Update app.py

app.py

@@ -1,30 +1,30 @@
 import numpy as np
 import gradio as gr
+import spaces
 
+@spaces.GPU  # Decorate the function to utilize GPU
 def ovarian(Shadow, Solid, Menopause, CA125):
-
     if Shadow == "Yes":
-      Shadow = 1
+        Shadow = 1
     else:
-      Shadow = 0
+        Shadow = 0
     if Solid == "Yes":
-      Solid = 1
+        Solid = 1
     else:
-      Solid = 0
+        Solid = 0
     if Menopause == "Yes":
-      Menopause = 1
+        Menopause = 1
     else:
-      Menopause = 0
+        Menopause = 0
     if CA125 == "Yes":
-      CA125 = 1
+        CA125 = 1
     else:
-      CA125 = 0
+        CA125 = 0
     
-    #print(Shadow, Solid, Menopause, CA125)
-    logit_P = -3.771109 - 2.293585*Shadow + 3.877268*Solid + 1.76309*Menopause + 1.320551*CA125
-    e_logit_P = 2.71828182846 ** logit_P
-    prob = e_logit_P/(1+e_logit_P) * 100
-    return "Risk of Ovarian cancer in this patient : \n" + str('%.3f' %(prob)) + " %" + "\n[Following up on the Predictive Model for Ovarian Cancer.]"
+    logit_P = -3.771109 - 2.293585 * Shadow + 3.877268 * Solid + 1.76309 * Menopause + 1.320551 * CA125
+    e_logit_P = np.exp(logit_P)
+    prob = e_logit_P / (1 + e_logit_P) * 100
+    return "Risk of Ovarian cancer in this patient : \n" + str('%.3f' % (prob)) + " %" + "\n[Following up on the Predictive Model for Ovarian Cancer.]"
 
 demo = gr.Interface(
     ovarian,
@@ -42,4 +42,4 @@ demo = gr.Interface(
     description="Here is a predictive model for ovarian cancer. Choose any four of the risk factors listed below for your patient. This model allows you to calculate the risk of ovarian cancer based on these selected factors.",
 )
 
-demo.launch()
+demo.launch()