app.py

import os
import torch
from PIL import Image
from torchvision import transforms
import gradio as gr
#https://huggingface.co/spaces/yuhe6/final_project/blob/main/Net_Rotate9.pth
#os.system("wget https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt")

#model = torch.hub.load('huawei-noah/ghostnet', 'ghostnet_1x', pretrained=True)
#model = torch.jit.load('https://huggingface.co/spaces/yuhe6/final_project/blob/main/Net_Rotate9.pth').eval().to(device)


model = torch.jit.load('Net2_Blur_jit.pt', map_location = torch.device('cpu'))
model.eval()


#torch.hub.download_url_to_file('https://huggingface.co/spaces/yuhe6/final_project/blob/main/Net_Rotate9.pth', '/tmp/temporary_file')
#model = torch.hub.load('/tmp', 'temporary_file', pretrained=True)

#model.eval()
# Download an example image from the pytorch website
torch.hub.download_url_to_file("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")

def inference(input_image):
    preprocess = transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    ])
    input_tensor = preprocess(input_image)
    input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model

    # move the input and model to GPU for speed if available
    if torch.cuda.is_available():
        input_batch = input_batch.to('cuda')
        model.to('cuda')

    with torch.no_grad():
        output = model(input_batch)
    # The output has unnormalized scores. To get probabilities, you can run a softmax on it.
    probabilities = torch.nn.functional.softmax(output[0], dim=0)

    # Read the categories
    with open("dog_cat.txt", "r") as f:
        categories = [s.strip() for s in f.readlines()]
    with open("dog_cat.txt", "r") as f:
        categories = [s.strip() for s in f.readlines()]
    # Show top categories per image
    top5_prob, top5_catid = torch.topk(probabilities, 1)
    result = {}
    for i in range(top5_prob.size(0)):
        result[categories[top5_catid[i]]] = top5_prob[i].item()
    return result

inputs = gr.inputs.Image(type='pil')
outputs = gr.outputs.Label(type="confidences",num_top_classes=1)

title = "GHOSTNET"
description = "Gradio demo for GHOSTNET, Efficient networks by generating more features from cheap operations. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1911.11907'>GhostNet: More Features from Cheap Operations</a> | <a href='https://github.com/huawei-noah/CV-Backbones'>Github Repo</a></p>"

examples = [
            ['dog.jpg']
]
gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False).launch()