Update app_function.py

app_function.py

@@ -91,54 +91,6 @@ def loading_data():
     return results
 
 
-
-# Function use to get the user input data
-def user_input(HighBP,HighChol, CholCheck, BMI, Smoker,Stroke,HeartDiseaseorAttack,
-                PhysActivity, Fruits, Veggies,HvyAlcoholConsump,AnyHealthcare, NoDocbcCost, GenHlth,
-                MentHlth, PhysHlth, DiffWalk,
-                Sex, Age, Education,Income):
-
-    user_data = {'HighBP':HighBP, 'HighChol':HighChol, 'CholCheck':CholCheck, 'BMI':BMI, 'Smoker':Smoker,
-       'Stroke':Stroke, 'HeartDiseaseorAttack':HeartDiseaseorAttack, 'PhysActivity':PhysActivity, 'Fruits':Fruits, 'Veggies':Veggies,
-       'HvyAlcoholConsump':HvyAlcoholConsump, 'AnyHealthcare':AnyHealthcare, 'NoDocbcCost':NoDocbcCost, 'GenHlth':GenHlth,
-       'MentHlth':MentHlth, 'PhysHlth':PhysHlth, 'DiffWalk':DiffWalk, 'Sex':Sex, 'Age':Age, 'Education':Education,
-       'Income':Income}
-    
-    user_df = pd.DataFrame(user_data,index=[0])
-
-    ## Load Model from pickle file
-    with open("Model_package.pkl","rb") as f:
-        Model_package = pickle.load(f)
-        model = Model_package['my_classif']
-
-    #LOADING THE DATA
-    results = loading_data()
-    num_col_trans = results["num_col_trans"]
-    cat_col_trans = results["cat_col_trans"]
-    
-
-    # Data preparation before fitting to the model
-    user_df["Age2"] = user_df["Age"]**2 
-    user_df_std = num_col_trans.transform(user_df)
-    user_df_std = pd.DataFrame(user_df_std ,columns=list(user_df.columns) )
-    user_df_encoded = cat_col_trans.transform(user_df_std)
-    user_encoded = np.array(user_df_encoded).reshape((1,-1))
-
-
-    #PREDICTION 
-
-   
-
-    target_names = ['No diabetes', 'Prediabetes', 'Diabetes']
-
-    pred_proba = model.predict_proba(user_encoded)[0]
-
-    
-    return {classe : pred_proba[i] for i, classe in enumerate(target_names)}
-
-
-
-
 # Get PDF File for user
 def get_data_inputPDF(PDF_file):