test_2.py

import subprocess

import streamlit as st
from dotenv import load_dotenv
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.embeddings import FastEmbedEmbeddings  # General embeddings from HuggingFace models.
from langchain.memory import ConversationBufferMemory
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from htmlTemplates import css, bot_template, user_template
from langchain.llms import LlamaCpp  # For loading transformer models.
from langchain.document_loaders import PyPDFLoader, TextLoader, JSONLoader, CSVLoader
import tempfile 
from langchain.chains import RetrievalQA
from langchain.prompts import PromptTemplate
from langchain import hub
import os
import glob


# TEXT LOADERS
def get_pdf_text(pdf_docs):
    """
    Purpose: A hypothetical loader for PDF files in Python.
    Usage: Used to extract text or other information from PDF documents.
    Load Function: A load_pdf function might be used to read and extract data from a PDF file.

    input : pdf document path
    returns : extracted text 
    """
    temp_dir = tempfile.TemporaryDirectory() 
    temp_filepath = os.path.join(temp_dir.name, pdf_docs.name) 

    with open(temp_filepath, "wb") as f:  
        f.write(pdf_docs.getvalue()) 

    pdf_loader = PyPDFLoader(temp_filepath) 
    pdf_doc = pdf_loader.load() 
    return pdf_doc 


def get_text_file(text_docs):
    """
    """
    temp_dir = tempfile.TemporaryDirectory()
    temp_filepath = os.path.join(temp_dir.name, text_docs.name)
    
    with open(temp_filepath, "wb") as f:
        f.write(text_docs.getvalue())
        
    text_loader = TextLoader(temp_filepath)
    text_doc = text_loader.load()
    return text_doc 
    
def get_csv_file(csv_docs):
    temp_dir = tempfile.TemporaryDirectory()
    temp_filepath = os.path.join(temp_dir.name, csv_docs.name)
    
    with open(temp_filepath, "wb") as f:
        f.write(csv_docs.getvalue())
        
    csv_loader = CSVLoader(temp_filepath)
    csv_doc = csv_loader.load()
    return csv_doc
    

def get_json_file(json_docs):
    temp_dir = tempfile.TemporaryDirectory() 
    temp_filepath = os.path.join(temp_dir.name, json_docs.name) 
    with open(temp_filepath, "wb") as f:  
        f.write(json_docs.getvalue()) 
   
    json_loader = JSONLoader(
        file_path=temp_filepath,
        jq_schema='.messages[].content',
        text_content=False
    )   
    json_doc = json_loader.load()
    return json_doc


def get_text_chunks(documents):
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=512,  
        chunk_overlap=50,  
        length_function=len  
    )

    documents = text_splitter.split_documents(documents)  
    return documents  



def get_vectorstore(text_chunks, embeddings):
    
    vectorstore = Chroma.from_documents(documents= text_chunks, 
                               embedding= st.session_state.embeddings,
                               persist_directory= "./vectordb/")
    # Document stored 
    return vectorstore  

def get_conversation_chain(vectorstore):

    model_path = st.session_state.model
    callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])

    llm = LlamaCpp(model_path= model_path, 
                   n_ctx=4000, 
                   max_tokens= 500,
                   fp = 50, 
                   n_batch = 512,
                   callback_manager = callback_manager,
                   verbose=True)
    
    memory = ConversationBufferMemory(
        memory_key='chat_history', return_messages=True)
    
        # prompt template 📝
    template = """
    You are a Experience human Resource Manager. When the employee asks you a question, you will have to refer the company policy and respond in a professional way. Make sure to sound Empethetic while being professional and sound like a Human!
    Try to summarise the content and keep the answer to the point.
    If you don't know the answer, just say that you don't know, don't try to make up an answer.


