app.py

import gradio as gr
from transformers import AutoModel, AutoTokenizer
import numpy as np

# Load a small CPU model for text to vector processing
model_name = "sentence-transformers/all-mpnet-base-v2"
model = AutoModel.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

def text_to_vector(text):
    inputs = tokenizer(text, return_tensors="pt")
    outputs = model(**inputs)
    vector = outputs.pooler_output.detach().numpy()[0]
    # Convert to a string representation for display
    return ", ".join(map(str, vector))

demo = gr.Interface(
    fn=text_to_vector,
    inputs=gr.Textbox(label="Enter text"),
    outputs=gr.Textbox(label="Text Vector"),
    title="Text to Vector",
    description="This demo uses a small CPU model to convert text to vector."
)

demo.launch()