app.py

import torch
import re
import gradio as gr
from transformers import AutoTokenizer,ViTFeatureExtractor,VisionEncoderDecoderModel

device = 'cpu'
encoder_checkpoint = 'google/vit-base-patch16-224'
decoder_checkpoint = 'gpt2'
model_checkpoint = 'nlpconnect/vit-gpt2-image-captioning'
feature_extractor = ViTFeatureExtractor.from_pretrained(encoder_checkpoint)
model = VisionEncoderDecoderModel.from_pretrained(model_checkpoint).to(device)


def predict(image,max_length=64,num_beams=4):
  image = image.convert('RGB')
  image = feature_extractor(image,return_tensor='pt').pixel_values.to(device)
  clean_text = lambda x: x.replace('<|endoftext|>','').split('\n')[0]  
  caption_ids = model.generate(image, max_length = max_length)[0]
  caption_text = clean_text(tokenizer.decode(caption_ids))
  return caption_text
  

input = gr.inputs.Image(label='Image to generate caption',type = 'pil', optional=False)
output = gr.outputs.Textbox(type="auto",label="Caption")

article = "This is a Image captioning model created by Shreyas Dixit"
title = "Image Captioning"

interface = gr.Interface(
        fn=predict,
        inputs = input,
        theme="grass",
        outputs=output,
        examples = examples,
        title=title,
        description=article,
    )
interface.launch(debug=True)