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import streamlit as st
import faiss
from sentence_transformers import SentenceTransformer
import pickle
import re
from transformers import pipeline
import torch
st.set_page_config(page_title = "Vietnamese Legal Question Answering System", page_icon= "🐧", layout="centered", initial_sidebar_state="collapsed")
st.markdown(
"""
<h1 style="text-align: center;">Vietnamese Legal Question Answering System</h1>
""",
unsafe_allow_html=True
)
with open('articles.pkl', 'rb') as file:
articles = pickle.load(file)
index_loaded = faiss.read_index("sentence_embeddings_index_no_citation.faiss")
device = 0 if torch.cuda.is_available() else -1
if 'model_embedding' not in st.session_state:
st.session_state.model_embedding = SentenceTransformer('bkai-foundation-models/vietnamese-bi-encoder', device = device)
# Replace this with your own checkpoint
model_checkpoint = "model"
question_answerer = pipeline("question-answering", model=model_checkpoint, device = device)
def question_answering(question):
print(question)
query_sentence = [question]
query_embedding = st.session_state.model_embedding.encode(query_sentence)
k = 20
D, I = index_loaded.search(query_embedding.astype('float32'), k) # D is distances, I is indices
answer = [question_answerer(question=query_sentence[0], context=articles[I[0][i]], max_answer_len = 256) for i in range(k)]
best_answer = max(answer, key=lambda x: x['score'])
print(best_answer['answer'])
if best_answer['score'] > 0.5:
return best_answer['answer']
return f"Tôi không chắc lắm nhưng có lẽ câu trả lời là: \n{best_answer['answer']}"
if "messages" not in st.session_state:
st.session_state.messages = []
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
def clean_answer(s):
# Sử dụng regex để loại bỏ tất cả các ký tự đặc biệt ở cuối chuỗi
return re.sub(r'[^aAàÀảẢáÁạẠăĂằẰẳẲẵẴắẮặẶâÂầẦẩẨẫẪấẤậẬbBcCdDđĐeEèÈẻẺẽẼéÉẹẸêÊềỀểỂễỄếẾệỆfFgGhHiIìÌỉỈĩĨíÍịỊjJkKlLmMnNoOòÒỏỎõÕóÓọỌôÔồỒổỔỗỖốỐộỘơƠờỜởỞỡỠớỚợỢpPqQrRsStTuUùÙủỦũŨúÚụỤưƯừỪửỬữỮứỨựỰvVwWxXyYỳỲỷỶỹỸýÝỵỴzZ0-9]+$', '', s)
if prompt := st.chat_input("What is up?"):
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user"):
st.markdown(prompt)
response = clean_answer(question_answering(prompt))
with st.chat_message("assistant"):
st.markdown(response)
st.session_state.messages.append({"role": "assistant", "content": response})
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