Update app.py

app.py

@@ -1,6 +1,14 @@
 import streamlit as st
+import pandas as pd
+import numpy as np
+
 from sentence_transformers.util import cos_sim
 from sentence_transformers import SentenceTransformer
+from bokeh.plotting import figure, output_notebook, show, save
+from bokeh.io import output_file, show
+from bokeh.models import ColumnDataSource, HoverTool
+from sklearn.manifold import TSNE
+
 
 @st.cache
 def load_model():
@@ -8,6 +16,12 @@ def load_model():
     model.eval()
     return model
     
+ @st.cache
+ def load_plot_data():
+     embs = np.load('semeval2015-embs.npy')
+     data = pd.read_csv('semeval2015-data.csv')
+     return embs, data
+    
 st.title("Sentence Embedding for Spanish with Bertin")
 st.write("Sentence embedding for spanish trained on NLI. Used for Sentence Textual Similarity. Based on the model hackathon-pln-es/bertin-roberta-base-finetuning-esnli.")
 
@@ -20,6 +34,41 @@ if st.button('Compute similarity'):
     encodings = model.encode([sent1, sent2])
     sim = cos_sim(encodings[0], encodings[1]).numpy().tolist()[0][0]
     st.text('Cosine Similarity: {0:.4f}'.format(sim))
+    
+    print('Generating visualization...')
+    sentembs, data = load_plot_data()
+    X_embedded = TSNE(n_components=2, learning_rate='auto',
+                  init='random').fit_transform(np.concatenate([sentembs, encodings], axis=1))
+                  
+    data.append({'sent': sent1, 'color': '#F0E442'}) # sentence 1
+    data.append({'sent': sent2, 'color': '#D55E00'}) # sentence 2
+    data['x'] = X_embedded[:,0]
+    data['y'] = X_embedded[:,1]
+    
+    source = ColumnDataSource(data)
+    
+    p = figure(title="Embeddings in space")
+    p.circle(
+      x='x',
+      y='y',
+      legend_label="Objects",
+      #fill_color=["red"],
+      color='color',
+      fill_alpha=0.5,
+      line_color="blue",
+      size=14,
+      source=source
+    )
+    p.add_tools(HoverTool(
+      tooltips=[
+          ('sent', '@sent')
+      ],
+      formatters={
+          '@sent': 'printf'
+      },
+      mode='mouse'
+    ))
+    st.bokeh_chart(p, use_container_width=True)
   else:
       st.write('Missing a sentences')
 else: