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 # 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 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 ConversationChain object with the specified configuration Conversation = ConversationChain(llm=llm, prompt=ENTITY_MEMORY_CONVERSATION_TEMPLATE, memory=st.session_state.entity_memory ) # 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