app.py

import os
import streamlit as st
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.prompts import PromptTemplate
from langchain.memory import ConversationBufferMemory
from transformers import pipeline
from htmlTemplates import css, bot_template, user_template
from dotenv import load_dotenv

load_dotenv()



# Creating custom template to guide LLM model
custom_template = """Given the following conversation and a follow-up question, rephrase the follow-up question to be a standalone question, in its original language.
Chat History:
{chat_history}
Follow Up Input: {question}
Standalone question:"""

CUSTOM_QUESTION_PROMPT = PromptTemplate.from_template(custom_template)

# 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):
    text_splitter = CharacterTextSplitter(
        separator="\n",
        chunk_size=1000,
        chunk_overlap=200,
        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'}
    )
    vectorstore = FAISS.from_texts(texts=chunks, embedding=embeddings)
    return vectorstore

# Generating conversation chain with improved out-of-scope handling
def get_conversationchain(vectorstore):
    # Use a Hugging Face model for question-answering
    model_name = "distilbert-base-uncased-distilled-squad"  # Pretrained QA model
    qa_pipeline = pipeline("question-answering", model=model_name, tokenizer=model_name)

    def qa_function(question, context):
        response = qa_pipeline(question=question, context=context)
        return response['answer'], response['score']

    memory = ConversationBufferMemory(
        memory_key='chat_history',
        return_messages=True,
        output_key='answer'
    )

    def conversation_chain(inputs):
        question = inputs['question']
        # Extract text content from Document objects
        documents = vectorstore.similarity_search(question, k=5)

        # If no similar documents are found or similarity is too low
        if not documents:
            answer = "Sorry, I couldn't find relevant information in the document. Please ask a question related to the document."
            memory.save_context({"user_input": question}, {"answer": answer})
            return {"chat_history": memory.chat_memory.messages, "answer": answer}

        context = "\n".join([doc.page_content for doc in documents])  # Extract `page_content` from each Document
        answer, score = qa_function(question, context)

        # Define a threshold for confidence (e.g., 0.5)
        if score < 0.5:
            answer = "Sorry, I couldn't find relevant information in the document. Please ask a question related to the document."

        memory.save_context({"user_input": question}, {"answer": answer})
        return {"chat_history": memory.chat_memory.messages, "answer": answer}

    return conversation_chain

# Generating response from user queries and displaying them accordingly
def handle_question(question):
    response = st.session_state.conversation({'question': question})
    st.session_state.chat_history = response["chat_history"]
    for i, msg in enumerate(st.session_state.chat_history):
        if i % 2 == 0:
            st.write(user_template.replace("{{MSG}}", msg.content), unsafe_allow_html=True)
        else:
            st.write(bot_template.replace("{{MSG}}", msg.content), unsafe_allow_html=True)

def main():
    st.set_page_config(page_title="Chat with Notes and AI", page_icon=":books:", layout="wide")
    st.write(css, unsafe_allow_html=True)

    if "conversation" not in st.session_state:
        st.session_state.conversation = None

    if "chat_history" not in st.session_state:
        st.session_state.chat_history = None

    st.header("CSS Edge - Intelligent Document Chatbot with Notes :books:")

    # Subject selection dropdown
    subjects = [
        "A Trumped World", "Agri Tax in Punjab", "Assad's Fall in Syria", "Elusive National Unity", "Europe and Trump 2.0",
        "Going Down with Democracy", "Indonesia's Pancasila Philosophy", "Pakistan in Choppy Waters",
        "Pakistan's Semiconductor Ambitions", "Preserving Pakistan's Cultural Heritage", "Tackling Informal Economy",
        "Technical Education in Pakistan", "The Case for Solidarity Levies", "The Decline of the Sole Superpower",
        "The Power of Big Oil", "Trump 2.0 and Pakistan's Emerging Foreign Policy", "Trump and the World 2.0",
        "Trump vs BRICS", "US-China Trade War", "War on Humanity", "Women's Suppression in Afghanistan"
    ]
    data_folder = "data"
    preview_folder = "Preview"
    subject_folders = {subject: os.path.join(data_folder, subject.replace(' ', '_')) for subject in subjects}
    preview_folders = {subject: os.path.join(preview_folder, subject.replace(' ', '_')) for subject in subjects}
    selected_subject = st.sidebar.selectbox("Select a Subject:", subjects)

    st.sidebar.info(f"You have selected: {selected_subject}")  # Display selected subject

    # Option to upload documents or use preloaded subject data
    use_preloaded = st.sidebar.radio("Select Data Source:", ("Use Preloaded Notes", "Upload Your Documents"))

    if use_preloaded == "Use Preloaded Notes":
        # Load preview content
        preview_folder_path = preview_folders[selected_subject]
        if os.path.exists(preview_folder_path):
            preview_text = get_text_files_content(preview_folder_path)
            st.subheader("Preview of Notes")
            st.text_area("Preview Content:", preview_text, height=300, disabled=True)
        else:
            st.error(f"No preview available for {selected_subject}.")

        # Process data folder for question answering
        subject_folder_path = subject_folders[selected_subject]
        if os.path.exists(subject_folder_path):
            raw_text = get_text_files_content(subject_folder_path)
            if raw_text:
                text_chunks = get_chunks(raw_text)
                vectorstore = get_vectorstore(text_chunks)
                st.session_state.conversation = get_conversationchain(vectorstore)
            else:
                st.error("Could not load the content for question answering.")
        else:
            st.error(f"No data available for {selected_subject}.")

    else:  # Upload documents option
        docs = st.sidebar.file_uploader("Upload your text files here:", accept_multiple_files=True, type=['txt'])
        if docs:
            st.sidebar.info(f"Uploaded {len(docs)} file(s).")
            if st.sidebar.button("Process"):
                with st.spinner("Processing uploaded documents..."):
                    raw_text = "".join([doc.read().decode('utf-8') for doc in docs])
                    st.subheader("Uploaded Notes Preview")
                    st.text_area("Preview Content:", raw_text, height=300, disabled=True)
                    text_chunks = get_chunks(raw_text)
                    vectorstore = get_vectorstore(text_chunks)
                    st.session_state.conversation = get_conversationchain(vectorstore)

    # Chat interface
    question = st.text_input("Ask a question about your selected subject:")
    if question and st.session_state.conversation:
        st.write(f"**Subject:** {selected_subject}")  # Display subject before chat
        handle_question(question)
    elif question:
        st.warning("Please process a document before asking a question.")

if __name__ == '__main__':
    main()