Upload fruit_classifier_app.py

fruit_classifier_app.py

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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(debug = True, share=True)
+