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import streamlit as st
from streamlit_option_menu import option_menu
from word2vec import *
import pandas as pd
st.set_page_config(page_title="Ancient Greek Word2Vec", layout="centered")
# Horizontal menu
active_tab = option_menu(None, ["Nearest neighbours", "Cosine similarity", "3D graph", 'Dictionary'],
menu_icon="cast", default_index=0, orientation="horizontal")
# Nearest neighbours tab
if active_tab == "Nearest neighbours":
st.write("### TO DO: add description of function")
col1, col2 = st.columns(2)
with st.container():
with col1:
word = st.text_input("Enter a word", placeholder="πατήρ")
with col2:
time_slice = st.selectbox("Time slice", ["Archaic", "Classical", "Hellenistic", "Early Roman", "Late Roman"])
models = st.multiselect(
"Select models to search for neighbours",
["Archaic", "Classical", "Hellenistic", "Early Roman", "Late Roman"]
)
n = st.slider("Number of neighbours", 1, 50, 15)
nearest_neighbours_button = st.button("Find nearest neighbours")
# If the button to calculate nearest neighbours is clicked
if nearest_neighbours_button:
# Rewrite timeslices to model names: Archaic -> archaic_cbow
if time_slice == 'Hellenistic':
time_slice = 'hellen'
elif time_slice == 'Early Roman':
time_slice = 'early_roman'
elif time_slice == 'Late Roman':
time_slice = 'late_roman'
time_slice = time_slice.lower() + "_cbow"
# Check if all fields are filled in
if validate_nearest_neighbours(word, time_slice, n, models) == False:
st.error('Please fill in all fields')
else:
# Rewrite models to list of all loaded models
models = load_selected_models(models)
nearest_neighbours = get_nearest_neighbours(word, time_slice, n, models)
df = pd.DataFrame(
nearest_neighbours,
columns=["Word", "Time slice", "Similarity"],
index = range(1, len(nearest_neighbours) + 1)
)
st.table(df)
# Cosine similarity tab
elif active_tab == "Cosine similarity":
col1, col2 = st.columns(2)
col3, col4 = st.columns(2)
with st.container():
with col1:
word_1 = st.text_input("Enter a word", placeholder="πατήρ")
with col2:
time_slice_1 = st.selectbox("Time slice word 1", ["Archaic", "Classical", "Hellenistic", "Early Roman", "Late Roman"])
with st.container():
with col3:
word_2 = st.text_input("Enter a word", placeholder="μήτηρ")
with col4:
time_slice_2 = st.selectbox("Time slice word 2", ["Archaic", "Classical", "Hellenistic", "Early Roman", "Late Roman"])
# Create button for calculating cosine similarity
cosine_similarity_button = st.button("Calculate cosine similarity")
# If the button is clicked, execute calculation
if cosine_similarity_button:
cosine_simularity_score = get_cosine_similarity(word_1, time_slice_1, word_2, time_slice_2)
st.write(cosine_simularity_score)
# 3D graph tab
elif active_tab == "3D graph":
with st.container():
st.write("3D graph tab")
# Dictionary tab
elif active_tab == "Dictionary":
with st.container():
st.write("Dictionary tab")
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