Create app.py

app.py

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+import gradio as gr
+from sentence_transformers import SentenceTransformer
+
+# Load the model
+model = SentenceTransformer('sentence-transformers/msmarco-distilbert-dot-v5')
+
+# Function to get the embedding
+def embedding(text):
+    text_emb = model.encode(text)
+    return text_emb
+
+# Define the Streamlit app
+gradio_app = gr.Interface(
+    embedding,
+    inputs=gr.Text(label="Select hot dog candidate"),
+    outputs=gr.Text(label="Embedding"),
+    title="Embedding",
+)
+
+if __name__ == "__main__":
+    gradio_app.launch()