app.py

# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.

# %% auto 0
__all__ = ['learn', 'categories', 'train_csv', 'n_inp', 'image', 'label', 'examples', 'iface', 'label_func', 'classify_image']

# %% app.ipynb 1
import gradio as gr
from fastai.vision.all import *
from fastai.data.all import *

# %% app.ipynb 2
learn = load_learner('model.pkl')

# %% app.ipynb 3
categories = ('Badminton', 'Cricket', 'Karate', 'Soccer', 'Swimming', 'Tennis', 'Wrestling')

# %% app.ipynb 4
import pandas as pd
train_csv = pd.read_csv('dataset/train.csv')
n_inp = len(set(train_csv['label']))
train_csv.head()
def label_func(item):
    rel_path = str(item.relative_to('dataset/train'))
    return train_csv[train_csv['image_ID']==rel_path]["label"].values[0]

# %% app.ipynb 5
def classify_image(img):
    pred, idx, probs = learn.predict(img)
    return dict(zip(categories, map(float, probs)))

# %% app.ipynb 6
image = gr.inputs.Image(shape(224, 224))
label = gr.outputs.Label()
examples = ['Badminton.jpg', 'Cricket.jpg', 'Karate.jpg', 'Soccer.jpg', 'Swimming.jpg', 'Tennis.jpg', 'Wrestling.jpg']

# %% app.ipynb 7
iface = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
iface.launch(inline=False)