app.py

import subprocess

# Instalar un paquete utilizando pip desde Python
subprocess.check_call(["pip", "install", "langchain_community","langchain"])
# Import necessary libraries
import streamlit as st
from langchain.chains import ConversationChain
from langchain.chains.conversation.memory import ConversationEntityMemory
from langchain.chains.conversation.prompt import ENTITY_MEMORY_CONVERSATION_TEMPLATE
import os
from getpass import getpass
from langchain import HuggingFaceHub
from langchain_community.llms import HuggingFaceEndpoint
from langchain_core.output_parsers import StrOutputParser
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain





# Set Streamlit page configuration
st.set_page_config(page_title='🧠MemoryBot🤖', layout='wide')
# Initialize session states. Un session state es como un diccionario
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"] = []

# Define function to get user input
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

# #parte para hacer un chat nuevo 
def new_chat():
    """
    Clears session state and starts a new chat.
    """
    save = []
    for i in range(len(st.session_state['generated'])-1, -1, -1):
        save.append("User:" + st.session_state["past"][i])
        save.append("Bot:" + st.session_state["generated"][i])        
    st.session_state["stored_session"].append(save)
    st.session_state["generated"] = []
    st.session_state["past"] = []
    st.session_state["input"] = ""
    st.session_state.entity_memory.entity_store = {}
    st.session_state.entity_memory.buffer.clear()

# Add a button to start a new chat
st.sidebar.button("New Chat", on_click = new_chat, type='primary')




# Move K outside of the sidebar expander
K = st.sidebar.number_input(' (#)Summary of prompts to consider', min_value=3, max_value=1000)

# Set up the Streamlit app layout
st.title("Personalized chatbot")

# Create prompt ( elige entre template_mario, template_poet, template_helper, template_chef, template_gym...)
prompt = PromptTemplate.from_template(os.environ["template_chef"])

#Cambia la referencia a esta variable en llm_chain SOLO si vas a usar la memoria 
prompt_memory = ENTITY_MEMORY_CONVERSATION_TEMPLATE


# Create an OpenAI instance
llm = HuggingFaceEndpoint(repo_id='mistralai/Mistral-7B-Instruct-v0.2', 
                          temperature=0.3, 
                          model_kwargs = {"max_length":128},
                          huggingfacehub_api_token = os.environ["HUGGINGFACEHUB_API_TOKEN"])




# Create a ConversationEntityMemory object if not already created
if 'entity_memory' not in st.session_state:
    st.session_state.entity_memory = ConversationEntityMemory(llm=llm, k=K )
    
# Create the LLMChain object with the specified configuration

llm_chain = LLMChain(
    llm = llm, 
    prompt= prompt_memory, #cambia esto a prompt si no vas a usar memoria 
    memory = st.session_state.entity_memory,
    output_parser=StrOutputParser()
)

# Get the user input
user_input = get_text()

# Generate the output using the ConversationChain object and the user input, and add the input/output to the session
if user_input:
    output = Conversation.run(input=user_input)  
    st.session_state.past.append(user_input)  
    st.session_state.generated.append(output)  


# Display the conversation history using an expander, and allow the user to download it
with st.expander("Conversation", expanded=True):
    for i in range(len(st.session_state['generated'])-1, -1, -1):
        st.info(st.session_state["past"][i],icon="🧐")
        st.success(st.session_state["generated"][i], icon="🤖")



# Display stored conversation sessions in the sidebar
for i, sublist in enumerate(st.session_state.stored_session):
        with st.sidebar.expander(label= f"Conversation-Session:{i}"):
            st.write(sublist)

# Allow the user to clear all stored conversation sessions
if st.session_state.stored_session:   
    if st.sidebar.checkbox("Clear-all"):
        del st.session_state.stored_session