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import gradio as gr
from models.blip_vqa import blip_vqa
import torch
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
image_size = 480
transform = transforms.Compose([
transforms.Resize((image_size,image_size),interpolation=InterpolationMode.BICUBIC),
transforms.ToTensor(),
transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
])
model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'
model = blip_vqa(pretrained=model_url, image_size=image_size, vit='base')
model.eval()
model = model.to(device)
def pool_alarm(raw_image):
question = 'there is someone in the pool?'
image = transform(raw_image).unsqueeze(0).to(device)
with torch.no_grad():
answer = model(image, question, train=False, inference='generate')
return 'answer: ' + answer[0]
input = gr.inputs.Image(type='pil')
output = gr.outputs.Textbox()
examples = ['alarm.jpeg', 'alarm1.jpeg', 'walk.jpeg']
title = "Pool Alarm"
description = "Using visual question answering to check if there is someone in the swimming pool"
intf = gr.Interface(fn=pool_alarm, inputs=input, outputs=output, examples=examples,
title=title, description=description).launch() |