Similarity Heatmap Demo

app.py

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+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)
+
+

requirements.txt

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+torch
+transformers
+sentence_transformers
+nltk
+scipy
+numpy
+seaborn
+matplotlib