| 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() |