app.py

# -*- coding: utf-8 -*-
"""caption.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/17BgQi1eU254RKp6BKOdC-Kfr1LqIwKmj

## Image Caption Generator 


In Colab, Pytorch comes preinstalled and same goes with PIL for Image. You will only need to install **transformers** from Huggingface.
"""

#!pip install transformers

#from google.colab import drive
#drive.mount('/content/drive')
#import transformers
from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer
import torch
from PIL import Image

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)




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_step1(image_paths):
    i_image = PIL.Image.open(image_paths)
    if i_image.mode != "RGB":
        i_image = i_image.convert(mode="RGB")

    pixel_values = feature_extractor(images=i_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


import gradio as gr

inputs = [
    gr.inputs.Image(type="filepath", label="Original Image")
]

outputs = [
    gr.outputs.Textbox(label = 'Caption')
]

title = "Image Captioning"
description = "ViT and GPT2 are used to generate Image Caption for the uploaded image."
article = " <a href='https://huggingface.co/nlpconnect/vit-gpt2-image-captioning'>Model Repo on Hugging Face Model Hub</a>"
examples = [
    ["horses.png"],
    ['persons.png'],
    ['football_player.png']

]



gr.Interface(
    predict_step,
    inputs,
    outputs,
    title=title,
    description=description,
    article=article,
    examples=examples,
    theme="huggingface",
).launch(debug=True, enable_queue=True)