app.py

import gradio as gr
from gradio.mix import Parallel, Series



description = "BigGAN text-to-image demo."
title = "BigGAN ImageNet"

Gans=["biggan-deep-128", "biggan-deep-256", "biggan-deep-512"]

model_names={"vqgan_imagenet_f16_16384": 'ImageNet 16384',"vqgan_imagenet_f16_1024":"ImageNet 1024", 'vqgan_openimages_f16_8192':'OpenImages 8912',"wikiart_1024":"WikiArt 1024", "wikiart_16384":"WikiArt 16384", "coco":"COCO-Stuff", "faceshq":"FacesHQ", "sflckr":"S-FLCKR"}

import os
import torch
from PIL import Image
from torchvision import transforms

# === SEMI-WEAKLY SUPERVISED MODELSP RETRAINED WITH 940 HASHTAGGED PUBLIC CONTENT ===
model = torch.hub.load('facebookresearch/semi-supervised-ImageNet1K-models', 'resnet18_swsl')
# model = torch.hub.load('facebookresearch/semi-supervised-ImageNet1K-models', 'resnet50_swsl')
# model = torch.hub.load('facebookresearch/semi-supervised-ImageNet1K-models', 'resnext50_32x4d_swsl')
# model = torch.hub.load('facebookresearch/semi-supervised-ImageNet1K-models', 'resnext101_32x4d_swsl')
# model = torch.hub.load('facebookresearch/semi-supervised-ImageNet1K-models', 'resnext101_32x8d_swsl')
# model = torch.hub.load('facebookresearch/semi-supervised-ImageNet1K-models', 'resnext101_32x16d_swsl')
# ================= SEMI-SUPERVISED MODELS PRETRAINED WITH YFCC100M ==================
# model = torch.hub.load('facebookresearch/semi-supervised-ImageNet1K-models', 'resnet18_ssl')
# model = torch.hub.load('facebookresearch/semi-supervised-ImageNet1K-models', 'resnet50_ssl')
# model = torch.hub.load('facebookresearch/semi-supervised-ImageNet1K-models', 'resnext50_32x4d_ssl')
# model = torch.hub.load('facebookresearch/semi-supervised-ImageNet1K-models', 'resnext101_32x4d_ssl')
# model = torch.hub.load('facebookresearch/semi-supervised-ImageNet1K-models', 'resnext101_32x8d_ssl')
# model = torch.hub.load('facebookresearch/semi-supervised-ImageNet1K-models', 'resnext101_32x16d_ssl')

io1 = gr.Interface.load('huggingface/osanseviero/BigGAN-deep-128')
io2 = gr.Interface.load('huggingface/osanseviero/BigGAN-deep-128')
#io3 = gr.Interface.load('vqgan_imagenet_f16_16384')                  
io3 = gr.Interface.load(model)                  
#io3 = gr.Interface.load("huggingface/emilyalsentzer/Bio_Discharge_Summary_BERT")                  
#io3 = gr.Interface.load("huggingface/google/pegasus-pubmed")  
#io3 = gr.Interface.load("huggingface/tennessejoyce/titlewave-t5-base")  

# = Parallel(io1, io2, io3,

interface = Parallel(io1,io2,io3, 
            description=description,
            title = title,
            examples=[
            ["lighthouse"],
            ["eyeglasses"],
            ["stool"],
            ["window"],
            ["hand"],
            ["dice"],
            ["cloud"],
            ["gate"],
            ["cat"],
            ["toes"]
            ]
)
interface.launch()