First commit

app.py

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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
+learn = 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()

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dcdc0c651110f6b0e233ff1567d0ed8c0133c7ef067c58933da96f55284dc928
+size 87591933

requirements.txt

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+fastai
+scikit-image