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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 = (
"<body>"
"<center>"
"<img src='/file=Gradcam.png' width=200>"
"<h2>"
"This Space demonstrates a model based on efficientnetB7 base model. The Model was trained to classify chest xray image. To test it, "
"</h2>"
"<h3>"
"Use the Example Images provided below the up or Upload your own xray images with the App."
"</h3>"
"<h3>"
"!!!PLEASE NOTE MODEL WAS TRAINED and VALIDATED USING PNG FILES!!!"
"</h3>"
"</center>"
"<p>"
"<b>""<a href='https://www.kaggle.com/datasets/anasmohammedtahir/covidqu'>The model is trained using COVID-QU-Ex dataset</a>""</b>"
" that the researchers from Qatar University compiled,that consists of 33,920 chest X-ray (CXR) images including:"
"</p>"
"<ul>"
"<li>"
"11,956 COVID-19"
"</li>"
"<li>"
"11,263 Non-COVID infections (Viral or Bacterial Pneumonia)"
"</li>"
"<li>"
"10,701 Normal"
"</li>"
"</ul>"
"<p>"
"Thanks to Kaggle & KaggleX, this is the largest ever created lung mask dataset, that I am aware of publicly available as of October 2023."
"</p>"
"</body>"
)
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)))
def interpretation_function(image_path, model, target_layer, target_category=None):
# Load and preprocess the image
image = Image.open(image_path)
preprocess = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
])
input_image = preprocess(image).unsqueeze(0) # Add a batch dimension
input_image = input_image.to('cuda' if torch.cuda.is_available() else 'cpu')
# Create an instance of GradCAM
cam = GradCAM(model=model, target_layer=target_layer)
# Compute the CAM
cam_image = cam(input_tensor=input_image, target_category=target_category)
# Show the CAM on the original image
visualization = show_cam_on_image(input_image, cam_image)
#visualization.show()
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
input_img = gr.Image(label="Input Image", shape=(224, 224))
with gr.Row():
interpret = gr.Button("Interpret")
with gr.Column():
label = gr.Label(label="Predicted Class")
with gr.Column():
interpretation = gr.components.Interpretation(input_img)
interpret.click(interpretation_function(input_img,learn, learn.layer4[-1],target_category=None), input_img, interpretation)
#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, interpretation=interpretation,enable_queue=enable_queue).launch(share=False)
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