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from sentence_transformers import SentenceTransformer
import gradio as gr
import ast
# Load the pre-trained model
model = SentenceTransformer('all-MiniLM-L6-v2')
def get_embeddings(sentences):
chunks = ast.literal_eval(sentences)
embeddings = model.encode(chunks, convert_to_tensor=True)
return embeddings.tolist()
# Define the Gradio interface
interface = gr.Interface(
fn=get_embeddings, # Function to call
inputs=gr.Textbox(lines=2, placeholder="Enter sentences here, one per line"), # Input component
# outputs=gr.JSON(),
outputs=gr.Textbox(label="Embeddings"),
title="Sentence Embeddings", # Interface title
description="Enter sentences to get their embeddings." # Description
)
# Launch the interface
interface.launch()
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