app.py

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

# Load the tokenizer and model
@st.cache_resource
def load_model():
    tokenizer = AutoTokenizer.from_pretrained('Alibaba-NLP/gte-base-en-v1.5', trust_remote_code=True)
    model = AutoModel.from_pretrained('Alibaba-NLP/gte-base-en-v1.5', trust_remote_code=True)
    model.to('cpu')
    return tokenizer, model

tokenizer, model = load_model()

def extract_embeddings(text, tokenizer, model):
    # Tokenize the input text
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
    inputs = {k: v.to('cpu') for k, v in inputs.items()}
    
    # Get the model's outputs
    with torch.no_grad():
        outputs = model(**inputs)
    
    # Extract the embeddings (use the output of the last hidden state)
    embeddings = outputs.last_hidden_state.mean(dim=1)
    
    return embeddings.squeeze().cpu().numpy()

# Streamlit app
st.title("Text Embeddings Extractor")

text = st.text_area("Enter text to extract embeddings:", "This is an example sentence.")

if st.button("Extract Embeddings"):
    embeddings = extract_embeddings(text, tokenizer, model)
    st.write("Embeddings:")
    st.write(embeddings)