Update app.py

app.py

@@ -1,6 +1,5 @@
 import gradio as gr
 from fastai.vision.all import *
-import skimage
 
 learn = load_learner('export.pkl')
 labels = learn.dls.vocab
@@ -11,13 +10,13 @@ def predict(img):
     return {labels[i]: float(probs[i]) for i in range(len(labels))}
 
 title = "Breast cancer detection with Deep Transfer Learning(ResNet18)."
-description = "<p style='text-align: center'><b>As a radiologist or oncologist, it is crucial to know what is wrong with a breast x-ray image.</b><br><b>Upload the breast X-ray image to know what is wrong with a patient's breast with or without implant. This product is from the findings of my (Team) published research paper: <a href='https://iopscience.iop.org/article/10.1088/2057-1976/ad3cdf' target='_blank' style='color: blue;'>read paper</a>. Learn more about me: <a href='https://www.linkedin.com/in/fosberg-addai-53a6991a7/' target='_blank' style='color: blue;'>Fosberg Addai</a></b></p>"
-article = "<p style='text-align: center'><b>Web app is built and managed by Addai Fosberg</b></p>"
+description = "Upload a breast X-ray image to detect potential issues."
+article = "Web app built and managed by Addai Fosberg"
 examples = ['img1.jpeg', 'img2.jpeg']
 
 iface = gr.Interface(
     fn=predict,
-    inputs=gr.Image(),  # Removed the shape parameter
+    inputs=gr.Image(),
     outputs=gr.Label(num_top_classes=3),
     title=title,
     description=description,