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 = "COVID_19 Infection Detectation App!"
head = (
  "<center>"
  "<img src='covid/Xray Image SuperImposed Gradcam.png' width=400>"
  "This Space demonstrates model based on efficientnet base model. I has been trained to classify chest xray image." 
  "To test it, Use the Example Images Provided or Upload your own xray images the space provided."
  "The model is trained using [anasmohammedtahir/covidqu](https://www.kaggle.com/datasets/anasmohammedtahir/covidqu) dataset"  
  "</center>"
)
description = head
article="<p style='text-align: center'><a href='https://www.kaggle.com/datasets/anasmohammedtahir/covidqu' target='_blank'>COVID-QU-Ex Dataset</a></p>"
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']
]

interpretation="default"
enable_queue=True

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)