apppp.py

import streamlit as st
from langchain.llms import HuggingFacePipeline
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain.prompts.prompt import PromptTemplate
from langchain.embeddings import HuggingFaceEmbeddings, OpenAIEmbeddings
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from langchain.schema import Document
from langchain_community.llms import HuggingFaceEndpoint
from langchain.vectorstores import Chroma
from transformers import TextStreamer
from langchain.llms import HuggingFacePipeline
from langchain.prompts import ChatPromptTemplate
from langchain.llms import HuggingFaceHub
import os
import pandas as pd
from langchain.vectorstores import FAISS
import subprocess
from langchain_community.llms import HuggingFaceHub
from langchain_core.runnables import RunnablePassthrough # Use for testing (make 'example' easy to execute and experiment with)
from langchain_core.output_parsers import StrOutputParser # format LLM's output text into (list, dict or any custom structure we can work with)
import pandas as pd

# Configuración del modelo
MODEL_NAME = "mistralai/Mixtral-8x7B-Instruct-v0.1"
model_name = "google/gemma-2-2b"
TOKEN=os.getenv('gemma2')
subprocess.run(["huggingface-cli", "login", "--token", TOKEN, "--add-to-git-credential"])
######
# set this key as an environment variable
os.environ["HUGGINGFACEHUB_API_TOKEN"] = st.secrets['gemma2']


# Initialize tokenizer
@st.cache_resource
def load_model():
    # OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
    # client = OpenAI(api_key=OPENAI_API_KEY)


    # TOKEN = os.environ.get('gemma2')
    # subprocess.run(["huggingface-cli", "login", "--token", TOKEN, "--add-to-git-credential"])
    # local_llm = HuggingFaceHub(repo_id=MODEL_NAME, model_kwargs={"temperature":0.5, "max_length":512}, huggingfacehub_api_token = TOKEN)
    # model = AutoModelForCausalLM.from_pretrained(model_name)
    # tokenizer = AutoTokenizer.from_pretrained(model_name)
    # from transformers import pipeline
    # streamer = TextStreamer(tokenizer)
    
    # text_generation_pipeline = pipeline(
    #     # model=model,
    #     # tokenizer=tokenizer,
    #     # task="text-generation",
    #     # do_sample=True,
    #     # temperature=0.3,
    #     # top_p=0.9,
    #     # max_new_tokens=512,
    #     # repetition_penalty=1.2,
    #     # return_full_text=False,
    #     # streamer=streamer
    # )
    # local_llm = HuggingFacePipeline(pipeline=text_generation_pipeline)
    local_llm = HuggingFaceHub(
            repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
            #repo_id="TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF"
            model_kwargs={"temperature": 0.5, "max_length": 1048})
    return local_llm

# Initialize embeddings
@st.cache_resource
def load_embeddings():
    embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
    # embeddings = OpenAIEmbeddings()
    return embeddings

# Chroma Vector store


# Set up chain
def setup_conversation_chain():
    llm = load_model()
    chunks = []

    df = pd.read_excel(r"chunk_metadata_template.xlsx")
    for _, row in df.iterrows():
        chunk_with_metadata = Document(
            page_content=row['page_content'],
            metadata={
                'chunk_id': row['chunk_id'],
                'document_title': row['document_title'],
                'topic': row['topic'],
                'access': row['access']
            }
        )
        chunks.append(chunk_with_metadata)
    embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
    vector = Chroma.from_documents(chunks, embedding=embeddings)
    # memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)

    template = """Bạn là một chuyên viên tư vấn cho khách hàng về sản phẩm bảo hiểm của công ty MB Ageas Life tại Việt Nam.
    Hãy trả lời  chuyên nghiệp, chính xác, cung cấp thông tin trước rồi hỏi câu tiếp theo. Tất cả các thông tin cung cấp đều trong phạm vi MBAL. Khi có đủ thông tin khách hàng thì mới mời khách hàng đăng ký để nhận tư vấn trên https://www.mbageas.life/
        {context}
        Câu hỏi: {question}
        Trả lời:"""
    parser = StrOutputParser()
    # PROMPT = PromptTemplate(
    #     template=template
    #     , input_variables=["context","question"]
    # )
    # PROMPT = ChatPromptTemplate.from_template(template=template)
    # chain = ConversationalRetrievalChain.from_llm(
    #     llm=llm,
    #     retriever=vector.as_retriever(search_kwargs={'k': 5}),
    #     memory=memory,
    #     combine_docs_chain_kwargs={"prompt": PROMPT}
    #     # condense_question_prompt=CUSTOM_QUESTION_PROMPT
    # )
    def format_docs(docs= chunks):
        return "\n\n".join(doc.page_content for doc in docs)
    chain = (
    {"context": vector.as_retriever(search_kwargs={'k': 5}) | format_docs, "question": RunnablePassthrough()}
    | template
    | llm
    | parser
    )
    
    return chain

# Streamlit
def main():
    st.title("🛡️ MBAL Chatbot 🛡️")

    # Inicializar la cadena de conversación
    if 'conversation_chain' not in st.session_state:
        st.session_state.conversation_chain = setup_conversation_chain()

    # Mostrar mensajes del chat
    if 'messages' not in st.session_state:
        st.session_state.messages = []

    for message in st.session_state.messages:
        with st.chat_message(message["role"]):
            st.markdown(message["content"])

    # Campo de entrada para el usuario
    if prompt := st.chat_input("Bạn cần tư vấn về điều gì? Hãy chia sẻ nhu cầu và thông tin của bạn nhé!"):
        st.session_state.messages.append({"role": "user", "content": prompt})
        with st.chat_message("user"):
            st.markdown(prompt)

        with st.chat_message("assistant"):
            message_placeholder = st.empty()
            full_response = ""
            
            # Generar respuesta
            response = st.session_state.conversation_chain({"question": prompt, "chat_history": []})
            full_response = response['answer']
            # full_response = response.get("answer", "No response generated.")
            
            message_placeholder.markdown(full_response)
        
        st.session_state.messages.append({"role": "assistant", "content": full_response})

# if __name__ == "__main__":
main()