First commit

app.py

@@ -1,5 +1,20 @@
 import streamlit as st
+import numpy as np
+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+import spacy
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+nlp = spacy.load("en_core_web_sm")
+model = SentenceTransformer("rufimelo/Legal-BERTimbau-sts-base")
 
+def compute_similarity(left_text: str, right_text: str) -> np.ndarray:
+    embeddings = model.encode([left_text, right_text])
+    similarity = cosine_similarity(embeddings[0], embeddings[1])
+
+    return similarity
+
+first = st.text_input('First', 'This is a test')
+second = st.text_input('Second', 'This is another test')
+
+s = compute_similarity(left_text=first, right_texts=second)
+st.datafeame(s)