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import torch
import gradio as gr
from PIL import Image
from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer
def main():
model = VisionEncoderDecoderModel.from_pretrained("OmkarShidore/scene-caption")
feature_extractor = ViTImageProcessor.from_pretrained("OmkarShidore/scene-caption")
tokenizer = AutoTokenizer.from_pretrained("OmkarShidore/scene-caption")
max_length = 16
num_beams = 4
gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
def predict(image):
#image = Image.open(image_path)
image = image.convert(mode="RGB")
pixel_values = feature_extractor(images=[image], return_tensors="pt").pixel_values
pixel_values = pixel_values.to(device="cpu")
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[0]
#built interface with gradio to test the function
imagein = gr.components.Image(label='Scene Image', type='pil')
output = gr.components.Textbox()
gui = gr.Interface(fn=predict, inputs=imagein, outputs=[output])
gr.Interface(fn=predict,
inputs=imagein,
outputs=output,
title='Image To Text- Scene Description',
description="<html> <body> <h3>Hugging Face: <a href='https://huggingface.co/OmkarShidore/scene-caption'>OmkarShidore/scene-caption</a></h3><h3>Git: <a href='https://github.com/OmkarShidore/ImageToText-SceneDescription'>OmkarShidore/ImageToText-SceneDescription</a></h3> </body></html>",
examples=["./data/car.jpg", "./data/gsd.jpg", "./data/highway.jpg"],
theme=gr.themes.Default()
).launch()
if __name__ == '__main__':
main() |