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import gradio as gr
from fastai.vision.all import *
import pathlib
plt = platform.system()
if plt == 'Linux': pathlib.WindowsPath = pathlib.PosixPath
def get_x(r):
return r['name']
def get_y(r):
return r['labels'].split(' ')
learner = load_learner('model.pkl')
labels = learner.dls.vocab
def bla(predicted):
ShirtLength = ('Crop_length', 'Regular_length', 'Long_length', 'ShirtLength_other')
ShirtNeck = ('Round_neck', 'Tailored_collar_neck', 'Turtle_neck', 'V_neck', 'ShirtNeck_other')
ShirtSleeveLength = ('Short_sleeve', 'Long_sleeve', 'Sleeveless', 'ShirtSleeveLength_other')
PatternPlacement = ('No_pattern', 'Pattern')
shirtlength_idx = [labels.o2i[s] for s in ShirtLength]
shirtneck_idx = [labels.o2i[s] for s in ShirtNeck]
shirtsleevelength_idx = [labels.o2i[s] for s in ShirtSleeveLength]
patternplacement_idx = [labels.o2i[s] for s in PatternPlacement]
shirtlength_pred = predicted[2][shirtlength_idx]
shirtneck_pred = predicted[2][shirtneck_idx]
shirtsleevelength_pred = predicted[2][shirtsleevelength_idx]
patternplacement_pred = predicted[2][patternplacement_idx]
val, ind = shirtlength_pred.sort(descending=True)
#l1 = {ShirtLength[i]: float(shirtlength_pred[i]) for i in ind}
l1 = {ShirtLength[ind[0]]: float(shirtlength_pred[ind[0]])}
val, ind = shirtneck_pred.sort(descending=True)
#l2 = {ShirtNeck[i]: float(shirtneck_pred[i]) for i in ind}
l2 = {ShirtNeck[ind[0]]: float(shirtneck_pred[ind[0]])}
val, ind = shirtsleevelength_pred.sort(descending=True)
#l3 = {ShirtSleeveLength[i]: float(shirtsleevelength_pred[i]) for i in ind}
l3 = {ShirtSleeveLength[ind[0]]: float(shirtsleevelength_pred[ind[0]])}
val, ind = patternplacement_pred.sort(descending=True)
#l4 = {PatternPlacement[i]: float(patternplacement_pred[i]) for i in ind}
l4 = {PatternPlacement[ind[0]]: float(patternplacement_pred[ind[0]])}
l1.update(l2)
l1.update(l3)
l1.update(l4)
return l1
def predict(img):
img = PILImage.create(img)
# pred,pred_idx,probs = learner.predict(img)
# return {labels[i]: float(probs[i]) for i in range(len(labels))}
pred = learner.predict(img)
return bla(pred)
title = "Multi-Class Classifier"
description = "Fasion multi-class classifier"
article="<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"
examples = ['demo1.jpg', 'demo2.jpg', 'demo3.jpg', 'demo4.jpg', 'demo5.jpg']
interpretation='default'
enable_queue=True
gr.Interface(fn=predict,
inputs=gr.inputs.Image(shape=(300, 300)),
outputs=gr.outputs.Label(),
title=title,
description=description,
article=article,
examples=examples,
interpretation=interpretation,
enable_queue=enable_queue).launch()
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