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import streamlit as st
from embedding_models.registry import registry as embedding
from similarity_models.registry import registry as similarity
import pandas as pd
def calculate_similarity(text_1, text_2):
# TODO: pick any N random embedding models
similarity_scores = []
# TODO: pick any random similarity model
similarity_model = similarity.models()["cosine"]
for name, model in embedding.models().items():
embedding_1 = model.embed(text_1)
embedding_2 = model.embed(text_2)
similarity_scores.append((name, similarity_model.score(embedding_1, embedding_2)))
return similarity_scores
class BattlegroundTab:
def __init__(self):
pass
def ui(self):
st.header("Battleground")
st.write("Battle embedding models with each other! May the best win!")
col1, col2 = st.columns(2)
with col1:
text_1 = st.text_input("Enter first text here!")
with col2:
text_2 = st.text_input("Enter second text here!")
expected_sc = st.slider(
'How similar do feel these words are',
min_value=1, max_value=10, step=1, value=5) / 10
st.write('Expected Similarity Score = ', expected_sc)
if st.button("Calculate Similarity Score"):
similarity_scores = calculate_similarity(text_1, text_2)
df = pd.DataFrame(similarity_scores, columns=['Model', 'Score'])
df['Loss'] = abs(df['Score'] - expected_sc)
winner_model = df.loc[df['Loss'].idxmin(), 'Model']
df['Winner'] = ''
df.loc[df['Model'] == winner_model, 'Winner'] = '👑'
df = df.drop(columns=['Loss'])
markdown_table = df.to_markdown(index=False)
st.markdown(markdown_table)
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