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''' To-do | |
Create a side bar to compare two or upload CSV | |
In the second tab, allow them to compare all CSV files | |
''' | |
import streamlit as st | |
import pandas as pd | |
from sentence_transformers import SentenceTransformer | |
from sklearn.metrics.pairwise import cosine_similarity | |
model = SentenceTransformer('paraphrase-xlm-r-multilingual-v1') | |
# Streamlit interface | |
st.title("Sentence Similarity") | |
sidebar_selectbox = st.sidebar.selectbox( | |
"What would you like to work with?", | |
("Compare two sentences", "Bulk upload and mark") | |
) | |
# Streamlit form elements (default to "Compare two sentences") | |
if sidebar_selectbox == "Compare two sentences": | |
st.subheader("Compare the similarity between two sentences") | |
with st.form("submission_form", clear_on_submit=False): | |
sentence_1 = st.text_input("Sentence 1 input") | |
sentence_2 = st.text_input("Sentence 2 input") | |
submit_button_compare = st.form_submit_button("Compare Sentences") | |
# If submit_button_compare clicked | |
if submit_button_compare: | |
# Perform calculations | |
#Initialise sentences | |
sentences = [] | |
# Append input sentences to 'sentences' list | |
sentences.append(sentence_1) | |
sentences.append(sentence_2) | |
# Create embeddings for both sentences | |
sentence_embeddings = model.encode(sentences) | |
cos_sim = cosine_similarity(sentence_embeddings[0].reshape(1, -1), sentence_embeddings[1].reshape(1, -1))[0][0] | |
cos_sim = round(cos_sim * 100) # Convert to percentage and round-off | |
st.write('Similarity between {} and {} is {}%'.format(sentence_1, | |
sentence_2, cos_sim)) | |
if sidebar_selectbox == "Bulk upload and mark": | |
st.subheader("Bulk compare similarity of sentences") | |
sentence_reference = st.text_input("Reference sentence input") | |
# Only allow user to upload CSV files | |
data_file = st.file_uploader("Upload CSV",type=["csv"]) | |
if data_file is not None: | |
with st.spinner('Wait for it...'): | |
file_details = {"filename":data_file.name, "filetype":data_file.type, "filesize":data_file.size} | |
# st.write(file_details) | |
df = pd.read_csv(data_file) | |
# Get length of df.shape (might not need this) | |
#total_rows = df.shape[0] | |
similarity_scores = [] | |
for idx, row in df.iterrows(): | |
# st.write(idx, row['Sentences']) | |
# Create an empty sentence list | |
sentences = [] | |
# Compare the setences two by two | |
sentence_comparison = row['Sentences'] | |
sentences.append(sentence_reference) | |
sentences.append(sentence_comparison) | |
sentence_embeddings = model.encode(sentences) | |
cos_sim = cosine_similarity(sentence_embeddings[0].reshape(1, -1), sentence_embeddings[1].reshape(1, -1))[0][0] | |
cos_sim = round(cos_sim * 100) | |
similarity_scores.append(cos_sim) | |
# Append new column to dataframe | |
df['Similarity (%)'] = similarity_scores | |
st.dataframe(df) | |
st.success('Done!') | |
def convert_df(df): | |
return df.to_csv().encode('utf-8') | |
csv = convert_df(df) | |
st.download_button( | |
"Press to Download", | |
csv, | |
"marked assignment.csv", | |
"text/csv", | |
key='download-csv' | |
) |