functions.py

from PIL import Image
from tqdm import tqdm
from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer
import torch 
from PIL import Image
from tqdm import tqdm
import urllib.request
from itertools import cycle
import os

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
num_return_sequences = 3  # Number of captions to generate for each image
gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "num_return_sequences": num_return_sequences}


def predict_step(images_list,is_url):
    images = []
    for image in tqdm(images_list):
        if is_url:
            urllib.request.urlretrieve(image, "file.jpg")
            i_image = Image.open("file.jpg")
            
        else:
            i_image = Image.open(image)

        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, clean_up_tokenization_spaces=True)
    preds = [pred.strip() for pred in preds]
    if is_url:
        os.remove('file.jpg')
    return preds