luiscape

import io
import time
import modal
import os
import torch
import diffusers

from pathlib import Path

stub = modal.Stub("local-model-mount-test")

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import io
import os
import time
from pathlib import Path

import modal
import typer

stub = modal.Stub("stable-diffusion-cli")
app = typer.Typer()

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# Runs diffusers-rs on Modal.
import os
import time
import modal
import subprocess

CARGO_PATH:str = "/root/.cargo/bin/cargo"


def _convert_clip():

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"""
Script to run batch inference using HuggingFace's zero-shot text-classification model on Modal.

Based on the tutorial: https://modal.com/docs/guide/ex/batch_inference_using_huggingface 

Goal: filter a large Hugging Face dataset for food-related images (based on the text caption).
"""
import io

import modal

luiscape

"""
Script to run batch inference using HuggingFace's zero-shot text-classification model on Modal.

Based on the tutorial: https://modal.com/docs/guide/ex/batch_inference_using_huggingface 

Goal: filter a large Hugging Face dataset for food-related images (based on the text caption).
"""
import io

import modal

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# ## Basic setup
from __future__ import annotations

import io
import os
import time
from pathlib import Path

import modal

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#!/usr/bin/env bash
# Needed in order to support the ollama/ollama image.
exec "$@"

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import sys
import time

import zmq
from common import Message, app

from server import process_message


def zmq_query(address: str, n_messages: int = 10):

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# Run NVIDIA Parakeet in Modal. 
import wave

import numpy as np

import modal

image = (
    modal.Image.from_registry("nvidia/cuda:12.8.0-cudnn-devel-ubuntu22.04", add_python="3.12")
    .pip_install("uv")