app.py

# Import required libraries
import gradio as gr # For interface
from sentence_transformers import SentenceTransformer # For embedding the text
import torch # For gpu 
import numpy as np 

# Make the app device agnostic
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

# Load a pretrained Sentence Transformer model and move it to the appropriate device
model = SentenceTransformer("mixedbread-ai/mxbai-embed-large-v1")
model = model.to(device)

# Function that does the embedding
def predict(input_text):
    
    # Calculate embeddings by calling model.encode(), specifying the device
    embeddings = model.encode(input_text, device=device)
    
    # Set the print options to avoid truncation and use fixed-point notation
    np.set_printoptions(threshold=np.inf, precision=8, suppress=True, floatmode='fixed')

    # Convert the array to a string for display
    embeddings_str = np.array2string(embeddings, separator=',')
    
    return embeddings_str

# Gradio app interface
gradio_app = gr.Interface(
    predict,
    inputs=gr.Textbox(placeholder="Insert Text", label='Text'), 
    outputs=gr.Textbox(max_lines=1, placeholder='Vector of dimensions 1024', label='Vector', show_label=True, show_copy_button=True),
    title="Text to Vector Generator",
    description="Embedding model: mixedbread-ai/mxbai-embed-large-v1."
)

if __name__ == "__main__":
    gradio_app.launch()