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Build error
Build error
Merge branch 'main' of hf.co:spaces/Anthony-Ml/covid_predictor
Browse files- Gradcam.png +0 -0
- app.py +52 -17
- requirements.txt +8 -1
Gradcam.png
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app.py
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@@ -2,6 +2,56 @@ import gradio as gr
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from fastai.vision.all import *
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from efficientnet_pytorch import EfficientNet
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#learn = load_learner('model/predictcovidfastaifinal18102023.pkl')
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learn = load_learner('model/final_20102023_eb7_model.pkl')
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@@ -11,24 +61,9 @@ def predict_image(get_image):
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pred, idx, probs = learn.predict(get_image)
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return dict(zip(categories, map(float, probs)))
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description = """
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This Space demonstrates model based on efficientnet base model.
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The model is trained using [anasmohammedtahir/covidqu](https://www.kaggle.com/datasets/anasmohammedtahir/covidqu) dataset
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"""
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examples = [
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['covid/covid_1038.png'], ['covid/covid_1034.png'],
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['covid/cd.png'], ['covid/covid_1021.png'],
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['covid/covid_1027.png'], ['covid/covid_1042.png'],
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['covid/covid_1031.png']]
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article="<p style='text-align: center'><a href='https://www.kaggle.com/datasets/anasmohammedtahir/covidqu' target='_blank'>COVID-QU-Ex Dataset</a></p>"
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interpretation="shap"
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num_shap=5
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enable_queue=True
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gr.Interface(fn=predict_image, inputs=gr.Image(shape=(224,224)),
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outputs = gr.Label(num_top_classes=3),title=title,description=description,examples=examples,
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from fastai.vision.all import *
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from efficientnet_pytorch import EfficientNet
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import torch, torchvision
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from torchvision import transforms
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from pytorch_grad_cam import GradCAM
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from pytorch_grad_cam.utils.image import show_cam_on_image
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from PIL import Image
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title = "COVID_19 Infection Detectation App!"
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head = (
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"<body>"
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"<center>"
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"<img src='/file=Gradcam.png' width=200>"
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"<h2>"
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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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"</h2>"
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"<h3>"
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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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"</h3>"
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"<h3>"
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"!!!PLEASE NOTE MODEL WAS TRAINED and VALIDATED USING PNG FILES!!!"
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"</h3>"
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"</center>"
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"<p>"
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"<b>""<a href='https://www.kaggle.com/datasets/anasmohammedtahir/covidqu'>The model is trained using COVID-QU-Ex dataset</a>""</b>"
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" that the researchers from Qatar University compiled,that consists of 33,920 chest X-ray (CXR) images including:"
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"</p>"
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"<ul>"
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"<li>"
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"11,956 COVID-19"
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"</li>"
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"<li>"
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"11,263 Non-COVID infections (Viral or Bacterial Pneumonia)"
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"</li>"
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"<li>"
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"10,701 Normal"
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"</li>"
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"</ul>"
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"<p>"
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"Thanks to Kaggle & KaggleX, this is the largest ever created lung mask dataset, that I am aware of publicly available as of October 2023."
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"</p>"
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"</body>"
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)
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description = head
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examples = [
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['covid/covid_1038.png'], ['covid/covid_1034.png'],
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['covid/cd.png'], ['covid/covid_1021.png'],
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['covid/covid_1027.png'], ['covid/covid_1042.png'],
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['covid/covid_1031.png']
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]
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#learn = load_learner('model/predictcovidfastaifinal18102023.pkl')
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learn = load_learner('model/final_20102023_eb7_model.pkl')
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pred, idx, probs = learn.predict(get_image)
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return dict(zip(categories, map(float, probs)))
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interpretation= 'shap'
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enable_queue=True
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gr.Interface(fn=predict_image, inputs=gr.Image(shape=(224,224)),
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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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requirements.txt
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fastai
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efficientnet-pytorch
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fastai
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efficientnet-pytorch
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torch
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torchvision
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torch-lr-finder
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pytorch-lightning
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grad-cam
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pillow
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numpy
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