Update app.py

app.py

@@ -19,14 +19,14 @@ learn.fit(16, lr=0.025)
 
 def predict(age, hypertension, heart_disease, avg_glucose_level, bmi, gender, married, work_type, residence_type, smoking_status):
 
-    data = [[gender, age, hypertension, heart_disease, married, work_type, residence_type, avg_glucose_level, bmi, 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)]]
     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)
 
     dl = learn.dls.test_dl(df_row)
     preds,_ = learn.get_preds(dl=dl)
     
-    return "O paciente tem a seguinte possibilidade de infarto: " + str(preds[0])
+    return f"O paciente tem a seguinte possibilidade de infarto: {preds[0]}"
 
 
 gr.Interface(
@@ -39,7 +39,7 @@ 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 (BMI)"),
+        gr.inputs.Number(default=28.8, label="Body Mass Index"),
         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"),