app.py

import streamlit as st
from transformers import AutoModel, AutoTokenizer
import torch

model_name = "sentence-transformers/all-MiniLM-L6-v2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

def get_embedding(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
    with torch.no_grad():
        outputs = model(**inputs)
    embeddings = outputs.last_hidden_state.mean(dim=1)
    return embeddings

st.title("Text Embedding with all-MiniLM-L6-v2")
st.write("Enter text to get its embedding:")

input_text = st.text_input("Input Text", "")

if input_text:
    embedding = get_embedding(input_text)
    st.write("Embedding:")
    st.write(embedding)