app.py

from typing import List

import gradio as gr
import spaces
import numpy as np
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/nomic-embed-text-v1.5", trust_remote_code=True)

def embed(document: str) -> List[float]:
    """
    Embed text using the Nomic AI model, normalize the embedding, and return a 768-dimension vector.

    Args:
        document (str): The input text to embed.

    Returns:
        List[float]: The normalized embedding vector (length 768).
    """
    embedding = model.encode(document)
    normalized_embedding = embedding / np.linalg.norm(embedding)
    return normalized_embedding.tolist()

with gr.Blocks() as app:
    text_input = gr.Textbox(label="Enter text to embed")
    output = gr.JSON(label="Normalized Text Embedding")

    text_input.submit(embed, inputs=text_input, outputs=output)

if __name__ == '__main__':
    app.queue().launch(server_name="0.0.0.0", show_error=True, server_port=7860, mcp_server=True)