app.py

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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

#pip install gradio

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

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import gradio as gr
from fastai.vision.all import *
import skimage

learn = load_learner('export.pkl')

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labels = learn.dls.vocab

def predict(img):
    img = PILImage.create(img)
    pred, idx, probs = learn.predict(img)
    return {labels[i]: float(probs[i]) for i in range(len(labels))}

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examples = ['street_bicycle.jpg',
           'street_image.jpg']

gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(512, 512)),
             title = "bicycle detector",
             examples = examples,
             interpretation = 'default',
             outputs = gr.outputs.Label(num_top_classes=2)).launch(),