# Bismillahir Rahmaanir Raheem 
# Almadadh Ya Gause Radi Allahu Ta'alah Anh - Ameen 


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


def is_pneumonia(x): 
    return (x.find('virus')!=-1 or x.find('bacteria')!=-1)
	


# load the trained fast ai model for predictions
learn = load_learner('model.pkl')


# define the function to call
categories = ('Pneumonia', 'Normal') 

def predict(img):
	pred, idx, probs = learn.predict(img)
	return dict(zip(categories, map(float, probs)))
	
	
title = "Pediatric Pneumonia Chest X-Ray Predictor"

description = "A pediatric pneumonia chest x-ray predictor model trained on the chest-xray-pneumonia dataset using ResNet18 via <a href='http://www.fast.ai/' target='_blank'>fast.ai</a>. The dataset is from: <a href='http://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia' target='_blank'>Chest X-Ray Images (Pneumonia)</a> and the associated scientific journal paper is <a href='http://www.cell.com/cell/fulltext/S0092-8674(18)30154-5' target='_blank'>Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning</a>. The accuracy of the model is: 81.25%"


article = "<p style='text-align: center'><span style='font-size: 15pt;'>Pediatric Pneumonia Chest X-Ray Predictor. Zakia Salod. 2022. </span></p>"


image = gr.inputs.Image(shape=(512, 512))
label = gr.outputs.Label()
examples = [
             ['person1_virus_6.jpeg'],
			 ['NORMAL2-IM-0285-0001.jpeg'],
			 ['person82_bacteria_404.jpeg'],
			 ['NORMAL2-IM-0373-0001.jpeg'],
			 ['person1618_virus_2805.jpeg'],
			 ['NORMAL2-IM-0381-0001.jpeg'],
			 ['person159_bacteria_747.jpeg'],
			 ['NORMAL2-IM-0222-0001.jpeg'],
		   ]
interpretation = 'default'
enable_queue = True 




iface = gr.Interface(
		fn=predict, 
		title=title, 
		description=description, 
		article=article,
		inputs=image, 
		outputs=label,
		theme="default",
		examples=examples,
		interpretation=interpretation,
		enable_queue=enable_queue
)



iface.launch(inline=False)