Update app.py

app.py

@@ -17,9 +17,16 @@ dls = TabularPandas(
 learn = tabular_learner(dls, metrics=accuracy, layers=[10,10])
 learn.fit(16, lr=0.025)
 
+def convert_yes_no(data):
+  if data == "Yes":
+    return 1
+  else:
+    return 0
+
+
 def predict(age, hypertension, heart_disease, avg_glucose_level, bmi, gender, married, work_type, residence_type, smoking_status):
 
-    data = [[str(gender), float(age), int(hypertension), int(heart_disease), str(married), str(work_type), str(residence_type), float(avg_glucose_level), float(bmi), str(smoking_status)]]
+    data = [[str(gender), float(age), int(convert_yes_no(hypertension)), int(convert_yes_no(heart_disease)), str(married), str(work_type), str(residence_type), float(avg_glucose_level), float(bmi), str(smoking_status)]]
     columns_df = ['gender', 'age', 'hypertension', 'heart_disease', 'ever_married', 'work_type', 'Residence_type', 'avg_glucose_level', 'bmi','smoking_status']
     df_row = pd.DataFrame(data,columns=columns_df)
 
@@ -39,7 +46,7 @@ gr.Interface(
         gr.Dropdown(["Yes", "No"], label="Hypertension"),
         gr.Dropdown(["Yes", "No"], label="Heart disease"),
         gr.inputs.Number(default=100, label="Average glucose level"),
-        gr.inputs.Number(default=28.8, label="Body Mass Index"),
+        gr.inputs.Number(default=28.8, label="Body Mass Index (BMI)"),
         gr.inputs.Radio(choices=["Female", "Male", "Other"], default="Female", label="Gender"),
         gr.Dropdown(["No", "Yes"], label="Married"),
         gr.Dropdown(["Governamental", "Never worked", "Private", "Self-employed", "Children"], label="Work type"),