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)