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requirement.txt

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+numpy==1.22.4
+pandas==1.4.2
+scikit-learn==1.0.2
+imblearn==0.0
+

urine_R.py

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+import pandas as pd 
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler, MaxAbsScaler
+from sklearn.neighbors import KNeighborsClassifier
+import streamlit as st
+df=pd.read_excel("resistant_data_urine.xlsx")
+
+X=df.iloc[:,:-1].values
+y=df.iloc[:,-1].values
+X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=10)
+
+sc=MaxAbsScaler()
+X_train_new=sc.fit_transform(X_train)
+X_test_new=sc.transform(X_test)
+
+model=KNeighborsClassifier(n_neighbors=4,p=1)
+model=model.fit(X_train_new,y_train)
+
+print("Training accuracy: ",model.score(X_train_new,y_train))
+print("Testing accuracy : ",model.score(X_test_new,y_test))
+
+st.title('Resistant')
+st.title(':blue[Urine test]:')
+
+
+Age = st.number_input("Age")
+options = ["Male", "Female"]
+selectbox_selection = st.selectbox("Select Gender", options)
+#st.write(f"Gender selected is {selectbox_selection}")
+Fever = st.number_input("Fever")
+options1 = ["Yes", "No"]
+selectbox_selection = st.selectbox("Bone_merrow_transplantation", options1)
+HB = st.number_input("HB")
+platet = st.number_input("platet")
+CRP= st.number_input("CRP")
+Procalictonin =st.number_input("Procalictonin")
+E_colli= st.number_input("CTX-M")
+Result1 =0
+Klebsilla = st.number_input("KPC")
+Result2 = 0
+Pseudomonas= st.number_input("NDM")
+Result3 = 0
+submit=st.button("Result")
+gender = 1
+Bone_merrow_transplantation=1
+
+if float(E_colli)<= -10:
+    Result1 = 1
+if float(Klebsilla)<= -10:
+    Result2 = 1
+if float(Pseudomonas)<= -10:
+    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])
+st.write(s)
+print(s)