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import streamlit as st
# from streamlit_chat import message
from langchain.chains import ConversationalRetrievalChain
from langchain.document_loaders import PyPDFLoader, DirectoryLoader
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.llms import CTransformers
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.memory import ConversationBufferMemory
import streamlit.components.v1 as components
from templatesStreamlit import *
import tempfile
import os
# Funcion para leer los documentos
def load_documents(uploaded_files):
docs = []
temp_dir = tempfile.TemporaryDirectory()
for file in uploaded_files:
temp_filepath = os.path.join(temp_dir.name, file.name)
with open(temp_filepath, "wb") as f:
f.write(file.getvalue())
loader = PyPDFLoader(temp_filepath)
docs.extend(loader.load())
# loader = DirectoryLoader('data/', glob="*.pdf", loader_cls=PyPDFLoader)
# documents = loader.load()
return docs
# Funcion para convertir el texto en chunks
def split_text_into_chunks(documents):
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
text_chunks = text_splitter.split_documents(documents)
return text_chunks
def get_vectorstore(text_chunks):
embbedings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={'device': "cpu"})
vector_store = FAISS.from_documents(text_chunks, embbedings)
return vector_store
# def create_llms_model():
# llm = CTransformers(model="mistral-7b-instruct-v0.1.Q4_K_M.gguf", config={'max_new_tokens': 512, 'temperature': 0.01})
# return llm
def get_conversation_chain(vector_store):
llm = CTransformers(model="mistral-7b-instruct-v0.1.Q4_K_M.gguf", config={'max_new_tokens': 512, 'temperature': 0.01})
#Creamos la memoria
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
# Create chain (lANGCHAIN)
conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, chain_type='stuff',
retriever=vector_store.as_retriever(search_kwargs={"k": 2}),
memory=memory)
return conversation_chain
def handle_userinput(user_question):
response = st.session_state.conversation({'question': user_question})
st.session_state.chat_history = response['chat_history']
for i, message in enumerate(st.session_state.chat_history):
if i % 2 == 0:
st.write(user_template2.replace(
"{{MSG}}", message.content), unsafe_allow_html=True)
else:
st.write(bot_template2.replace(
"{{MSG}}", message.content), unsafe_allow_html=True)
def main():
st.set_page_config(page_title="LLM-RAG",
page_icon=":books:")
st.write(css, unsafe_allow_html=True)
titulo = f"""
<div class="btn-neon">
<span class="icon"><img src=static/Mistral.png></span>
Mistral7b + Streamlit
<span class="icon"><img src=static/streamlit.png></span>
</div>
"""
st.markdown(titulo, unsafe_allow_html=True)
presentacion = f"""
<div class="skill">
<div class="skill-content">
<div class="skill-img-box">
<a href="https://www.linkedin.com/in/manueloteromarquez/" target="_blank">
<img src="https://media.licdn.com/dms/image/C4D03AQEsabRcMGkMmQ/profile-displayphoto-shrink_800_800/0/1663585925916?e=1708560000&v=beta&t=1Ofx1PsbTSlMcNIVCxznEjtIA_aIlTVaJm52toMKddU" alt="Tu descripción">
</a>
</div>
<div class="skill-detail">
<h2 class="skill-title">Manuel Otero</h2>
<p>211 Days</p>
<div class="skill-progress">
<div class="progress progress-1"></div>
</div>
</div>
</div>
<h2 class="percent">60%</h2>
</div>
"""
st.markdown(presentacion, unsafe_allow_html=True)
if "conversation" not in st.session_state:
st.session_state.conversation = None
if "chat_history" not in st.session_state:
st.session_state.chat_history = None
st.header("Hazle preguntas a tus documentos PDFs :books:")
with st.sidebar:
st.subheader("Tus Documentos")
pdf_docs = st.file_uploader(
"Sube tus PDFs aquí y pulsa 'Procesar PDF'", accept_multiple_files=True)
if not pdf_docs:
st.info("Sube tus pdfs para continuar.")
st.stop()
if st.button("Procesar PDF"):
with st.spinner("Procesando"):
# get pdf text
documents = load_documents(pdf_docs)
print(documents)
text_chunks = split_text_into_chunks(documents)
# create vector store
vectorstore = get_vectorstore(text_chunks)
# create conversation chain
st.session_state.conversation = get_conversation_chain(vectorstore)
user_question = st.text_input("Adelante pregunta")
if user_question:
handle_userinput(user_question)
if __name__ == '__main__':
main() |