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73717cf 95fe60f 973f58e 95fe60f 1da6394 95fe60f bfd27d3 95fe60f 973f58e 95fe60f 973f58e b48fcfc 95fe60f a41d0df 4b09ce1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | from PIL import Image
from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, PreTrainedTokenizerFast
import requests
model = VisionEncoderDecoderModel.from_pretrained("Zayn/vit2distilgpt2")
vit_feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k")
tokenizer = PreTrainedTokenizerFast.from_pretrained("distilgpt2")
def vit2distilgpt2(img):
pixel_values = vit_feature_extractor(images=img, return_tensors="pt").pixel_values
encoder_outputs = model.generate(pixel_values.to('cpu'),num_beams=5)
generated_sentences = tokenizer.batch_decode(encoder_outputs, skip_special_tokens =True)
return(generated_sentences[0].split('.')[0])
import gradio as gr
inputs = [
gr.inputs.Image(type="pil", label = "Original Image")
]
outputs = [
gr.outputs.Textbox(label = 'Caption')
]
title = "Image Captioning using Vision Transformer and GPT-2"
description = "Developed by Zayn"
article = "< a href='https://huggingface.co/Zayn/vit2distilgpt2'>Hugging Face AI Community</a>"
examples = [
["car.jpg"]
]
gr.Interface(
vit2distilgpt2,
inputs,
outputs,
title = title,
description = description,
article = article,
examples = examples,
theme = "huggingface",
).launch(debug=True,enable_queue=True) |