app.py

__import__('pysqlite3')
import sys
sys.modules['sqlite3'] = sys.modules.pop('pysqlite3')

# DATABASES = {
#     'default': {
#         'ENGINE': 'django.db.backends.sqlite3',
#         'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
#     }
# }
import streamlit as st
from huggingface_hub import InferenceClient
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, ServiceContext, PromptTemplate
from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.core import StorageContext
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
import chromadb
from langchain.memory import ConversationBufferMemory
import pandas as pd
from langchain.schema import Document


# Set page config
st.set_page_config(page_title="MBAL Chatbot", page_icon="🛡️", layout="wide")

# Set your Hugging Face token here

HF_TOKEN = st.secrets["HF_TOKEN"]

@st.cache_resource
def init_chroma():
    persist_directory = "chroma_db"
    chroma_client = chromadb.PersistentClient(path=persist_directory)
    chroma_collection = chroma_client.get_or_create_collection("my_collection")
    return chroma_client, chroma_collection

@st.cache_resource
def init_vectorstore():
    persist_directory = "chroma_db"
    embeddings = HuggingFaceEmbeddings()
    vectorstore = Chroma(persist_directory=persist_directory, embedding_function=embeddings, collection_name="my_collection")
    return vectorstore
@st.cache_resource
def setup_vector():
    # Đọc dữ liệu từ file Excel
    df = pd.read_excel("chunk_metadata_template (1).xlsx")
    chunks = []

    # Tạo danh sách các Document có metadata
    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)

    # Khởi tạo embedding
    embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')

    # Khởi tạo hoặc ghi vào vectorstore đã tồn tại
    persist_directory = "chroma_db"
    collection_name = "my_collection"

    # Tạo vectorstore từ dữ liệu và ghi vào Chroma
    vectorstore = Chroma.from_documents(
        documents=chunks,
        embedding=embeddings,
        persist_directory=persist_directory,
        collection_name=collection_name
    )
    
    # Ghi xuống đĩa để đảm bảo dữ liệu được lưu
    vectorstore.persist()

    return vectorstore

# Initialize components
client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3", token=HF_TOKEN)
chroma_client, chroma_collection = init_chroma()
init_vectorstore()
vectorstore = setup_vector()

# Initialize memory buffer
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)

def rag_query(query):
    # Lấy tài liệu liên quan
    retrieved_docs = vectorstore.similarity_search(query, k=5)
    context = "\n".join([doc.page_content for doc in retrieved_docs]) if retrieved_docs else ""

    # Lấy tương tác cũ
    past_interactions = memory.load_memory_variables({})[memory.memory_key]
    context_with_memory = f"{context}\n\nConversation History:\n{past_interactions}"

    # Chuẩn bị prompt
    messages = [
        {
            "role": "user",
            "content": f"""You are a consultant advising clients on insurance products from MB Ageas Life in Vietnam. Please respond professionally and accurately, and suggest suitable products by asking a few questions about the customer's needs. All information provided must remain within the scope of MBAL. Invite the customer to register for a more detailed consultation at https://www.mbageas.life/
{context_with_memory}
Question: {query}
Answer:"""
        }
    ]

    response_content = client.chat_completion(messages=messages, max_tokens=1024, stream=False)
    response = response_content.choices[0].message.content.split("Answer:")[-1].strip()
    return response


def process_feedback(query, response, feedback):
   # st.write(f"Feedback received: {'👍' if feedback else '👎'} for query: {query}")
    if feedback:
        # If thumbs up, store the response in memory buffer
        memory.chat_memory.add_ai_message(response)
    else:
        # If thumbs down, remove the response from memory buffer and regenerate the response
       # memory.chat_memory.messages = [msg for msg in memory.chat_memory.messages if msg.get("content") != response]
        new_query=f"{query}. Tạo câu trả lời đúng với câu hỏi"
        new_response = rag_query(new_query)
        st.markdown(new_response)
        memory.chat_memory.add_ai_message(new_response)

# Streamlit interface

st.title("Chào mừng bạn đã đến với MBAL Chatbot")
st.markdown("***")
st.info('''
        Tôi sẽ giải đáp các thắc mắc của bạn liên quan đến các sản phẩm bảo hiểm nhân thọ của MB Ageas Life''')

col1, col2 = st.columns(2)

with col1:
    chat = st.button("Chat")
    if chat:
        st.switch_page("pages/chatbot.py")

with col2:
    rag = st.button("Store Document")
    if rag:
        st.switch_page("pages/management.py")

st.markdown("<div style='text-align:center;'></div>", unsafe_allow_html=True)