import subprocess

# Instalar un paquete utilizando pip desde Python
subprocess.check_call(["pip", "install", "langchain_community","langchain"])

import streamlit as st 
from langchain.chains import LLMChain
from langchain.chains import ConversationChain
#importacipones adicionales 
import streamlit as st
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationChain
from langchain.prompts import PromptTemplate

from langchain.chains.conversation.memory import ConversationEntityMemory
from langchain.chains.conversation.prompt import ENTITY_MEMORY_CONVERSATION_TEMPLATE
from langchain_community.llms import HuggingFaceEndpoint
import os



st.set_page_config(page_title= "bot", layout="wide")

#Interfaz
st.title("Maqueta")



#template




#llm

llm = HuggingFaceEndpoint(repo_id='mistralai/Mistral-7B-v0.1', 
                          max_length=128, 
                          temperature=0.5,
                          higgingfacehub_api_token = os.environ["HUGGINGFACEHUB_API_TOKEN"])

#memory



conversation_buf = ConversationChain(llm = llm,
                      memory = ConversationBufferMemory(),
                      verbose = True)



if "generated" not in st.session_state:
    st.session_state["generated"] = []
if "past" not in st.session_state:
    st.session_state["past"] = []
if "input" not in st.session_state:
    st.session_state["input"] = ""
if "stored_session" not in st.session_state:
    st.session_state["stored_session"] = []


def get_text():
    """
    Get the user input text.

    Returns:
        (str): The text entered by the user
    """
    input_text = st.text_input("You: ", st.session_state["input"], key="input",
                            placeholder="Your AI assistant here! Ask me anything ...", 
                            label_visibility='hidden')
    return input_text

user_input = get_text()

if 'entity memory' not in st.session_state:
    st.session_state.entity_memory = ConversationEntityMemory(llm = llm,k=10)

Conversation = ConversationChain(llm = llm, 
                                 prompt= ENTITY_MEMORY_CONVERSATION_TEMPLATE,
                                 memory = st.session_state.entity_memory)




submit = st.button("Generate")
while submit:
    output = Conversation.run(input=user_input)  
    st.session_state.past.append(user_input)  
    st.session_state.generated.append(output)