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| # This Python 3 environment comes with many helpful analytics libraries installed | |
| # It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python | |
| # For example, here's several helpful packages to load | |
| import numpy as np # linear algebra | |
| import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) | |
| # Input data files are available in the read-only "../input/" directory | |
| # For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory | |
| import os | |
| for dirname, _, filenames in os.walk('/kaggle/input'): | |
| for filename in filenames: | |
| print(os.path.join(dirname, filename)) | |
| # You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" | |
| # You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session | |
| #|default_exp app | |
| #|export | |
| #pip install fastbook | |
| import fastbook | |
| from fastbook import * | |
| #pip install fastai | |
| from fastai.vision.widgets import * | |
| #pip install gradio | |
| import gradio as gr | |
| import IPython | |
| from IPython.display import display | |
| from PIL import Image | |
| import pathlib | |
| #temp = pathlib.PosixPath | |
| #pathlib.PosixPath = pathlib.WindowsPath | |
| plt = platform.system() | |
| if plt == 'Linux': pathlib.WindowsPath = pathlib.PosixPath | |
| def search_images(term, max_images=999999): | |
| print(f"Searching for '{term}'") | |
| return search_images_ddg(term, max_images) | |
| learn = load_learner('model.pkl') | |
| breeds = ('Labrador Retrievers','German Shepherds','Golden Retrievers','French Bulldogs','Bulldogs','Beagles','Poodles','Rottweilers','Chihuahua') | |
| def classify_image(img): | |
| pred,idx,probs = learn.predict(img) | |
| #return dict(zip(breeds, map(float,probs))) | |
| return "This is " + pred | |
| image = gr.components.Image() | |
| label = gr.components.Label() | |
| examples = ['dog.jpg','labrador.jpeg','dunno.jpg'] | |
| for x in examples: | |
| Image.open(x) | |
| intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples) | |
| intf.launch(inline=False) |