app.py

import gradio as gr
from sentence_transformers import SentenceTransformer

# Load the Nomic embedding model
model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)

def get_embedding(text):
    """Generate an embedding for the input text using Nomic encoder."""
    if not text.strip():
        return "Please provide some text."
    
    # Generate embedding
    embedding = model.encode([text])[0]  # Get the first (and only) embedding
    
    # Return embedding as list (more user-friendly in the UI)
    return embedding.tolist()

# Create Gradio interface
interface = gr.Interface(
    fn=get_embedding,
    inputs=gr.Textbox(lines=5, placeholder="Enter text to embed..."),
    outputs=gr.JSON(),
    title="Text Embedding with Nomic Encoder",
    description="Enter text to get its embedding vector using the Nomic Encoder model."
)

# Launch the interface
if __name__ == "__main__":
    interface.launch()