app.py

import streamlit as st
import nltk
from transformers import pipeline
from sentence_transformers import SentenceTransformer
from scipy.spatial.distance import cosine
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

def plot_heatmap(labels, heatmap, rotation=90):
  sns.set(font_scale=1.2)
  fig, ax = plt.subplots()
  g = sns.heatmap(
      heatmap,
      xticklabels=labels,
      yticklabels=labels,
      vmin=-1,
      vmax=1,
      cmap="coolwarm")
  g.set_xticklabels(labels, rotation=rotation)
  g.set_title("Textual Similarity")

  st.pyplot(fig)
  #plt.show()

st.header("Sentence Similarity Demo")
st.markdown("This demo uses the sentence_transformers library to plot sentence similarity between a list of sentences. Change the text below and try for yourself!")

# Streamlit text boxes
text = st.text_area('Enter sentences:', value="The sun is hotter than the moon.\nThe sun is very bright.\nI hear that the universe is very large.\nToday is Tuesday.")

# Model setup
model = SentenceTransformer('paraphrase-distilroberta-base-v1')
nltk.download('punkt')

# Run model
if text:
    sentences = nltk.tokenize.sent_tokenize(text)
    embed = model.encode(sentences)
    
    sim = np.zeros([len(embed), len(embed)])
    for i,em in enumerate(embed):
        for j,ea in enumerate(embed):
            sim[i][j] = 1.0-cosine(em,ea)
    plot_heatmap(sentences, sim)