display_highlighted_pdf():
                 else:
                     st.error("The provided image is not a valid Pillow Image object.")
 
-display_highlighted_pdf()
+# 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.
+- 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.
+- 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()
+    display_highlighted_pdf(file_path, result, sources)
+
+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(
+    "Select Your Favorite Scriptures",
+    ("what is jeeva and paramathma?",
+     "What are the Krishna told to Arjuna?",
+     "What are the key points from Krishna?",
+     "Why does atheism exist even when all questions are answered in Bhagavad Gita?", 
+     "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 war?"),
+    # index=None,
+    # placeholder="Select scriptures...",
+    key="faq",
+    on_change=open_chat
+    )
+    st.write("You selected:", faq)
+    
+
+    if "chat_history" not in st.session_state:
+        st.session_state["chat_history"] = []
+
+    st.chat_input(question, on_submit=chat_actions, key="chat_input")
+
+    for i in st.session_state["chat_history"]:
+        with st.chat_message(name=i["role"]):
+            st.write(i["content"])
+
+
+
+
+