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import logging
import os
import streamlit as st
import torch
import sys
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings, StorageContext, load_index_from_storage
from llama_index.core.chat_engine.types import ChatMode
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.llms.openai_like import OpenAILike
API_BASE_URL="https://chatapi.akash.network/api/v1"
EMBEDDING_MODEL="sentence-transformers/paraphrase-MiniLM-L6-v2" #Modèle le plus rapide
LLM_MODEL="DeepSeek-R1-Distill-Llama-70B"
PERSIST_DIR = "./storage"
# Ajuster le chemin de torch.classes pour éviter le conflit
torch.classes.__path__ = []
st.set_page_config(page_title="Votre expert en règles de marché RTE", layout="centered", initial_sidebar_state="auto", menu_items=None)
st.title("Votre expert en règles de marché RTE")
custom_llm = OpenAILike(api_base=API_BASE_URL, model=LLM_MODEL, api_key=st.secrets["openai_key"], max_tokens=1500, temperature=0.2)
Settings.embed_model = HuggingFaceEmbedding(model_name=EMBEDDING_MODEL)
Settings.llm=custom_llm
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
# Load and index data
@st.cache_resource
def load_data():
if not os.path.exists(PERSIST_DIR):
documents = SimpleDirectoryReader(input_dir="./data").load_data()
document_index = VectorStoreIndex.from_documents(documents)
document_index.storage_context.persist(persist_dir=PERSIST_DIR)
else:
storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
document_index = load_index_from_storage(storage_context)
return document_index
index = load_data()
if "messages" not in st.session_state.keys(): # Initialize the chat messages history
st.session_state.messages = [
{
"role": "assistant",
"content": "Posez-moi une question sur les règles de marché RTE :\nhttps://www.services-rte.com/fr/actualites/nouvelles-versions-des-r%C3%A8gles-de-march%C3%A9-applicables-au-01-avril-2024.html",
}
]
if "chat_engine" not in st.session_state.keys(): # Initialize the chat engine
st.session_state.chat_engine = index.as_chat_engine(chat_mode=ChatMode.CONTEXT, similarity_top_k=5, verbose=True, streaming=True)
if prompt := st.chat_input("Posez votre question"): # Prompt for user input and save to chat history
st.session_state.messages.append({"role": "user", "content": prompt})
for message in st.session_state.messages: # Write message history to UI
with st.chat_message(message["role"]):
st.write(message["content"])
# If last message is not from assistant, generate a new response
if st.session_state.messages[-1]["role"] != "assistant":
with st.chat_message("assistant"):
response_stream = st.session_state.chat_engine.stream_chat(prompt)
st.write_stream(response_stream.response_gen)
message = {"role": "assistant", "content": response_stream.response}
# Add response to message history
st.session_state.messages.append(message)
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