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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
+from sklearn.cluster import KMeans
+import tensorflow as tf
+import tensorflow_hub as hub
+
+
+def cluster_examples(messages, embed, nc=3):
+    km = KMeans(
+    n_clusters=nc, init='random',
+    n_init=10, max_iter=300, 
+    tol=1e-04, random_state=0
+    )
+    km = km.fit_predict(embed)
+    for n in range(nc):
+        idxs = [i for i in range(len(km)) if km[i] == n]
+        ms = [messages[i] for i in idxs]
+        st.markdown ("CLUSTER : %d"%n)
+        for m in ms:
+            st.markdown (m)
+
+
+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)
+  
+# Streamlit text boxes
+text = st.text_area('Enter sentences:', value="Behavior right this is a kind of Heisenberg uncertainty principle situation if I told you, then you behave differently. What would be the impressive thing is you have talked about winning a nobel prize in a system winning a nobel prize. Adjusting it and then making your own.  That is when I fell in love with computers.  I realized that they were a very magical device.  Can go to sleep come back the next day and it is solved.  You know that feels magical to me.")
+
+nc = st.slider('Select a number of clusters:', min_value=1, max_value=15, value=3)
+
+model_type = st.radio("Choose model:", ('Sentence Transformer', 'Universal Sentence Encoder'), index=0)
+
+# Model setup
+if model_type == "Sentence Transformer":
+    model = SentenceTransformer('paraphrase-distilroberta-base-v1')
+elif model_type == "Universal Sentence Encoder":
+    model_url = "https://tfhub.dev/google/universal-sentence-encoder-large/5"
+    model = hub.load(model_url)
+
+nltk.download('punkt')
+
+# Run model
+if text:
+    sentences = nltk.tokenize.sent_tokenize(text)
+    if model_type == "Sentence Transformer":
+        embed = model.encode(sentences)
+    elif model_type == "Universal Sentence Encoder":
+        embed = model(sentences).numpy()
+    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)
+    st.subheader("Similarity Heatmap")
+    plot_heatmap(sentences, sim)
+    st.subheader("Results from K-Means Clustering")
+    cluster_examples(sentences, embed, nc)