Update app.py

app.py

@@ -9,21 +9,18 @@ def predict(img):
     img = PILImage.create(img)
     pred,pred_idx,probs = learn.predict(img)
     prediction = str(pred)
-     # Provide explanation based on the prediction
-    if pred == "cancer_positive":
-        explanation = "The model predicts that the image shows signs of cancer."
-    elif pred == "cancer_negative":
-        explanation = "The model predicts that the image does not show signs of cancer."
-    elif pred == "implant_cancer_positive":
-        explanation = "The model predicts that the image shows signs of implant-related cancer."
-    elif pred == "implant_cancer_negative":
-        explanation = "The model predicts that the image does not show signs of implant-related cancer."
-    else:
-        explanation = "Unknown prediction."
 
-    return prediction, explanation
-    
+     explanations = {
+        "cancer_negative": "The image does not show signs of cancer.",
+        "cancer_positive": "The image shows signs of cancer.",
+        "implant_cancer_positive": "The image shows signs of implant-related cancer.",
+        "implant_cancer_negative": "The image does not show signs of implant-related cancer."
+    }
+
+    # Get the explanation for the predicted class
+    explanation = explanations[prediction]
     
+    return prediction, explanation
 
 title = "Breast cancer detection with AI(Deep Transfer Learning)"
 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 patients breast with or without inplant<b><p>"