app.py

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()