import gradio as gr
from langchain.chat_models import ChatOpenAI
from langchain.llms import OpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.memory import ConversationSummaryMemory
from langchain.chains import ConversationalRetrievalChain
from dotenv import load_dotenv, find_dotenv

_ = load_dotenv(find_dotenv()) # read local .env file

# Read VectorStore
vectorstore = Chroma(
    embedding_function=OpenAIEmbeddings(),
    persist_directory="chroma_db"
    )

# Memory
llm_memory = OpenAI(temperature=0.0)
memory = ConversationSummaryMemory(
    llm=llm_memory, memory_key="chat_history", return_messages=True
)

# Chabot QA
llm_qa = ChatOpenAI(temperature=0.0)
retriever = vectorstore.as_retriever()
qa = ConversationalRetrievalChain.from_llm(
    llm_qa,
    retriever=retriever,
    memory=memory
    )


def chatbot(message, history):
    response = qa(message)
    return response["answer"]

iface = gr.ChatInterface(
    fn=chatbot,
    title="🤖 AI Bot for Help Center in CCC.uno 📞",
    examples = [
        "Quién eres?",
        "Qué es Press 3?",
        "Cómo puedo usar Secure Screen?",
        "Cómo puedo obtener reportes de IVR?",
        "Por que se queda procesando en Take Calls?",
        
    ],
    description="A chatbot for CCC.uno Support <br> Ask whatever you want related to CCC.uno <br> Knowledge base from: https://help.ccc.uno/es/"
)


if __name__ == "__main__":
    iface.launch()