Upload app.py

app.py

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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)