| 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) |