| # -*- coding: utf-8 -*- | |
| """Copy of dogs_cats.ipynb | |
| Automatically generated by Colaboratory. | |
| Original file is located at | |
| https://colab.research.google.com/drive/1pu75-TcRCtcDPFHn3zA6IqSsH8BM5yka | |
| ## Gradio Pets | |
| # New Section | |
| """ | |
| # !pip install -Uqq fastai | |
| #/ default_exp app | |
| from fastai.vision.all import * | |
| import gradio as gr | |
| def is_cat(x): return x[0].isupper() | |
| # path = untar_data(URLs.PETS)/'images' | |
| # dls = ImageDataLoaders.from_name_func('.', | |
| # get_image_files(path), valid_pct=0.2, seed=42, #giving | |
| # label_func=is_cat, | |
| # item_tfms=Resize(192)) | |
| # dls.show_batch() | |
| # learn = vision_learner(dls, resnet18, metrics=error_rate) | |
| # learn.fine_tune(3) | |
| # learn.export('model.pkl') | |
| # cell | |
| learn= load_learner('model.pk1') | |
| categories= ('Dog', 'Cat') | |
| # # function we need to define for gradio | |
| # # predcition is a string and prob ius a | |
| # # grradio wants dict with each category and prob of each | |
| # # zip -- putting together .... dict-- putting into correct format | |
| # # gradio doesnt work with tensors thus need to convert to float | |
| def classify_image(img): | |
| pred,idx,probs= learn.predict(img) | |
| return dict(zip(categories, map(float, probs))) | |
| im=PILImage.create('dog.jpg') | |
| im.thumbnail((192,192)) | |
| im | |
| learn.predict(im) | |
| classify_image(im) | |
| examples= ['dog.jpg', 'cat.jpg'] | |
| gr.Interface(fn=classify_image, inputs=[gr.Image(type="pil")], outputs=[gr.Label(num_top_classes=2)], examples=examples).launch() |