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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_file1 = st.file_uploader("Choose a file: sentence list")
if uploaded_file1 is not None:
#read csv
df1=pd.read_csv(uploaded_file1)
st.write(df1.head())
uploaded_file2 = st.file_uploader("Choose a file: topic list")
if uploaded_file2 is not None:
#read csv
df2=pd.read_csv(uploaded_file2)
st.write(df2.head())
if uploaded_file1 is not None and uploaded_file2 is not None:
from sentence_transformers import SentenceTransformer, util
import torch
embedder = SentenceTransformer('all-MiniLM-L6-v2')
corpus = df1['sentence']
topics = df2['topic']
corpus_embeddings = embedder.encode(corpus, convert_to_tensor=True)
for topic in topics:
topic_embedding = embedder.encode(topic, convert_to_tensor=True)
cos_scores = util.cos_sim(query_embedding, corpus_embeddings)[0]
df1[str(topic)] = cos_scores
st.write(df1)
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