from langchain.vectorstores import FAISS
# from langchain.chains import ConversationalRetrievalChain
from langchain.chains import RetrievalQA
from langchain.llms import HuggingFaceHub
import gradio as gr
import os
from langchain.embeddings import HuggingFaceEmbeddings
from langchain_experimental.agents.agent_toolkits.csv.base import create_csv_agent
from langchain.document_loaders import PyPDFDirectoryLoader
from langchain.document_loaders.csv_loader import CSVLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.memory import ConversationSummaryBufferMemory
import io
import contextlib


embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L12-v2")
vector_store= FAISS.load_local("vector_db/", embeddings)

repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1"
llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={"temperature": 0.01, "max_new_tokens": 2048})

# memory = ConversationSummaryBufferMemory(
#     llm=llm,
#     output_key='answer',
#     memory_key='chat_history',
#     max_token_limit=300,
#     return_messages=True)

# retriever = vector_store.as_retriever(
#     search_type="similarity",
#     search_kwargs={"k": 10, "include_metadata": True})

# qa = ConversationalRetrievalChain.from_llm(
#     llm=llm,
#     memory=memory,
#     chain_type="stuff",
#     retriever=retriever,
#     return_source_documents=True,
#     get_chat_history=lambda h : h,
#     verbose=True)

agent=create_csv_agent(llm,['data/Gretel_Data.csv','data/RAN_Data _T.csv'],verbose=True)

def echo(message, history):
  try:
    qa=RetrievalQA.from_chain_type(llm=llm, retriever=retriever,return_source_documents=True)
    message= "Your name is Clara. You are a senior telecom network engineer having access to troubleshooting tickets data and other technical and product documentation.Stick to the knowledge from these tickets. Ask clarification questions if needed.  "+message
    result=qa({"query":message})
    bold_answer= "<b>" + result['result'] + "</b>"
    return bold_answer + "<br></br>" +'1. ' + str(result["source_documents"][0]) +"<br>" + '2. ' + str(result["source_documents"][1]) +  "<br>" + "3. " + str(result["source_documents"][2])
  except Exception as e:
    error_message = f"An error occurred: {e}"+str(e.with_traceback) + str(e.args)

def echo_agent(message, history):
  message="There are 2 df's. If you find a KeyError check for the same in the other df." + "<br>" + message
  try:
    with io.StringIO() as buffer:
      with contextlib.redirect_stdout(buffer):
        result= agent.run(message)
      verbose_output = buffer.getvalue()
      verbose_output = verbose_output.replace("\x1b[36;1m\x1b[1;3m", "")
      verbose_output = verbose_output.replace("> ", "")
      verbose_output = verbose_output.replace("", "")
      verbose_output = verbose_output.replace("", "")
      result= "<b>" + verbose_output + "<br>" + result + "</b>"
    return result
  except Exception as e:
    error_message = f"An error occurred: {e}"+str(e.with_traceback) + str(e.args)
    return error_message

demo=gr.ChatInterface(
        fn=echo,
        chatbot=gr.Chatbot(height=300, label="Hi I am Clara!", show_label=True),
        textbox=gr.Textbox(placeholder="Ask me a question", container=True, autofocus=True, scale=7),
        title="Network Ticket Knowledge Management",
        description="<span style='font-size: 16x;'>Welcome to Verizon Network Operations Center!! I am here to help the Verizon Field Operations team with technical queries & escalation. I am trained on 1000s of RAN, Backhaul, Core network & End user equipment trouble tickets. Ask me!!!&nbsp;☺</span>",
        theme=gr.themes.Soft(),
        examples=["wifi connected but no internet showing", "internet stopped working after primary link down", "internet stopped working link not shifted to secondary after primary link down"],
        cache_examples=False,
        retry_btn=None,
        undo_btn="Delete Previous",
        clear_btn="Clear",
        stop_btn="Stop",
    )


demo1=gr.ChatInterface(
        fn=echo_agent,
        chatbot=gr.Chatbot(height=300, label="Hi I am Sam!", show_label=True),
        textbox=gr.Textbox(placeholder="Ask me a question", container=True, autofocus=True, scale=7),
        title="LLM Powered Agent",
        description="<span style='font-size: 16x;'>Welcome to Verizon RAN Visualization & Analytics powered by GEN AI. I have access 100 of metrices generated by a RAN base station and can help in visualizing, correlating and generating insights, using power of Conversational AI &nbsp;☺</span>",
        theme=gr.themes.Soft(),
        retry_btn=None,
        undo_btn="Delete Previous",
        clear_btn="Clear",
        stop_btn="Stop",
    )
demo2=gr.TabbedInterface([demo,demo1],["RAG","AGENT"], title='INCEDO', theme=gr.themes.Soft())
demo2.launch(auth=("admin", "Sam&Clara"))