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8de41e5 cce011e 8de41e5 26a33b7 cce011e 8de41e5 26a33b7 8de41e5 26a33b7 cce011e 26a33b7 8de41e5 26a33b7 8de41e5 26a33b7 cce011e 8de41e5 cce011e 8de41e5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | import gradio as gr
from PIL import Image
import os
import torch
from model import ClassifierModel
from typing import List, Dict, Union
class GradioApp:
def __init__(self) -> None:
self.models: Dict[str, Union[str, ClassifierModel]] = {
'Custom': 'models/my_vit.pth',
'Pretrained': 'models/pretrained_vit.pth'
}
with open('classname.txt') as f:
self.classes: List[str] = [line.strip() for line in f.readlines()]
def predict(self, img_file: str, model_name: str) -> Dict[str, float]:
# Lazy loading of models
if isinstance(self.models[model_name], str):
self.models[model_name] = torch.load(self.models[model_name], map_location='cpu')
img = torch.unsqueeze(self.models[model_name].val_transform(Image.open(img_file)), 0)
with torch.inference_mode():
preds = torch.softmax(self.models[model_name](img), dim=1)[0].numpy()
return dict(zip(self.classes, preds))
def launch(self):
dataset_url = 'https://www.kaggle.com/datasets/marquis03/bean-leaf-lesions-classification/data'
github_repo_url = 'https://github.com/i4ata/TransformerClassification'
examples_list = [['examples/' + example] for example in os.listdir('examples')]
demo = gr.Interface(
fn=self.predict,
inputs=[
gr.Image(type='filepath', label='Input image to classify'),
gr.Radio(choices=('Custom', 'Pretrained'), label='Available models')
],
outputs=gr.Label(num_top_classes=3, label='Model predictions'),
title='Plants Diseases Classification',
description=f'This model performs classification on images of leaves that are either healthy, \
have bean rust, or have an angular leaf spot. A vision transformer neural network architecture is used. \
The dataset can be downloaded from [Kaggle]({dataset_url}) and the source code is on [GitHub]({github_repo_url}).',
examples=examples_list
)
demo.launch()
if __name__ == '__main__':
app = GradioApp()
app.launch()
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