import gradio as gr from fastai.vision.all import * from efficientnet_pytorch import EfficientNet import torch, torchvision from torchvision import transforms from pytorch_grad_cam import GradCAM from pytorch_grad_cam.utils.image import show_cam_on_image from PIL import Image title = "COVID_19 Infection Detectation App!" head = ( "" "" "

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" "This Space demonstrates a model based on efficientnetB7 base model. The Model was trained to classify chest xray image. To test it, " "

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" "Use the Example Images provided below the up or Upload your own xray images with the App." "

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" "!!!PLEASE NOTE MODEL WAS TRAINED and VALIDATED USING PNG XRAY IMAGES!!!" "

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" """The model is trained using COVID-QU-Ex dataset""" " that the researchers from Qatar University compiled. THe Dataset consists of 33,920 chest X-ray (CXR) images:" "

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" "11,956 COVID-19" "
" "11,263 Non-COVID infections (Viral or Bacterial Pneumonia)" "
" "10,701 Normal" "

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" "Thanks to Kaggle & KaggleX, this is the largest ever created lung xray dataset, that I am aware of, publicly available as of October 2023." "

" "" ) description = head 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'] ] #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))) interpretation= 'shap' 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, interpretation=interpretation,enable_queue=enable_queue).launch(share=False)