app.py

import torch
from torchvision import transforms
import gradio as gr
import timm

# Read the categories
with open("labels.txt", "r") as f:
    categories = [s.strip() for s in f.readlines()]

model_ft = timm.create_model('vit_base_patch16_224_in21k', pretrained=True, num_classes=len(categories))
model_path = 'best_cpu.pt'
model_ft.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
model_ft.eval()


# Download an example image from the pytorch website
# torch.hub.download_url_to_file("https://iiif.dl.itc.u-tokyo.ac.jp/iiif/genji/TIFF/A00_6587/01/01_0001.tif/full/,400/0/default.jpg", "examples/other.jpg")
# torch.hub.download_url_to_file("https://iiif.dl.itc.u-tokyo.ac.jp/iiif/genji/TIFF/A00_6587/01/01_0002.tif/full/,400/0/default.jpg", "examples/front.jpg")
# torch.hub.download_url_to_file("https://iiif.dl.itc.u-tokyo.ac.jp/iiif/genji/TIFF/A00_6587/01/01_0003.tif/full/,400/0/default.jpg", "examples/page.jpg")
# torch.hub.download_url_to_file("https://iiif.dl.itc.u-tokyo.ac.jp/iiif/genji/TIFF/A00_6587/01/01_0009.tif/full/,400/0/default.jpg", "examples/page2.jpg")
# torch.hub.download_url_to_file("https://iiif.dl.itc.u-tokyo.ac.jp/iiif/genji/TIFF/A00_6587/01/01_0032.tif/full/,400/0/default.jpg", "examples/back.jpg")


def inference(input_image):
    preprocess = transforms.Compose([
        transforms.Resize(224),
        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_ft.to('cuda')

    with torch.no_grad():
        output = model_ft(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)

    
    # Show top categories per image
    top5_prob, top5_catid = torch.topk(probabilities, len(categories))
    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=len(categories))

title = "表紙・裏表紙・その他のページの分類"
description = "Vision Transformerを用いた表紙・裏表紙・その他のページの分類モデルです。"

article = "<p style='text-align: center'>次のデータセットを使用しました。<a href='' target='_blank'>あああ</a></p>"

examples = [
    ['examples/other.jpg'],
    ['examples/front.jpg'],
    ["examples/page.jpg"],
    ["examples/page2.jpg"],
    ["examples/back.jpg"]
]

gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False).launch()