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# -*- coding: utf-8 -*-
"""app
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1Uvn7yZCyrMpOYNPb7K0G45tQZJVx8LyX
"""
from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer
import gradio as gr
#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)
max_length = 16
num_beams = 4
gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
def predict_step(image):
# images = []
# for image_path in image_paths:
# i_image = Image.open(image_path)
# if i_image.mode != "RGB":
# i_image = i_image.convert(mode="RGB")
# images.append(i_image)
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
inputs = [ gr.inputs.Image(type = 'pil', label = 'Original Image')]
outputs = [ gr.outputs.Textbox(label = 'Caption')]
title = 'Image Captioning using ViT + GPT2'
description = 'ViT and GPT2 are used here to generate Image Caption for the user uploaded image.'
article = " <a href=' https://huggingface.co/sachin/vit2distilgpt2 '>Model Repository on Hugging Face Model Hub</a>"
gr.Interface(
predict_step,
inputs, outputs,
title = title,
description = description,
article = article,
theme = 'huggingface'
).launch(debug = True, enable_queue = True)
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