Update app.py

app.py

@@ -1,10 +1,10 @@
-'''import pandas as pd
+import pandas as pd
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import StandardScaler, MaxAbsScaler
-from sklearn.neighbors import KNeighborsClassifier'''
+from sklearn.neighbors import KNeighborsClassifier
 import streamlit as st
 import requests
-'''from imblearn.over_sampling import SMOTE
+from imblearn.over_sampling import SMOTE
 from imblearn.over_sampling import SMOTE
 
 
@@ -24,7 +24,7 @@ X_train_new=sc.fit_transform(X_train)
 X_test_new=sc.transform(X_test)
 
 model=KNeighborsClassifier(n_neighbors=5,p=1)
-model=model.fit(X_train_new,y_train)'''
+model=model.fit(X_train_new,y_train)
 
 
 st.title('Infection detection')
@@ -52,20 +52,20 @@ submit=st.button("Result")
 gender = 1
 Bone_merrow_transplantation=1
 
-'''if float(E_colli)<= -10:
+if float(E_colli)<= -10:
     Result1 = 1
 if float(Klebsilla)<= -10:
     Result2 = 1
 if float(Pseudomonas)<= -10:
-    Result3 = 1'''
+    Result3 = 1
 if selectbox_selection == "FEMALE":
     gender = 0
 if selectbox_selection == "NO":
     Bone_merrow_transplantation=0
 
 
-#sapmle=[Age, gender, Fever, Bone_merrow_transplantation, HB, platet, CRP, Procalictonin, E_colli, Result1, Klebsilla, Result2, Pseudomonas, Result3]
-#s=model.predict([sapmle])
+sapmle=[Age, gender, Fever, Bone_merrow_transplantation, HB, platet, CRP, Procalictonin, E_colli, Result1, Klebsilla, Result2, Pseudomonas, Result3]
+s=model.predict([sapmle])
 
 
 response = requests.get(f"http://127.0.0.1:8000/my-first-api?age={Age}&gender={gender}&fever={Fever}&Bone_merrow_transplantation={Bone_merrow_transplantation}&hb={HB}&platet={platet}&crp={CRP}&procalictonin={Procalictonin}&e_colli={E_colli}&klebsilla={Klebsilla}&pseudomonas={Pseudomonas}")