app.py

import torch
import torchvision.transforms as T
from timm import create_model
from safetensors.torch import load_model
import numpy as np
from pathlib import Path
import gradio as gr

examples = Path('./examples').glob('*')
examples = list(map(str,examples))

valid_tfms = T.Compose([
    T.Resize((224,224)),
    T.ToTensor(),
    T.Normalize(
        mean = (0.5,0.5,0.5),
         std = (0.5,0.5,0.5)
    )
])


model_path = 'model/swin_s3_base_224-pascal/model.safetensors'
model = create_model(
        'swin_s3_base_224',
        pretrained = False,
        num_classes = 20
)
load_model(model,model_path)
model.eval()

class_names = [
    "Aeroplane","Bicycle","Bird","Boat","Bottle",
    "Bus","Car","Cat","Chair","Cow","Diningtable",
    "Dog","Horse","Motorbike","Person",
    "Potted plant","Sheep","Sofa","Train","Tv/monitor"
]

label2id = {c:idx for idx,c in enumerate(class_names)}
id2label = {idx:c for idx,c in enumerate(class_names)}


def predict(im):
    im = valid_tfms(im).unsqueeze(0)
    with torch.no_grad():
        logits = model(im)
    
    confidences = logits.sigmoid().flatten()
    predictions = confidences > 0.5
    predictions = predictions.float().numpy()
    pred_labels = np.where(predictions==1)[0]
    confidences = confidences[pred_labels].numpy()
    pred_labels = [id2label[label] for label in pred_labels]
    outputs = {l:c for l,c in zip(pred_labels, confidences)}
    return outputs

gr.Interface(fn=predict,
             inputs=gr.Image(type="pil"),
             outputs=gr.Label(label='the image contains:'),
             examples=examples).queue().launch()