import gradio as gr
import numpy as np

from datasets import load_dataset
from sentence_transformers import SentenceTransformer
from qdrant_client import QdrantClient, models

qdrant = QdrantClient(':memory:') # create in-mem instance of vector db

split_range = '[0:500]'
split = 'train{}'.format(split_range)

quora_ds = load_dataset(path='quora', split=split, streaming=True)

EMBEDDING_MODEL_NAME = 'mixedbread-ai/mxbai-embed-large-v1'
encoder = SentenceTransformer(EMBEDDING_MODEL_NAME)

def greet(name):
    return "Hello " + name + "!!"

iface = gr.Interface(fn=greet, inputs="text", outputs="text")
iface.launch()