Update app.py

app.py

@@ -1,7 +1,109 @@
-import gradio as gr
+!pip install -Uqq fastbook duckduckgo_search
+!pip install jupyter_contrib_nbextensions
+!pip install nbdev
 
-def greet(name):
-    return "Hello " + name + "!!"
+import fastbook
+fastbook.setup_book()
+from fastbook import *
+from fastai.vision.widgets import *
+key = os.environ.get('AZURE_SEARCH_KEY', 'XXX')
+search_images_bing
+search_images_ddg
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+doc(search_images_ddg)
+def search_images(term):
+  url ='https://duckduckgo.com/'
+  res = urlread(url,data={'q':term})
+  searchObj=re.search(r'vqd=([\d-]+)\&',res)
+  requestUrl = url + 'i.js'
+  params = dict(l='us-en',o='json', q= term, vqd=searchObj.group(1), f=',,,', p='1', v7exp='a')
+  urls,data = set(),{'next':1}
+  while len(urls)<max_images and 'next' in data:
+    data = urljson(requestUrl,data=params)
+    urls.update(L(data['results']).itemgot('image'))
+    requestUrl = url + data['next']
+    time.sleep(0.2)
+  return L(urls)[:max_images]
+ims = ['http://3.bp.blogspot.com/-S1scRCkI3vY/UHzV2kucsPI/AAAAAAAAA-k/YQ5UzHEm9Ss/s1600/Grizzly%2BBear%2BWildlife.jpg']
+dest = 'images/grizzly.jpg'
+download_url(ims[0], dest)
+im = Image.open(dest)
+im.to_thumb(128,128)
+bear_types = 'grizzly','black','teddy'
+path = Path('bears')
+
+doc(Path)
+if not path.exists():
+    path.mkdir()
+    for o in bear_types:
+        dest = (path/o)
+        dest.mkdir(exist_ok=True)
+        results = search_images_ddg(f'{o} bear', 150)
+        print(results)
+        download_images(dest, urls=results)
+fns = get_image_files(path)
+fns
+failed = verify_images(fns)
+failed
+#??verify_images
+failed = verify_images(fns)
+failed
+class DataLoaders(GetAttr):
+    def __init__(self, *loaders): self.loaders = loaders
+    def __getitem__(self, i): return self.loaders[i]
+    train,valid = add_props(lambda i,self: self[i])
+bears = DataBlock(
+    blocks=(ImageBlock, CategoryBlock),
+    get_items=get_image_files,
+    splitter=RandomSplitter(valid_pct=0.2, seed=42),
+    get_y=parent_label,
+    item_tfms=Resize(128))
+
+doc(DataBlock)
+# Create a DataLoaders object from source
+dls = bears.dataloaders(path)
+dls.valid.show_batch(max_n=6, nrows=2)
+doc(bears.new)
+bears = bears.new(item_tfms=Resize(128, ResizeMethod.Squish))
+dls = bears.dataloaders(path)
+dls.valid.show_batch(max_n=6, nrows=1)
+doc(bears.new)
+bears = bears.new(item_tfms=Resize(128, ResizeMethod.Pad, pad_mode='zeros'))
+dls = bears.dataloaders(path)
+dls.valid.show_batch(max_n=6, nrows=2)
+bears = bears.new(item_tfms=RandomResizedCrop(128, min_scale=0.3))
+dls = bears.dataloaders(path)
+
+doc(dls.train.show_batch)
+
+# unique
+dls.train.show_batch(max_n=3, nrows=1)
+dls.train.show_batch(max_n=3, nrows=1, unique=True)
+bears = bears.new(item_tfms=Resize(128), batch_tfms=aug_transforms(mult=2))
+dls = bears.dataloaders(path)
+dls.train.show_batch(max_n=8, nrows=2, unique=True)
+bears = bears.new(
+    item_tfms=RandomResizedCrop(128, min_scale=0.3),
+    batch_tfms=aug_transforms())
+
+
+dls = bears.dataloaders(path)
+
+#dls.train.show_batch(max_n=8, nrows=2, unique=True)
+learn = vision_learner(dls, resnet18, metrics=error_rate)
+learn.fine_tune(4)
+interp = ClassificationInterpretation.from_learner(learn)
+interp.plot_confusion_matrix()
+interp.plot_top_losses(3, nrows=2)
+
+doc(interp.plot_top_losses)
+??interp.plot_top_losses
+
+# According to the matplot docs it’s the image size in inches.
+interp.plot_top_losses(3, nrows=2, figsize=(10,10))
+#doc(ImageClassifierCleaner)
+cleaner = ImageClassifierCleaner(learn)
+cleaner
+for idx in cleaner.delete(): cleaner.fns[idx].unlink()
+for idx,cat in cleaner.change(): shutil.move(str(cleaner.fns[idx]), path/cat)
+for idx in cleaner.delete(): cleaner.fns[idx].unlink()