Create app.py

app.py

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+from classes import classes
+import numpy as np
+from sentence_transformers import SentenceTransformer, util
+import streamlit as st
+
+
+# Simple sentence transformer
+model_checkpoint = 'sentence-transformers/paraphrase-distilroberta-base-v1'
+model = SentenceTransformer(model_checkpoint)
+
+# Predefined messages and their embeddings
+classes_text = np.array(classes)
+classes_embeddings = model.encode(classes_text, convert_to_numpy=True)
+assert classes_embeddings.shape[0] == len(classes)
+
+# Function to compare the embedding of the human chat/text message with the embeddings of the 
+# predefined messages
+def convert(sentence_embedding: np.array, class_embeddings: np.array, top_n=5) -> np.array:
+    similarities = np.array(util.cos_sim(sentence_embedding, class_embeddings)).reshape(-1,)
+    top_n_indices = np.argsort(similarities)[::-1][0:top_n]
+
+    return top_n_indices
+
+# Simple title and description for the app
+st.title('JHG Chat Message Converter')
+st.write('Converts human chat/text messages into predefined chat messages via a sentence transformer')
+
+# Text box to enter a chat/text message
+text = st.text_area('Enter chat message')
+
+if text:
+    # Use the sentence transformer and "convert" function to display predicted, predefined messages
+    text_embedding = model.encode(text, convert_to_numpy=True)
+    indices = convert(text_embedding, classes_embeddings)
+    predicted_classes = classes_text[indices]
+
+    for converted_message in predicted_classes:
+        st.write(converted_message)