    Context: {context}
    Question: {question}
    Answer:
    """

    rag_prompt_custom = PromptTemplate.from_template(template)

    # prompt = hub.pull("rlm/rag-prompt")
    
    conversation_chain = RetrievalQA.from_chain_type(
        llm,
        retriever=vectorstore.as_retriever(),
        chain_type_kwargs={"prompt": rag_prompt_custom},
    )
    conversation_chain.callback_manager = callback_manager
    conversation_chain.memory = ConversationBufferMemory()

    return conversation_chain


def handle_userinput():

    clear = False

    # Add clear chat button
    if st.button("Clear Chat history"):
        clear = True
        st.session_state.messages = []

    if "messages" not in st.session_state:
        st.session_state.messages = [{"role": "assistant", "content": "How can I help you?"}]

    for msg in st.session_state.messages:
        st.chat_message(msg["role"]).write(msg["content"])

    if prompt := st.chat_input():
        st.session_state.messages.append({"role": "user", "content": prompt})
        st.chat_message("user").write(prompt)
        if clear:
            st.session_state.conversation.clean()
        msg = st.session_state.conversation.run(prompt)
        print(msg)
        st.session_state.messages.append({"role": "assistant", "content": msg})
        st.chat_message("assistant").write(msg)

        

# Function to apply rounded edges using CSS
def add_rounded_edges(image_path="./randstad_featuredimage.png", radius=30):
    st.markdown(
        f'<style>.rounded-img{{border-radius: {radius}px; overflow: hidden;}}</style>',
        unsafe_allow_html=True,)
    st.image(image_path, use_column_width=True, output_format='auto')


def main():
    load_dotenv()
    st.set_page_config(page_title="Chat with multiple Files",
                       page_icon=":books:")
    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.title("💬 Randstad HR Chatbot")
    st.subheader("🚀 A HR powered by Generative AI")

    # default model 
    st.session_state.model = "./models/mistral-7b-instruct-v0.2.Q5_K_M.gguf"
    # user_question = st.text_input("Ask a question about your documents:")

    st.session_state.embeddings = FastEmbedEmbeddings( model_name= "BAAI/bge-small-en-v1.5", 
                                                                    cache_dir="./embedding_model/")

    if len(glob.glob("./vectordb/*.sqlite3")) > 0 :
        
        vectorstore = Chroma(persist_directory="./vectordb/", embedding_function=st.session_state.embeddings)
        st.session_state.conversation = get_conversation_chain(vectorstore)
        handle_userinput()

    with st.sidebar:

        # calling a 
        add_rounded_edges()

        st.subheader("Select Your Embedding Model Model")
        st.session_state.model = st.selectbox( 'Models', tuple( glob.glob('./models/*.gguf') ) )


        st.subheader("Your documents")
        docs = st.file_uploader(
            "Upload File (pdf,text,csv...) and click 'Process'", accept_multiple_files=True)
        if st.button("Process"):
            with st.spinner("Processing"):
                # get pdf text
                doc_list = []

                for file in docs:
                    print('file - type : ', file.type)
                    if file.type == 'text/plain':
                        # file is .txt
                        doc_list.extend(get_text_file(file))
                    elif file.type in ['application/octet-stream', 'application/pdf']:
                        # file is .pdf
                        doc_list.extend(get_pdf_text(file))
                    elif file.type == 'text/csv':
                        # file is .csv
                        doc_list.extend(get_csv_file(file))
                    elif file.type == 'application/json':
                        # file is .json
                        doc_list.extend(get_json_file(file))

                # get the text chunks
                text_chunks = get_text_chunks(doc_list)

                # create vector store
                vectorstore = get_vectorstore(text_chunks, st.session_state.embeddings)

                # create conversation chain
                st.session_state.conversation = get_conversation_chain(vectorstore)


if __name__ == '__main__':
    command = 'CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip install llama-cpp-python --no-cache-dir'
    
    # Run the command using subprocess
    try:
        subprocess.run(command, shell=True, check=True)
        print("Command executed successfully.")
    except subprocess.CalledProcessError as e:
        print(f"Error: {e}")
    main()