Update the app file

app.py

@@ -2,7 +2,7 @@ import gradio as gr
 from fastai.vision.all import *
 from efficientnet_pytorch import EfficientNet 
 
-learn = load_learner('predictcovidfastaifinal18102023.pkl')
+learn = load_learner('/model/predictcovidfastaifinal18102023.pkl')
 
 categories = learn.dls.vocab
 
@@ -12,8 +12,7 @@ def predict_image(get_image):
 
 title = "Detect COVID_19 Infection Xray Chest Images"
 description = "A covid19 infection classifier trained on the anasmohammedtahir/covidqu dataset with efficientnetb0 base model. Created demo using Gradio and HuggingFace Spaces."
-examples = ['covid_1038.png', 'covid_1034.png']
-examples = ['non_COVID (10610).png', 'non_COVID (10609).png']
+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']
 article="<p style='text-align: center'><a href='https://www.kaggle.com/datasets/anasmohammedtahir/covidqu' target='_blank'>COVID-QU-Ex Dataset</a></p>"
 interpretation='default'
 enable_queue=True

app.py.bak

@@ -0,0 +1,22 @@
+import gradio as gr
+from fastai.vision.all import *
+from efficientnet_pytorch import EfficientNet 
+
+learn = load_learner('predictcovidfastaifinal18102023.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 efficientnetb0 base model. Created demo using Gradio and HuggingFace Spaces."
+examples = ['covid_1038.png', 'covid_1034.png']
+examples = ['non_COVID (10610).png', 'non_COVID (10609).png']
+article="<p style='text-align: center'><a href='https://www.kaggle.com/datasets/anasmohammedtahir/covidqu' 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,article=article, interpretation=interpretation,enable_queue=enable_queue).launch(share=False)