Update app.py

app.py

@@ -1,35 +1,42 @@
+
 import pickle
 import pandas as pd
 import sklearn
 import gradio as gr
 import joblib
 
+
+# In[38]:
+
+
 def encode_df(df):
-    
-    #Gender
+
+    # Gender
     sex_map = {"Male": 1, "Female": 0}
     df = df.replace({"Gender": sex_map})
-    
-    #Tipe Angina
-    chestpain_map = {"Typical": 0, "Asymptomatic": 1, "Nonanginal": 2, "Nontypical" : 3}
+
+    # Tipe Angina
+    chestpain_map = {"Typical": 0, "Asymptomatic": 1,
+                     "Nonanginal": 2, "Nontypical": 3}
     df = df.replace({"Tipe_angina": chestpain_map})
-    
-    #Gula_darah_puasa
+
+    # Gula_darah_puasa
     fastingBLP_map = {"<120": 0, ">120": 1, "120": 2}
     df = df.replace({"Gula_darah_puasa": fastingBLP_map})
-    
-    #Angina_aktivitas
+
+    # Angina_aktivitas
     exang_map = {"No": 0, "Yes": 1}
     df = df.replace({"Angina_aktivitas": exang_map})
-    
-    #Hasil_Elektrokardiografi
-    recg_map = {"Normal": 0, "Abnormal Wave": 1, "Left Ventricular Hypertrophy": 2}
+
+    # Hasil_Elektrokardiografi
+    recg_map = {"Normal": 0, "Abnormal Wave": 1,
+                "Left Ventricular Hypertrophy": 2}
     df = df.replace({"Hasil_Elektrokardiografi": recg_map})
-    
-    #st_slope_ECG
+
+    # st_slope_ECG
     slope_map = {"Upsloping": 1, "Flat": 2, "Downsloping": 3}
     df = df.replace({"st_slope_ECG": slope_map})
-    
+
     df = df[
         [
             "Umur",
@@ -47,22 +54,27 @@ def encode_df(df):
     ]
     return df
 
+
 filename = 'ACS_model_random_forest.sav'
 
 # load the model from disk
 loaded_model = joblib.load(filename)
 
-def predict(Umur, Gender, Tipe_angina, Tekanan_darah_istirahat, Kolesterol, 
-            Gula_darah_puasa, Hasil_Elektrokardiografi, 
+
+# In[51]:
+
+
+def predict(Umur, Gender, Tipe_angina, Tekanan_darah_istirahat, Kolesterol,
+            Gula_darah_puasa, Hasil_Elektrokardiografi,
             Denyut_jantung_max, Angina_aktivitas, st_depression_ECG, st_slope_ECG):
     df = pd.DataFrame.from_dict(
         {
             "Umur": [Umur],
             "Gender": [Gender],
             "Tipe_angina": [Tipe_angina],
-            "Tekanan_darah_istirahat" : [Tekanan_darah_istirahat],
-            "Kolesterol" : [Kolesterol],
-            "Gula_darah_puasa" : [Gula_darah_puasa],
+            "Tekanan_darah_istirahat": [Tekanan_darah_istirahat],
+            "Kolesterol": [Kolesterol],
+            "Gula_darah_puasa": [Gula_darah_puasa],
             "Hasil_Elektrokardiografi": [Hasil_Elektrokardiografi],
             "Denyut_jantung_max": [Denyut_jantung_max],
             "Angina_aktivitas": [Angina_aktivitas],
@@ -70,43 +82,54 @@ def predict(Umur, Gender, Tipe_angina, Tekanan_darah_istirahat, Kolesterol,
             "st_slope_ECG": [st_slope_ECG],
         }
     )
-    
+
     df = encode_df(df)
     pred = loaded_model.predict_proba(df)[0]
-    return {"Possible Heart Disease": float(pred[1]), "Less chance of Heart Disease": float(pred[0])}
+    output = {"Possible Heart Disease": float(pred[1]),
+              "Less chance of Heart Disease": float(pred[0])}
+
+    return output
+
 
 title = "Interactive Demonstration for Acute Coronary Syndrome Prediction System"
-des = '''This model predicts the possibility of a heart disease using a hybrid sampling SMOTE-TOMEK model that achieved an high accuracy of 85%, and f1-score 94% with Random Forest Algorithm'''
+des = '''This model predicts the possibility of a heart disease using a hybrid sampling SMOTE-TOMEK model that achieved an high accuracy of 85% with Random Forest Algorithm'''
 article = "<p style='text-align: center'><a href='https://www.linkedin.com/in/m-afif-rizky-a-a96048182/'>Created by @Vrooh933 Production</a> | <a href='https://github.com/afifrizkyandika11551100310'>GitHub Profile</a>"
 
