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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) |