import streamlit as st 
# from langchain_community.llms import HuggingFaceTextGenInference
import os
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.schema import StrOutputParser

from custom_llm import CustomLLM, custom_chain_with_history, custom_dataframe_chain, format_df,custom_unique_df_chain
import pandas as pd


API_TOKEN = os.getenv('HF_INFER_API')


from typing import Optional

from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_community.chat_models import ChatAnthropic
from langchain_core.chat_history import BaseChatMessageHistory
from langchain.memory import ConversationBufferMemory
from langchain_core.runnables.history import RunnableWithMessageHistory



st.title("LMD Chatbot Tiket Ebesha Management")
st.subheader("Monthly Ticket Sample")


uploaded_files = st.file_uploader("Choose CSV or XLSX files", accept_multiple_files=True, type=["csv", "xlsx"])
df_temp = []
for uploaded_file in uploaded_files:
    if uploaded_file.name.split(".")[-1] != 'csv':
        a = pd.read_excel(uploaded_file)
        uploaded_file = uploaded_file.name.split(".")[0]+".csv"
        a.to_csv(uploaded_file, encoding="utf8", header=True, index=False)
        
    df_temp.append(pd.read_csv(uploaded_file))



if uploaded_files:

    df = pd.concat(df_temp)
    
    # if "df" not in st.session_state:
    #     st.session_state.df = pd.concat(df_temp)

    if 'unique_values' not in st.session_state:
        exec(custom_unique_df_chain(llm=CustomLLM(repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1", model_type='text-generation', api_token=API_TOKEN, stop=["\n<|","<|"]), df=df).invoke({"df_example":format_df(df.head(4))}))
        st.session_state.unique_values = response

    @st.cache_resource
    def get_llm_chain():
        # return custom_chain_with_history(llm=CustomLLM(repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1", model_type='text-generation', api_token=API_TOKEN, stop=["\n<|","<|"]), memory=st.session_state.memory)
        
        
        return custom_dataframe_chain(llm=CustomLLM(repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1", model_type='text-generation', api_token=API_TOKEN, stop=["\n<|","<|"]), df=df, unique_values=st.session_state.unique_values)
    
    if 'memory' not in st.session_state:
        st.session_state['memory'] = ConversationBufferMemory(return_messages=True)
        st.session_state.memory.chat_memory.add_ai_message("Hello there! I'm AI assistant of Lintas Media Danawa. How can I help you today?")
    
    if 'chain' not in st.session_state:
        # st.session_state['chain'] = custom_chain_with_history(llm=CustomLLM(repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1", model_type='text-generation', api_token=API_TOKEN, stop=["\n<|","<|"]), memory=st.session_state.memory)
        st.session_state['chain'] = get_llm_chain()
        # st.session_state['chain'] = custom_chain_with_history(llm=InferenceClient("https://api-inference.huggingface.co/models/mistralai/Mixtral-8x7B-Instruct-v0.1", headers = {"Authorization": f"Bearer {API_TOKEN}"}, stream=True, max_new_tokens=512, temperature=0.01), memory=st.session_state.memory)
    
        
    # Initialize chat history
    if "messages" not in st.session_state:
        st.session_state.messages = [{"role":"assistant", "content":"Hello there! I'm AI assistant of Lintas Media Danawa. How can I help you today?"}]
    
    # Display chat messages from history on app rerun
    for message in st.session_state.messages:
        with st.chat_message(message["role"]):
            st.markdown(message["content"])
    
    
    
    # React to user input
    if prompt := st.chat_input("Ask me anything.."):
        # Display user message in chat message container
        st.chat_message("User").markdown(prompt)
        # Add user message to chat history
        st.session_state.messages.append({"role": "User", "content": prompt})
        
        # full_response = st.session_state.chain.invoke(prompt).split("\n<|")[0]
        # full_response = st.session_state.chain.invoke({"question":prompt, "memory":st.session_state.memory}).split("\n<|")[0]

        # df = st.session_state.df
        full_response = st.session_state.chain.invoke({"question":prompt,"df_example":format_df(df.head(4))})
        print(len(full_response))
        exec(full_response)

        full_response = "Here is the python code: \n\n```python"+ full_response +"\n```\n\nGenerated Response: \n\n"+ str(response)
        
    
        with st.chat_message("assistant"):
            st.markdown(full_response)
    
        # Display assistant response in chat message container
        # with st.chat_message("assistant"):
        #     message_placeholder = st.empty()
        #     full_response = ""
        #     for chunk in st.session_state.chain.stream(prompt):
        #         full_response += chunk + " "
        #         message_placeholder.markdown(full_response + " ")
        #         if full_response[-4:] == "\n<|":
        #             break
            # st.markdown(full_response)
        # st.session_state.memory.save_context({"question":prompt}, {"output":full_response})
        # st.session_state.memory.chat_memory.messages = st.session_state.memory.chat_memory.messages[-15:]
        # # Add assistant response to chat history
        st.session_state.messages.append({"role": "assistant", "content": full_response})