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| # -*- coding: utf-8 -*- | |
| """Fruit_Classifier_app.ipynb | |
| Automatically generated by Colaboratory. | |
| Original file is located at | |
| https://colab.research.google.com/drive/1wFmOPbrpLNAJxJsRfdiNoE_r1f8brR6M | |
| ## FRUIT CLASSIFICATION APP | |
| """ | |
| !pip install gradio | |
| !pip install -U albumentations | |
| !pip install -U albumentations | |
| !pip install opencv-python==4.5.4.60 | |
| !pip install timm==0.6.2.dev0 | |
| #Start by connecting gdrive into the google colab | |
| from google.colab import drive | |
| drive.mount('/content/gdrive') | |
| path = '/content/gdrive/MyDrive/Fruit_Project/' | |
| import gradio as gr | |
| from fastai.vision.all import * | |
| import skimage | |
| import pathlib | |
| from PIL import Image | |
| import albumentations | |
| from albumentations.pytorch import ToTensorV2 | |
| import timm | |
| plt = platform.system() | |
| if plt == 'Linux': | |
| pathlib.WindowsPath = pathlib.PosixPath | |
| # !unzip -o -q /content/gdrive/MyDrive/sign_prediction/ModImages -d Images/ | |
| class AlbumentationsTransform (RandTransform): | |
| split_idx,order=None,2 | |
| def __init__(self, train_aug, valid_aug): store_attr() | |
| def before_call(self, b, split_idx): | |
| self.idx = split_idx | |
| def encodes(self, img: PILImage): | |
| if self.idx == 0: | |
| aug_img = self.train_aug(image=np.array(img))['image'] | |
| else: | |
| aug_img = self.valid_aug(image=np.array(img))['image'] | |
| return PILImage.create(aug_img) | |
| def get_valid_aug(): return albumentations.Compose([ | |
| albumentations.Resize(224, 224), | |
| ], p=1.0) | |
| learn = load_learner(path + 'fruit_model_v2.pkl') | |
| labels = learn.dls.vocab | |
| def predict(img): | |
| pred,pred_idx,probs = learn.predict(img) | |
| return {labels[i]: float(probs[i]) for i in range(len(labels))} | |
| # predict('/content/gdrive/MyDrive/Fruit_Project/Onion.jpg') | |
| title = "Fruit and Vegetation Classifier" | |
| description = '''A simple app to classify various fruits and vegetables ''' | |
| examples = [[path + 'Onion.jpg'], | |
| [path + 'orange.jpg'], | |
| [path + 'plum.jpg'], | |
| [path + 'tomato.jpg'], | |
| [path + 'banana.jpg']] | |
| enable_queue = True | |
| gr.Interface (fn= predict, | |
| inputs=gr.inputs.Image(shape = (224,224)), | |
| outputs= gr.outputs.Label(num_top_classes =3), | |
| title = title, | |
| description = description, | |
| examples = examples, | |
| flagging_options=["Incorrect Prediction"], | |
| enable_queue = enable_queue).launch() | |