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from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, PreTrainedTokenizerFast
model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
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, num_return_sequences=3)
generated_sentences = tokenizer.batch_decode(encoder_outputs, skip_special_tokens=True)
return generated_sentences
import gradio as gr
inputs = [
gr.inputs.Image(type="pil", label="Original Images")
]
outputs = [
gr.outputs.Textbox(label="Caption 1"),
gr.outputs.Textbox(label="Caption 2"),
gr.outputs.Textbox(label="Caption 3")
]
title = "Image Captioning using ViT + GPT2"
description = "ViT and GPT2 are used to generate Image Caption for the uploaded image. COCO DataSet is used for Training"
examples = [
["Image1.png"],
["Image2.png"],
["Image3.png"]
]
gr.Interface(
vit2distilgpt2,
inputs,
outputs,
title=title,
description=description,
examples=examples,
theme="huggingface",
).launch(debug=True, enable_queue=True)
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