app.py

import os
import torch
import streamlit as st
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_core.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain_community.llms import HuggingFacePipeline
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
from dotenv import load_dotenv

# Set Streamlit page configuration
st.set_page_config(page_title="Chat with Notes and AI", page_icon=":books:", layout="wide")

# Load environment variables
load_dotenv()

# Dolly-v2-3b model pipeline
@st.cache_resource
def load_pipeline():
    model_name = "databricks/dolly-v2-3b"

    # Load tokenizer
    tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left", trust_remote_code=True)

    # Load model with offload folder for disk storage of weights
    model = AutoModelForCausalLM.from_pretrained(
        model_name,
        torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,  # Use float16 for GPU, float32 for CPU
        device_map="auto",          # Automatically map model to available devices (e.g., GPU if available)
        trust_remote_code=True,
        offload_folder="./offload_weights"  # Folder to store offloaded weights
    )

    # Return text-generation pipeline
    return pipeline(
        task="text-generation",
        model=model,
        tokenizer=tokenizer,
        torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
        device_map="auto",
        return_full_text=True
    )

# Initialize Dolly pipeline
generate_text = load_pipeline()

# Create a HuggingFace pipeline wrapper for LangChain
hf_pipeline = HuggingFacePipeline(pipeline=generate_text)

# Template for instruction-only prompts
prompt = PromptTemplate(
    input_variables=["instruction"],
    template="{instruction}"
)

# Template for prompts with context
prompt_with_context = PromptTemplate(
    input_variables=["instruction", "context"],
    template="{instruction}\n\nInput:\n{context}"
)

# Create LLM chains
llm_chain = LLMChain(llm=hf_pipeline, prompt=prompt)
llm_context_chain = LLMChain(llm=hf_pipeline, prompt=prompt_with_context)

# Extracting text from .txt files
def get_text_files_content(folder):
    text = ""
    for filename in os.listdir(folder):
        if filename.endswith('.txt'):
            with open(os.path.join(folder, filename), 'r', encoding='utf-8') as file:
                text += file.read() + "\n"
    return text

# Converting text to chunks
def get_chunks(raw_text):
    from langchain.text_splitter import CharacterTextSplitter
    text_splitter = CharacterTextSplitter(
        separator="\n",
        chunk_size=1000,  # Reduced chunk size for faster processing
        chunk_overlap=200,  # Smaller overlap for efficiency
        length_function=len
    )
    chunks = text_splitter.split_text(raw_text)
    return chunks

# Using Hugging Face embeddings model and FAISS to create vectorstore
def get_vectorstore(chunks):
    embeddings = HuggingFaceEmbeddings(
        model_name="sentence-transformers/all-MiniLM-L6-v2",
        model_kwargs={'device': 'cpu'}  # Ensure embeddings use CPU
    )
    vectorstore = FAISS.from_texts(texts=chunks, embedding=embeddings)
    return vectorstore

# Generating response from user queries
def handle_question(question, vectorstore=None):
    if vectorstore:
        # Reduce the number of retrieved chunks for faster processing
        documents = vectorstore.similarity_search(question, k=2)
        context = "\n".join([doc.page_content for doc in documents])

        # Limit context to 1000 characters to speed up model inference
        context = context[:1000]

        if context:
            result_with_context = llm_context_chain.invoke({"instruction": question, "context": context})
            return result_with_context

    # Fallback to instruction-only chain if no context is found
    return llm_chain.invoke({"instruction": question})

def main():
    st.title("Chat with Notes :books:")

    # Initialize session state
    if "vectorstore" not in st.session_state:
        st.session_state.vectorstore = None

    # Define folders for Current Affairs and Essays
    data_folder = "data"
    essay_folder = "essay"

    # Content type selection
    content_type = st.sidebar.radio("Select Content Type:", ["Current Affairs", "Essays"])

    # Handle Current Affairs (each subject has its own folder)
    if content_type == "Current Affairs":
        if os.path.exists(data_folder):
            subjects = [f for f in os.listdir(data_folder) if os.path.isdir(os.path.join(data_folder, f))]
        else:
            subjects = []
    # Handle Essays (all essays are in a single folder)
    elif content_type == "Essays":
        if os.path.exists(essay_folder):
            subjects = [f.replace(".txt", "") for f in os.listdir(essay_folder) if f.endswith('.txt')]
        else:
            subjects = []

    # Subject selection
    selected_subject = st.sidebar.selectbox("Select a Subject:", subjects)

    # Process selected subject
    raw_text = ""
    if content_type == "Current Affairs" and selected_subject:
        subject_folder = os.path.join(data_folder, selected_subject)
        raw_text = get_text_files_content(subject_folder)
    elif content_type == "Essays" and selected_subject:
        subject_file = os.path.join(essay_folder, selected_subject + ".txt")
        if os.path.exists(subject_file):
            with open(subject_file, "r", encoding="utf-8") as file:
                raw_text = file.read()

    # Display preview of notes
    if raw_text:
        st.subheader("Preview of Notes")
        st.text_area("Preview Content:", value=raw_text[:2000], height=300, disabled=True)  # Show a snippet of the notes

        # Create vectorstore for Current Affairs or Essays
        text_chunks = get_chunks(raw_text)
        vectorstore = get_vectorstore(text_chunks)
        st.session_state.vectorstore = vectorstore
    else:
        st.warning("No content available for the selected subject.")

    # Chat interface
    st.subheader("Ask Your Question")
    question = st.text_input("Ask a question about your selected subject:")
    if question:
        if st.session_state.vectorstore:
            response = handle_question(question, st.session_state.vectorstore)
            st.subheader("Answer:")
            st.write(response.get("text", "No response found."))
        else:
            st.warning("Please load the content for the selected subject before asking a question.")

if __name__ == '__main__':
    main()