app.py

from typing import List

import gradio as gr
from sentence_transformers import SentenceTransformer

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


def embed(document: str):
    return model.encode(document)


with gr.Blocks(title="Nomic Text Embeddings") as app:
    gr.Markdown("# Nomic Text Embeddings v1.5")
    gr.Markdown("Generate embeddings for your text using the nomic-embed-text-v1.5 model.")
    
    # Create an input text box
    text_input = gr.Textbox(label="Enter text to embed", placeholder="Type or paste your text here...")

    # Create an output component to display the embedding
    output = gr.JSON(label="Text Embedding")
    
    # Add a submit button with API name
    submit_btn = gr.Button("Generate Embedding", variant="primary")

    # Handle both button click and text submission
    submit_btn.click(embed, inputs=text_input, outputs=output, api_name="predict")
    text_input.submit(embed, inputs=text_input, outputs=output)
    
    # Add API usage guide
    gr.Markdown("## API Usage")
    gr.Markdown("""
    You can use this API programmatically with direct HTTP requests (queue disabled).
    
    ### Using cURL
    ```bash
    curl -X POST https://ipepe-nomic-embeddings.hf.space/api/predict \
      -H "Content-Type: application/json" \
      -d '{
        "fn_index": 0,
        "data": ["Your text to embed goes here"]
      }'
    ```
    
    ### Python Example (using requests)
    ```python
    import requests
    
    response = requests.post(
        "https://ipepe-nomic-embeddings.hf.space/api/predict",
        json={"fn_index": 0, "data": ["Your text to embed goes here"]}
    )
    print(response.json())  # Returns the embedding array
    ```
    
    ### Python Example (using Gradio Client)
    ```python
    from gradio_client import Client
    
    client = Client("ipepe/nomic-embeddings")
    result = client.predict(
        "Your text to embed goes here",
        api_name="/predict"
    )
    print(result)  # Returns the embedding array
    ```
    
    ### JavaScript/Node.js Example
    ```javascript
    import { client } from "@gradio/client";
    
    const app = await client("ipepe/nomic-embeddings");
    const result = await app.predict("/predict", ["Your text to embed goes here"]);
    console.log(result.data);
    ```
    
    The response will contain the embedding array as a list of floats.
    """)

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