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from fastai import *
from fastai.vision.all import *
from fastai.vision.widgets import *
from PIL import ImageFile
import gradio as gr
root='paintings/data/'
dblock = DataBlock(
blocks=(ImageBlock, CategoryBlock),
get_items=get_image_files,
splitter=RandomSplitter(valid_pct=0.1, seed=42),
get_y=parent_label,
item_tfms=Resize(128))
dataloader=dblock.dataloaders(root, bs=8)
dblock = dblock.new(item_tfms=Resize(224, ResizeMethod.Squish), batch_tfms=aug_transforms(do_flip=True,
flip_vert=True,
max_rotate=10,
max_lighting=0.1,
))
dls = dblock.dataloaders(root)
model = cnn_learner(dls, densenet201, metrics=[error_rate,accuracy])
model.load('modelv2')
def recognize_image(image):
pred, idx, probs = model.predict(image)
return dict(zip(model.dls.vocab, map(float, probs)))
image = gr.inputs.Image(shape=(224,224))
label = gr.outputs.Label(num_top_classes=10)
examples = [
'test_img/still-life.jpeg',
'test_img/landscape.jpeg',
'test_img/abstract2.jpg',
]
iface = gr.Interface(fn=recognize_image, inputs=image, outputs=label, examples=examples)
iface.launch(inline=False)
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