app.py

import os
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.document_transformers import BeautifulSoupTransformer
import streamlit as st
from langchain_huggingface import HuggingFaceEndpoint
from langchain.indexes import VectorstoreIndexCreator
from langchain_huggingface.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import PromptTemplate
from langchain.chains import RetrievalQA

model_id="mistralai/Mistral-7B-Instruct-v0.3"

def get_llm_hf_inference(model_id=model_id, max_new_tokens=128, temperature=0.1):
    """
    Returns a language model for HuggingFace inference.

    Parameters:
    - model_id (str): The ID of the HuggingFace model repository.
    - max_new_tokens (int): The maximum number of new tokens to generate.
    - temperature (float): The temperature for sampling from the model.

    Returns:
    - llm (HuggingFaceEndpoint): The language model for HuggingFace inference.
    """
    llm = HuggingFaceEndpoint(
        repo_id=model_id,
        max_new_tokens=max_new_tokens,
        temperature=temperature,
        token = os.getenv("HF_TOKEN")
    )
    return llm


st.set_page_config(page_title="Website Information Retirever Agent", page_icon="🤗")
st.title("Website Information Retriever Agent")
st.markdown(f"*This is a simple chatbot that uses the HuggingFace transformers library to generate responses to your text input.It uses the model mistralai/Mistral-7B-Instruct-v0.3. You can enter the specific website url and the use the agent to gather information.*")

# Initialize session state for avatars
if "avatars" not in st.session_state:
    st.session_state.avatars = {'user': None, 'assistant': None}

# Initialize session state for user text input
if 'user_text' not in st.session_state:
    st.session_state.user_text = None

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

# Initialize session state for model parameters
if "max_response_length" not in st.session_state:
    st.session_state.max_response_length = 256

if "system_message" not in st.session_state:
    st.session_state.system_message = "friendly AI conversing with a human user"

if "starter_message" not in st.session_state:
    st.session_state.starter_message = "Hello, there! How can I help you today?"



    
# Sidebar for settings
with st.sidebar:
    st.header("System Settings")

    # AI Settings
    st.session_state.system_message = st.text_area(
        "System Message", value="You are a friendly AI conversing with a human user."
    )
    st.session_state.starter_message = st.text_area(
        'First AI Message', value="Hello, there! How can I help you today?"
    )

    # Model Settings
    st.session_state.max_response_length = st.number_input(
        "Max Response Length", value=256
    )

    # Avatar Selection
    st.markdown("*Select Avatars:*")
    col1, col2 = st.columns(2)
    with col1:
        st.session_state.avatars['assistant'] = st.selectbox(
            "AI Avatar", options=["🤗", "💬", "🤖"], index=0
        )
    with col2:
        st.session_state.avatars['user'] = st.selectbox(
            "User Avatar", options=["👤", "👱‍♂️", "👨🏾", "👩", "👧🏾"], index=0
        )
    # Reset Chat History
    reset_history = st.button("Reset Chat History")
    
# Initialize or reset chat history
if "chat_history" not in st.session_state or reset_history:
    st.session_state.chat_history = [{"role": "assistant", "content": st.session_state.starter_message}]

if "sitemap_url" in st.session_state:
    sitemap_url = st.text_input("URL to the website", value="")   

if sitemap_url:
    with st.spinner("Processing..."):
        token = os.getenv("HF_TOKEN")
        loader = WebBaseLoader([sitemap_url])
        html = loader.load()

    # Transform
    # bs_transformer = BeautifulSoupTransformer()
    # docs_transformed = bs_transformer.transform_documents(html,tags_to_extract=["span"])


        text_splitter = RecursiveCharacterTextSplitter(
            chunk_size=1000,
            chunk_overlap=10,
            add_start_index=True,
            strip_whitespace=True,
            separators=["\n\n", "\n", ".", " ", ""],
        )
        docs_processed = text_splitter.split_documents(html)


    # # Create a vector store based on the crawled data
    # index = VectorstoreIndexCreator().from_loaders([docs_processed])


        embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
        db = FAISS.from_documents(docs_processed, embeddings)
        retriever = db.as_retriever(search_kwargs={"k": 4})






def get_response(system_message, chat_history, user_text, 
                 eos_token_id=['User'], max_new_tokens=256, get_llm_hf_kws={}):
    """
    Generates a response from the chatbot model.

    Args:
        system_message (str): The system message for the conversation.
        chat_history (list): The list of previous chat messages.
        user_text (str): The user's input text.
        model_id (str, optional): The ID of the HuggingFace model to use.
        eos_token_id (list, optional): The list of end-of-sentence token IDs.
        max_new_tokens (int, optional): The maximum number of new tokens to generate.
        get_llm_hf_kws (dict, optional): Additional keyword arguments for the get_llm_hf function.

    Returns:
        tuple: A tuple containing the generated response and the updated chat history.
    """
    # Set up the model
    hf = get_llm_hf_inference(max_new_tokens=max_new_tokens, temperature=0.1)

    # Create the prompt template
    prompt = PromptTemplate.from_template(
        (
            "[INST] {system_message}"
            "\nCurrent Conversation:\n{chat_history}\n\n"
            "\nUser: {user_text}.\n [/INST]"
            "\nAI:"
        )
    )
    # Make the chain and bind the prompt
    chat = prompt | hf.bind(skip_prompt=True) | StrOutputParser(output_key='content')
    qa = RetrievalQA.from_chain_type(llm=hf, chain_type="refine", retriever=retriever, return_source_documents=False)
    # Generate the response

    response  = qa.run({"query": user_text})
    # response = chat.invoke(input=dict(system_message=system_message, user_text=user_text, chat_history=chat_history))
    # response = response.split("AI:")[-1]
    # Update the chat history
    chat_history.append({'role': 'user', 'content': user_text})
    chat_history.append({'role': 'assistant', 'content': response})
    return response, chat_history




    




# Chat interface

if sitemap_url:
    chat_interface = st.container(border=True)
    with chat_interface:
        output_container = st.container()
        st.session_state.user_text = st.chat_input(placeholder="Enter your text here.")
        
    # Display chat messages
    with output_container:
        # For every message in the history
        for message in st.session_state.chat_history:
            # Skip the system message
            if message['role'] == 'system':
                continue
                
            # Display the chat message using the correct avatar
            with st.chat_message(message['role'], 
                                avatar=st.session_state['avatars'][message['role']]):
                st.markdown(message['content'])
                
 # When the user enter new text:
    if st.session_state.user_text:
        
        # Display the user's new message immediately
        with st.chat_message("user", 
                             avatar=st.session_state.avatars['user']):
            st.markdown(st.session_state.user_text)
            
        # Display a spinner status bar while waiting for the response
        with st.chat_message("assistant", 
                             avatar=st.session_state.avatars['assistant']):

            with st.spinner("Thinking..."):
                # Call the Inference API with the system_prompt, user text, and history
                
               
                response, st.session_state.chat_history = get_response(
                    system_message=st.session_state.system_message, 
                    user_text=st.session_state.user_text,
                    chat_history=st.session_state.chat_history,
                    max_new_tokens=st.session_state.max_response_length,
                )
                st.markdown(response)