import gradio as gr
from transformers import pipeline
import multiprocessing
import torch
import os
from psutil import virtual_memory
from pathlib import Path
import random
from datetime import datetime
import PIL
from PIL import Image
import os
import gc
from translatepy import Translator, Language
ts = Translator()
from deep_translator import DeeplTranslator, GoogleTranslator
from pathlib import Path
import csv
import logging
import requests
from rudalle.pipelines import generate_images, show, super_resolution, cherry_pick_by_ruclip
from rudalle import get_rudalle_model, get_tokenizer, get_vae, get_realesrgan
from rudalle.utils import seed_everything
from ruclip import load as get_ruclip

# prepare models:
device = 'cuda'
model = get_rudalle_model('Malevich', pretrained=True, fp16=True, device=device)
tokenizer = get_tokenizer()
vae = get_vae(dwt=True).to(device)

#device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")


pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")

def predict(image):
  predictions = pipeline(image)
  return {p["label"]: p["score"] for p in predictions}

gr.Interface(
    predict,
    inputs=gr.inputs.Image(label="Upload hot dog candidate", type="filepath"),
    outputs=gr.outputs.Label(num_top_classes=2),
    title="Hot Dog? Or Not?",
).launch()