import streamlit as st
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
from sentence_transformers import SentenceTransformer

def app():
    st.title("Text Similarity")
    
    model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
    
    with st.container():
        col1, col2 = st.columns(2)
        with col1:
            word_to_embed1 = st.text_input("Text 1", value="The most vulnerable countries are seeking ‘loss and damage’ compensation from the biggest polluters.",)
        with col2:                                 
            word_to_embed2 = st.text_input("Text 2", value="COP27 opens amid compounding crises of war, warming and economic instability.",)
            
    if st.button("Embed"):
        with st.spinner("Embedding comparing  your inputs"):
            
            document = [word_to_embed1 ,word_to_embed2]
            #Encode paragraphs
            document_embeddings = model.encode(document, show_progress_bar=False)
            #Compute cosine similarity between labels sentences and paragraphs
            similarity_matrix = cosine_similarity(document_embeddings)
            
            st.write("Text similarity:", round(similarity_matrix[0][1]*100,2),"%")