app.py

import gradio as gr
import timm
import torch
from torchvision import transforms
model = timm.create_model("mobileone_s2", pretrained = False)
model.head.fc = torch.nn.Linear(model.head.fc.in_features,3)
data_transforms = transforms.Compose(timm.data.create_transform(**timm.data.resolve_data_config(model.pretrained_cfg)).transforms)
model.load_state_dict(torch.load("olive-classifier.pth", map_location=torch.device('cpu'), weights_only=True))
model.eval()

categories = ("Aculus Olearius", "Healthy", "Peacock Spot")


def classify_health(input_img):
    input_img = transforms.ToTensor()(input_img)
    with torch.no_grad():
        image = data_transforms(input_img).unsqueeze(0)
        output = model(image)
        probs = torch.nn.functional.softmax(output, dim=1)
        idx = probs.argmax(dim=1)
    return dict(zip(categories, map(float, probs[0])))


labels = gr.Label()
examples = [
    "examples/healthy.jpg",
    "examples/aculus_2.jpg",
    "examples/peacock_3.jpg",
]
demo = gr.Interface(
    classify_health,
    inputs=gr.Image(height=224, width=224),
    outputs=labels,
    examples=examples,
)
demo.launch(inline=False)