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from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer | |
import torch | |
from PIL import Image | |
model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning") | |
feature_extractor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning") | |
tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning") | |
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
model.to(device) | |
max_length = 16 | |
num_beams = 4 | |
gen_kwargs = {"max_length": max_length, "num_beams": num_beams} | |
def image_to_text(image_paths): | |
images=[image_paths] | |
pixel_values = feature_extractor(images=images, return_tensors="pt").pixel_values | |
pixel_values = pixel_values.to(device) | |
output_ids = model.generate(pixel_values, **gen_kwargs) | |
preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True) | |
preds = [pred.strip() for pred in preds] | |
return preds[0] | |
title = "" | |
description = "" | |
interface = gr.Interface( | |
fn=image_to_text, | |
inputs=gr.inputs.Image(type="pil"), | |
outputs=gr.Textbox(), | |
title=title, | |
description=description, | |
enable_queue=True | |
) | |
interface.launch(debug=True) |