from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer
import torch
from PIL import Image

model = VisionEncoderDecoderModel.from_pretrained("vit-gpt2-image-captioning")
feature_extractor = ViTImageProcessor.from_pretrained("vit-gpt2-image-captioning")
tokenizer = AutoTokenizer.from_pretrained("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 generate(image):
    if image.mode != "RGB":
      image = image.convert(mode="RGB")

    pixel_values = feature_extractor(images=[image], 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]

def openAndGenerate(image_path):
   return generate(Image.open(image_path))