-demo = gr.Interface(
-    predict,
-    [gr.Slider(0, 88, value=25, label='Umur'),
-     gr.Radio(["Male", "Female"], label='Gender'),
-     gr.Dropdown(["Typical", "Asymptomatic", "Nonanginal", "Nontypical"], label="Tipe_angina"),
-     
-     gr.Slider(0, 200, value=125, label= 'Tekanan_darah_istirahat'),
-     gr.Slider(0, 603, value=50, label= 'Kolesterol'),
-     gr.Radio(["<120", ">120", '120'], label='Gula_darah_puasa'),
-     
-     
-     gr.Dropdown(["Normal", "Abnormal Wave", "Left Ventricular Hypertrophy"], label='Hasil_Elektrokardiografi'),
-     gr.Number(value=100, label='Denyut_jantung_max'),
-     gr.Dropdown(["No", "Yes"], label='Angina_aktivitas'),
-     gr.Slider(-2.6, 6.2, value=3.1, label='st_depression_ECG'),
-     gr.Radio(["Upsloping", "Flat", "Downsloping"], label='st_slope_ECG'),
-    ],
-    "label",
-    examples=[
-        [37,'Male','Typical', 89, 276, '>120', "Left Ventricular Hypertrophy",150,'No',2.3,'Downsloping',0,'Fixed'],
-        [63,'Male','Typical', 50, 100, '<120', "Abnormal Wave",90,'No',1.0,'Downsloping',1,'Reversable'],
-        [58,'Female','Asymptomatic', 70, 500, '>120', "Normal",111,'Yes',0.8,'Flat',0,'Normal']
-    ],
-    title=title,
-    description=des,
-    article=article,
-    live=True,
-)
+demo = gr.Interface(predict,
+                    [gr.Slider(0, 88, value=25, label='Umur'),
+                     gr.Radio(["Male", "Female"], label='Gender'),
+                     gr.Dropdown(["Typical", "Asymptomatic", "Nonanginal",
+                                 "Nontypical"], label="Tipe_angina"),
+
+                     gr.Slider(0, 200, value=125,
+                               label='Tekanan_darah_istirahat'),
+                     gr.Slider(0, 603, value=50, label='Kolesterol'),
+                     gr.Radio(["<120", ">120", '120'],
+                              label='Gula_darah_puasa'),
+                     gr.Dropdown(["Normal", "Abnormal Wave", "Left Ventricular Hypertrophy"],
+                                 label='Hasil_Elektrokardiografi'),
+                     gr.Number(value=100, label='Denyut_jantung_max'),
+                     gr.Dropdown(["No", "Yes"], label='Angina_aktivitas'),
+                     gr.Slider(-2.6, 6.2, value=3.1,
+                               label='st_depression_ECG'),
+                     gr.Radio(["Upsloping", "Flat", "Downsloping"],
+                              label='st_slope_ECG'),
+                     ], "label",
+                    examples=[[37, 'Male', 'Typical', 89, 276, '>120', "Left Ventricular Hypertrophy", 150, 'No', 2.3, 'Downsloping', 0, 'Fixed'],
+                              [63, 'Male', 'Typical', 50, 100, '<120', "Abnormal Wave",
+                                  90, 'No', 1.0, 'Downsloping', 1, 'Reversable'],
+                              [58, 'Female', 'Asymptomatic', 70, 500, '>120',
+                               "Normal", 111, 'Yes', 0.8, 'Flat', 0, 'Normal']
+                              ],
+                    title=title,
+                    description=des,
+                    article=article,
+                    live=True,
+                    )
+
+
+# In[53]:
+
 
 if __name__ == "__main__":
-    demo.launch() 
+    demo.launch()