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# 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
#token de hugging face
os.environ['HUGGINGFACEHUB_API_TOKEN'] = "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"
# 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='meta-llama/Llama-2-7b-chat-hf',
max_length=128,
temperature=0.5,
token="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")
# 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
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