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import os
import gradio as gr
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_1):
#reader = easyocr.Reader(['en'])
image = Image.open(image_1,mode = 'r')
##############
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]
#print(preds)
return preds
#1: Text to Speech
#import gradio as gr
title = "Image to text generation"
demo = gr.Interface(fn=get_quote, inputs = "image", outputs=['text'],title = title, description = "Import an image file and get text from it" ,cache_examples=False).launch(debug = True)
if __name__ == "__main__":
demo.launch() |