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

title = 'Sea Animals Classification'
description = '''
With this Spaces, you can classify 19 different sea animals with uploading their pictures or using examples given below. 

Here are the list of animals into this model. 'corals', 'crabs', 'dolphin', 'eel', 'jelly fish', 'lobster', 'nudibranchs', 'octopus', 'penguin', 'puffers', 'sea rays', 'sea urchins', 'seahorse', 'seal', 'sharks', 'squid', 'starfish', 'turtle_tortoise', 'whale'

<br>Source of training dataset : https://www.kaggle.com/datasets/vencerlanz09/sea-animals-image-dataste
<br>You can gather information about how this model is trained : https://www.kaggle.com/code/tolgakurtulus/sea-animals-classification-with-fastai

Enjoy it! 🐟
'''

article = "<p style='text-align: center'><center><img src='https://visitor-badge.glitch.me/badge?page_id=tkseaanimals' alt='visitor badge'></center></p>"

learn = load_learner('model.pkl')

image = gr.inputs.Image(shape=(128, 128))
label = gr.outputs.Label()
examples = ['coral.jpg', 'crabs.jpg', 'sea_rays.jpg', 'turtle_tortoise.jpg']

categories = ('corals','crabs','dolphin','eel','jelly fish','lobster','nudibranchs','octopus','penguin','puffers','sea rays','sea urchins','seahorse','seal','sharks','squid','starfish','turtle_tortoise','whale')

def classify_img(img):
    pred,idx,probs = learn.predict(img)
    return dict(zip(categories, map(float, probs)))

interface = gr.Interface(fn=classify_img,
                inputs=image,
                title=title,
                article = article,
                description=description,
                outputs=label,
                examples=examples)

interface.launch(inline=False)