app.py

import torch
from rudalle import get_tokenizer, get_vae
from rudalle.utils import seed_everything

import sys
from rudolph.model.utils import get_i2t_attention_mask, get_t2t_attention_mask
from rudolph.model import get_rudolph_model, ruDolphModel, FP16Module
from rudolph.pipelines import generate_codebooks, self_reranking_by_image, self_reranking_by_text, show, generate_captions, generate_texts from rudolph.pipelines import  zs_clf
import gradio as gr
from rudolph import utils
device = 'cuda'

model = get_rudolph_model('350M',  fp16=True, device='cuda')
tokenizer = get_tokenizer()
vae = get_vae(dwt=False).to(device)






# Download human-readable labels for ImageNet.



def classify_image(inp):
  print(type(inp))
  inp =  Image.fromarray(inp)
  texts = generate_captions(inp, tokenizer, model, vae, template=template, top_k=16, captions_num=1, bs=16, top_p=0.6, seed=43, temperature=0.8)

  
  return texts

image = gr.inputs.Image(shape=(128, 128))
label = gr.outputs.Label(num_top_classes=3)


iface = gr.Interface(fn=classify_image, inputs=image, outputs="text",examples=[
  ['b9c277a3.jpeg']])
iface.launch(share=True)