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app.py

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+import gradio as gr
+from fastai.vision.all import *
+import skimage
+
+learn = load_learner('final_18102023_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 fastai. Created demo using Gradio and HuggingFace Spaces."
+examples = ['/content/dataset/val/normal/Normal (10001).png']
+https://www.kaggle.com/datasets/anasmohammedtahir/covidqu/data
+article="<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>COVID-QU-Ex Dataset</a></p>"
+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)

requirements.txt

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+numpy
+pandas
+os.path
+matplotlib.pyplot
+tensorflow
+seaborn
+os
+shutil
+sys
+tensorflow.keras
+fastai
+torch
+timm
+streamlit
+pickle5
+gradio