Upload .py of the model

190055201_matheucvp_matheus_calixto_vaz_pinheiro.py

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+# -*- coding: utf-8 -*-
+"""190055201_matheucvp_Matheus Calixto Vaz Pinheiro.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1oftNwxIgliiduGxHmweISJfFlV4ZHma-
+"""
+
+!pip install --upgrade pip
+!pip install -Uqq duckduckgo_search
+!pip install ipywidgets
+!pip install voila
+!jupyter serverextension enable --sys-prefix voila
+
+from duckduckgo_search import ddg_images
+from fastcore.all import *
+from fastdownload import download_url
+from fastai.vision.all import *
+from time import sleep
+
+def search_images(term, max_images=200): return L(ddg_images(term, max_results=max_images)).itemgot('image')
+
+urls = search_images('strawberry', max_images=1)
+urls[0]
+
+dest = 'strawberry.jpg'
+download_url(urls[0], dest, show_progress=False)
+
+im = Image.open(dest)
+im.to_thumb(256,256)
+
+download_url(search_images('spoiled strawberry', max_images=1)[0], 'spoiled_strawberry.jpg', show_progress=False)
+Image.open('spoiled_strawberry.jpg').to_thumb(256,256)
+
+searches = 'ripe', 'spoiled'
+path = Path('ripe_or_spoiled')
+
+for o in searches:
+  dest = (path/o)
+  dest.mkdir(exist_ok=True, parents=True)
+  download_images(dest, urls=search_images(f'{o} strawberry fruit', 600))
+  sleep(10)
+  resize_images(path/o, max_size=400, dest=path/o)
+
+failed = verify_images(get_image_files(path))
+failed.map(Path.unlink)
+len(failed)
+
+strawberrys = DataBlock(
+    blocks=(ImageBlock, CategoryBlock),
+    get_items=get_image_files,
+    splitter=RandomSplitter(valid_pct=0.2, seed=42),
+    get_y=parent_label,
+    item_tfms=Resize(264)
+)
+
+dls = strawberrys.dataloaders(path)
+
+dls.valid.show_batch(max_n=4, nrows=1)
+
+strawberrys = strawberrys.new(item_tfms=Resize(128, ResizeMethod.Squish))
+dls = strawberrys.dataloaders(path)
+dls.valid.show_batch(max_n=4, nrows=1)
+
+strawberrys = strawberrys.new(item_tfms=Resize(128, ResizeMethod.Pad, pad_mode='zeros'))
+dls = strawberrys.dataloaders(path)
+dls.valid.show_batch(max_n=4, nrows=1)
+
+strawberrys = strawberrys.new(item_tfms=RandomResizedCrop(128, min_scale=0.3))
+dls = strawberrys.dataloaders(path)
+dls.valid.show_batch(max_n=4, nrows=1, unique=True)
+
+strawberrys = strawberrys.new(item_tfms=Resize(128), batch_tfms=aug_transforms(mult=2))
+dls = strawberrys.dataloaders(path)
+dls.valid.show_batch(max_n=8, nrows=2, unique=True)
+
+strawberrys = strawberrys.new(
+    item_tfms=RandomResizedCrop(224, min_scale=0.5),
+    batch_tfms=aug_transforms())
+dls = strawberrys.dataloaders(path)
+
+learn = vision_learner(dls, resnet18, metrics=[error_rate, accuracy])
+learn.fine_tune(10)
+
+Learning_interpreter = ClassificationInterpretation.from_learner(learn)
+
+Learning_interpreter.plot_top_losses(12)
+
+Learning_interpreter.plot_confusion_matrix()
+
+from fastai.vision.widgets import *
+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)
+
+is_spoiled_or_ripe,_,probs = learn.predict(PILImage.create('strawberry.jpg'))
+print(f'this is a: {is_spoiled_or_ripe}.')
+print(f"Probability it's a ripe: {probs[0]:.4f}")
+print(f"Probability it's a spoiled: {probs[1]:.4f}")
+
+learn.export()
+
+path = Path()
+path.ls(file_exts='.pkl')
+
+learn_inf = load_learner(path/'export.pkl')
+
+learn_inf.predict('strawberry.jpg')
+
+learn_inf.dls.vocab
+
+btn_upload = widgets.FileUpload()
+btn_upload
+
+btn_upload = SimpleNamespace(data = ['strawberry.jpg'])
+
+img = PILImage.create(btn_upload.data[-1])
+
+out_pl = widgets.Output()
+out_pl.clear_output()
+with out_pl: display(img.to_thumb(256,256))
+out_pl
+
+pred, pred_idx,probs = learn_inf.predict(img)
+
+lbl_pred = widgets.Label()
+lbl_pred.value = f'Predition: {pred}; Probability:{probs[pred_idx]:.04f}'
+lbl_pred
+
+btn_run = widgets.Button(description='Classify')
+btn_run
+
+def on_click_classify(change):
+  img = PILImage.create(btn_upload.data[-1])
+  out_pl.clear_output()
+  with out_pl: display(img.to_thumb(256,256))
+  pred,pred_idx,probs = learn_inf.predict(img)
+  lbl_pred.value = f'Predition: {pred}; Probability:{probs[pred_idx]:.04f}'
+
+btn_run.on_click(on_click_classify)
+
+btn_upload = widgets.FileUpload()
+
+from ipywidgets import vbox
+
+Vbox([widgets.Label('Select your Strawberry!'), btn_upload, btn_run, out_pl, lbl_pred])