Upload cbc_encryption.py

cbc_encryption.py

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+# -*- coding: utf-8 -*-
+"""car 4ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1DXku3LecVZkncfgprvC10z3cbVXU2Tvr
+"""
+
+#hide
+! [ -e /content ] && pip install -Uqq fastbook
+import fastbook
+fastbook.setup_book()
+
+from fastbook import *
+from fastai.vision.all import *
+from fastai.vision.widgets import *
+
+path = Path('/content/gdrive/MyDrive/cars 4')
+dblock = DataBlock(blocks = (ImageBlock, CategoryBlock),
+                 get_items=get_image_files,
+                 splitter=RandomSplitter(seed=42),
+                 get_y=parent_label,
+                 item_tfms=Resize(460),
+                 batch_tfms=aug_transforms(size=224, min_scale=0.75))
+dls = dblock.dataloaders(path)
+
+dls.show_batch(max_n=15)
+
+learn = vision_learner(dls, resnet50, metrics=accuracy)
+learn.fine_tune(200)
+
+learn.export()
+
+!pip install gradio
+
+
+from fastai.vision.all import *
+import gradio as gr
+import skimage
+
+
+learn_inf = load_learner('export.pkl')
+labels = learn_inf.dls.vocab
+
+
+def predict(img):
+    img = PILImage.create(img)
+    pred,pred_idx,probs = learn_inf.predict(img)
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+
+gr.Interface(fn=predict, inputs=gr.Image(), outputs=gr.Label(num_top_classes=3), title = "cars identifier",
+description = "cars identifier" ).launch(share=True)