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# -*- coding: utf-8 -*-
"""Listed_Intern.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1QMirZa5iTv4ryooNXeVJXp8K9sHVmuDd
"""
#!pip install 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("google/vit-base-patch16-224-in21k")
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(images):
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)
preds=[pred.strip() for pred in preds]
return preds
#predict_step(["/content/Image1.png"])
#!pip install gradio
import gradio as gr
inputs=[
gr.inputs.Image(type="pil", label="ORiginal Image")
]
outputs=[
gr.outputs.Textbox(label="Caption")
]
title="Image Captioning"
description="AI based Caption generator"
article = "<a href = 'https://huggingface.co/nlpconnect/vit-gpt2-image-captioning'>Model Repo hugging face model hub</a>"
examples = [
["Image1.png","Image2.png"]
]
gr.Interface(
predict_step,
inputs,
outputs,
title=title,
description=description,
article=article,
examples=examples,
theme="huggingFace"
).launch(debug=True, enable_queue=True) |