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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(texts):
# Tokenize the input array of sentences
inputs = tokenizer(texts, return_tensors="pt", padding=True, truncation=True)
outputs = model(**inputs)
vectors = outputs.pooler_output.detach().numpy()
# Convert each vector to a string representation and create an object
result = [
{"sentence": sentence, "vector": ", ".join(map(str, vector))}
for sentence, vector in zip(texts, vectors)
]
return result
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.JSON(label="Sentence and Vector Pairs"),
title="Batch Text to Vector",
description="This demo converts an array of sentences to vectors and returns objects with sentence and vector."
)
demo.launch()
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