app.py

# Human or Animal application in gradio.

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

def label_func(filename):
    if 'human' in Path(filename).parts[-2]:
        return 'human'
    elif 'animal' in Path(filename).parts[-2]:
        return 'animal'

loaded_generic_model = load_learner('model_generic.pkl')
loaded_special_model = load_learner('model_special.pkl')

def predict_human_or_animal(img):
    im = PILImage.create(img)
    generic_class, generic_class_id, generic_probs = loaded_generic_model.predict(img)
    special_class, special_class_id, special_probs = loaded_special_model.predict(img)
    
    return {generic_class: float(generic_probs[generic_class_id])}, {special_class: float(special_probs[special_class_id])}
    
examples = ['human_1.jpeg', 'human_2.jpeg', 'animal_1.jpeg', 'animal_2.jpeg']
title = "Human or Animal classifiers"
description = "Human or Animal classification signal from two models. First output is from a generic model trained with generic images whereas second output is from a specialized model trained with images of hard positives."

intf = gr.Interface(fn=predict_human_or_animal, inputs="image", outputs=["label", "label"],title=title,description=description,examples=examples).launch(share=True)