script.py

from transformers import VisionEncoderDecoderModel
from transformers import ViTFeatureExtractor
from transformers import AutoTokenizer
import torch
from PIL import Image
import warnings


warnings.filterwarnings('ignore')


model = VisionEncoderDecoderModel.from_pretrained(
    "nlpconnect/vit-gpt2-image-captioning")
feature_extractor = ViTFeatureExtractor.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 predict_step(image_paths):
    images = []
    for image_path in image_paths:
        i_image = Image.open(image_path)
        if i_image.mode != "RGB":
            i_image = i_image.convert(mode="RGB")

        images.append(i_image)

    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