# Hindi Image Captioning Model

This is an encoder-decoder image captioning model made with VIT encoder and GPT2-Hindi as a decoder. This is a first attempt at using ViT + GPT2-Hindi for image captioning task. We used the Flickr8k Hindi Dataset, which is the translated version of the original Flickr8k Dataset, available on kaggle to train the model.

This model was trained using HuggingFace course community week, organized by Huggingface. Training were done on Kaggle Notebooks.

## How to use

Here is how to use this model to caption an image of the Flickr8k dataset:
```python
import torch
import requests
from PIL import Image
from transformers import ViTFeatureExtractor, AutoTokenizer, VisionEncoderDecoderModel

if torch.cuda.is_available():
    device = 'cuda'
else:
    device = 'cpu'

url = 'https://shorturl.at/fvxEQ'
image = Image.open(requests.get(url, stream=True).raw)

feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224')
tokenizer = AutoTokenizer.from_pretrained('surajp/gpt2-hindi')
model = VisionEncoderDecoderModel.from_pretrained('team-indain-image-caption/hindi-image-captioning').to(device)

#Inference
sample = feature_extractor(image, return_tensors="pt").pixel_values.to(device)
clean_text = lambda x: x.replace('<|endoftext|>','').split('\n')[0]

caption_ids = model.generate(sample, max_length = 50)[0]
caption_text = clean_text(tokenizer.decode(caption_ids))
print(caption_text)
```