Update app.py

app.py

@@ -1,6 +1,26 @@
-import gradio as gr
-from datasets import load_dataset
+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+import numpy as np
 
-ds = load_dataset("b-mc2/sql-create-context")
+# Load the model
+model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
 
-gr.load("models/sentence-transformers/all-MiniLM-L6-v2").launch()
+# Define your sentences
+sentences = [
+    "That is a happy person",
+    "That is a happy dog",
+    "That is a very happy person",
+    "Today is a sunny day"
+]
+
+# Encode the sentences to get their embeddings
+embeddings = model.encode(sentences)
+
+# Compute the cosine similarity matrix
+similarities = cosine_similarity(embeddings)
+
+# Print the shape of the similarity matrix
+print(similarities.shape)  # Output: (4, 4)
+
+# Optionally, print the similarity matrix
+print(similarities)