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import gradio as gr
from transformers import AutoModel, AutoTokenizer
import numpy as np
import json
# Load a small CPU model for text to vector processing
model_name = "Supabase/gte-small"
model = AutoModel.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
def text_to_vector(texts_json):
try:
texts = json.loads(texts_json)
if not isinstance(texts, list):
raise ValueError("Input must be a JSON array of strings.")
except json.JSONDecodeError:
raise ValueError("Invalid JSON format.")
inputs = tokenizer(texts, return_tensors="pt", padding=True, truncation=True)
outputs = model(**inputs)
vectors = outputs.pooler_output.detach().numpy() # NumPy array
# Flatten the array and return as a 1D array of floats
return vectors.reshape(-1).tolist()
demo = gr.Interface(
fn=text_to_vector,
inputs=gr.Textbox(label="Enter JSON array", placeholder="Enter an array of sentences as a JSON string"),
outputs=gr.Textbox(label="Text Vectors (flattened float array)", lines=10),
title="Batch Text to Vector",
description="This demo converts an array of sentences to vectors and returns them as a flattened float array."
)
demo.launch() |