app.py

import streamlit as st
import pandas as pd
# To make things easier later, we're also importing numpy and pandas for
# working with sample data.
from sentence_transformers import SentenceTransformer
import faiss
import numpy as np

# Load the moka-ai/m3e-base model
model = SentenceTransformer("moka-ai/m3e-base")

# Encode the documents into embeddings
documents = ["班车线路", "兴趣小组", "财务报销"]
document_embeddings = model.encode(documents)

# Store the embeddings to FAISS
index = faiss.IndexFlatIP(document_embeddings.shape[1])
index.add(document_embeddings)

# Encode the query into an embedding
query = "报销餐费"
query_embedding = model.encode([query])[0]

# Search the FAISS index for the most similar document
D, I = index.search(np.array([query_embedding]), k=1)

# Print the most similar document
st.write('Most similar document: ', documents[I[0][0]])

#======================================================================

st.title('My first app')

st.write("Here's our first attempt at using data to create a table:")

df = pd.DataFrame({
    'first column': [1, 2, 3, 4],
    'second column': [10, 20, 30, 40]
})

st.write(df)

if st.checkbox('Show dataframe'):
    chart_data = pd.DataFrame(
       np.random.randn(20, 3),
       columns=['a', 'b', 'c'])

    chart_data
    

option = st.selectbox(
    'Which number do you like best?',
     df['first column'])

st.write('You selected: ', option)

text1 = st.text('This is some text.')

if st.button('Say hello'):
    st.write('Why hello there')
else:
    st.write('Goodbye')


agree = st.checkbox('I agree')

if agree:
    st.write('Great!')
    
age = st.slider('How old are you?', 0, 130, 25)

st.write("I'm ", age, 'years old')

title = st.text_input('Movie title', 'Life of Brian')

st.write('The current movie title is', title)

number = st.number_input('Insert a number')

st.write('The current number is ', number)