app.py

import streamlit as st
from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate
from llama_index.llms.huggingface import HuggingFaceInferenceAPI
from dotenv import load_dotenv
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core import Settings
from youtube_transcript_api import YouTubeTranscriptApi
import shutil
import os
import time

# Load environment variables
load_dotenv()

icons = {"assistant": "robot.png", "user": "man-kddi.png"}

# Configure the Llama index settings
Settings.llm = HuggingFaceInferenceAPI(
    model_name="meta-llama/Meta-Llama-3-8B-Instruct",
    tokenizer_name="meta-llama/Meta-Llama-3-8B-Instruct",
    context_window=3900,
    token=os.getenv("HF_TOKEN"),
    # max_new_tokens=1000,
    generate_kwargs={"temperature": 0.1},
)
Settings.embed_model = HuggingFaceEmbedding(
    model_name="BAAI/bge-small-en-v1.5"
)

# Define the directory for persistent storage and data
PERSIST_DIR = "./db"
DATA_DIR = "data"

# Ensure data directory exists
os.makedirs(DATA_DIR, exist_ok=True)
os.makedirs(PERSIST_DIR, exist_ok=True)

def data_ingestion():
    documents = SimpleDirectoryReader(DATA_DIR).load_data()
    storage_context = StorageContext.from_defaults()
    index = VectorStoreIndex.from_documents(documents)
    index.storage_context.persist(persist_dir=PERSIST_DIR)

def remove_old_files():
    # Specify the directory path you want to clear
    directory_path = "data"

    # Remove all files and subdirectories in the specified directory
    shutil.rmtree(directory_path)

    # Recreate an empty directory if needed
    os.makedirs(directory_path)

def extract_transcript_details(youtube_video_url):
    try:
        video_id=youtube_video_url.split("=")[1]
        
        transcript_text=YouTubeTranscriptApi.get_transcript(video_id)

        transcript = ""
        for i in transcript_text:
            transcript += " " + i["text"]
       
        return transcript

    except Exception as e:
        st.error(e)

def handle_query(query):
    storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
    index = load_index_from_storage(storage_context)
    chat_text_qa_msgs = [
    (
        "user",
        """You are Q&A assistant named CHATTO, created by Pachaiappan an AI Specialist. Your main goal is to provide answers as accurately as possible, based on the instructions and context you have been given. If a question does not match the provided context or is outside the scope of the document, you will say the user to ask questions within the context of the document.
        Context:
        {context_str}
        Question:
        {query_str}
        """
    )
    ]
    text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs)
    query_engine = index.as_query_engine(text_qa_template=text_qa_template)
    answer = query_engine.query(query)
    
    
    if hasattr(answer, 'response'):
        return answer.response
    elif isinstance(answer, dict) and 'response' in answer:
        return answer['response']
    else:
        return "Sorry, I couldn't find an answer."

def streamer(text):
    for i in text:
        yield i
        time.sleep(0.001)


# Streamlit app initialization
st.title("Chat with your PDF📄")
st.markdown("**Built by [Pachaiappan❤️](https://github.com/Mr-Vicky-01)**")

if 'messages' not in st.session_state:
    st.session_state.messages = [{'role': 'assistant', "content": 'Hello! Upload a PDF and ask me anything about the content.'}]

for message in st.session_state.messages:
    with st.chat_message(message['role'], avatar=icons[message['role']]):
        st.write(message['content'])

with st.sidebar:
    st.title("Menu:")
    uploaded_file = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button")
    video_url = st.text_input("Enter Youtube Video Link: ")
    if st.button("Submit & Process"):
        with st.spinner("Processing..."):
            if len(os.listdir("data")) !=0:
                remove_old_files()
                
            if uploaded_file:
                filepath = "data/saved_pdf.pdf"
                with open(filepath, "wb") as f:
                    f.write(uploaded_file.getbuffer())
        
            if video_url:
                extracted_text = extract_transcript_details(video_url)
                with open("data/saved_text.txt", "w") as file:
                    file.write(extracted_text)
                
            data_ingestion()  # Process PDF every time new file is uploaded
            st.success("Done")

user_prompt = st.chat_input("Ask me anything about the content of the PDF:")

if user_prompt and uploaded_file:
    st.session_state.messages.append({'role': 'user', "content": user_prompt})
    with st.chat_message("user", avatar="man-kddi.png"):
        st.write(user_prompt)

    # Trigger assistant's response retrieval and update UI
    with st.spinner("Thinking..."):
        response = handle_query(user_prompt)
    with st.chat_message("user", avatar="robot.png"):
        st.write_stream(streamer(response))
    st.session_state.messages.append({'role': 'assistant', "content": response})