app.py

import os
import gradio as gr
from PIL import Image
import requests

from transformers import ViTFeatureExtractor
feature_extractor = ViTFeatureExtractor()
# or, to load one that corresponds to a checkpoint on the hub:
feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224")

from transformers import VisionEncoderDecoderModel
# initialize a vit-bert from a pretrained ViT and a pretrained BERT model. Note that the cross-attention layers will be randomly initialized
model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained(
    "google/vit-base-patch16-224-in21k", "bert-base-uncased"
)
# saving model after fine-tuning
model.save_pretrained("./vit-bert")
# load fine-tuned model
model = VisionEncoderDecoderModel.from_pretrained("./vit-bert")

#####################
from transformers import AutoTokenizer
repo_name = "ydshieh/vit-gpt2-coco-en"
feature_extractor = ViTFeatureExtractor.from_pretrained(repo_name)
tokenizer = AutoTokenizer.from_pretrained(repo_name)
model = VisionEncoderDecoderModel.from_pretrained(repo_name)

def get_quote(image):
    
    ##############
    pixel_values = feature_extractor(image, return_tensors="pt").pixel_values
    # autoregressively generate text (using beam search or other decoding strategy)
    generated_ids = model.generate(pixel_values, max_length=16, num_beams=4, return_dict_in_generate=True)
    
    ################
    # decode into text
    preds = tokenizer.batch_decode(generated_ids[0], skip_special_tokens=True)
    preds = [pred.strip() for pred in preds]
    return preds

#1: Text to Speech
title = "Sentence, listing all the items present in the image file"
  
demo = gr.Interface(fn=get_quote, inputs=gr.inputs.Image(type="pil"), outputs=['text'],title = title, description = "Upload an image file and get text from it" ,cache_examples=False, enable_queue=True).launch()
if __name__ == "__main__":
    
    demo.launch(debug=True, cache_examples=True)