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/all-MiniLM-L6-v2")
+
+def get_embedding(text):
+    embedding = model.encode(text).tolist()  # Convert to list for better compatibility
+    return embedding
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=get_embedding,
+    inputs=gr.Textbox(lines=2, placeholder="Enter a sentence..."),
+    outputs="json",
+    title="Sentence Transformer Demo",
+    description="Enter a sentence and get its embedding using Sentence Transformers."
+)
+
+# Launch app
+if __name__ == "__main__":
+    iface.launch()