app.py

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

#learn = load_learner('model/predictcovidfastaifinal18102023.pkl')
learn = load_learner('model/final_20102023_eb7_model.pkl')

categories = learn.dls.vocab

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

title = "Detect COVID_19 Infection Xray Chest Images"
description = "A covid19 infection classifier trained on the anasmohammedtahir/covidqu dataset with efficientnetb0 base model. Created demo using Gradio and HuggingFace Spaces."
examples = [['covid/covid_1038.png'], ['covid/covid_1034.png'], ['covid/cd.png'], ['covid/covid_1021.png'], ['covid/covid_1027.png'], ['covid/covid_1042.png'], ['covid/covid_1031.png']]
article="<p style='text-align: center'><a href='https://www.kaggle.com/datasets/anasmohammedtahir/covidqu' target='_blank'>COVID-QU-Ex Dataset</a></p>"
interpretation='default'
enable_queue=True

with gr.Row():
    covid = ['covid/covid_1038.png', 'covid/covid_1034.png', 'covid/cd.png', 'covid/covid_1021.png', 'covid/covid_1027.png', 'covid/covid_1042.png', 'covid/covid_1031.png']
    gr.Gallery(value=covid, columns=2).style(height="200px", grid=2)
    
gr.Interface(fn=predict_image, inputs=gr.Image(shape=(224,224)),
             outputs = gr.Label(num_top_classes=3),title=title,description=description,examples=examples,article=article, interpretation=interpretation,enable_queue=enable_queue).launch(share=False)