app.py

import streamlit as st
import pandas as pd

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('paraphrase-MiniLM-L6-v2')

input_sentence = st.text_input('Movie title', 'Life of Brian')
#st.write('The current movie title is', title)

#Sentences we want to encode. Example:
sentence = ['This framework generates embeddings for each input sentence']


#Sentences are encoded by calling model.encode()
embedding = model.encode(input_sentence)

x = st.slider('Select a value')
#embedding = model.encode(input_sentence)
#st.write(x, 'squared is', x * x, 'embedding', embedding[0][0])
st.write('The embedding of', input_sentence, 'at position',x,'is',embedding[0][int(x)])


uploaded_file = st.file_uploader("Choose a file")
if uploaded_file is not None:
    #read csv
    df1=pd.read_csv(uploaded_file)