Update app.py

app.py

@@ -5,20 +5,9 @@ import pickle
 import sklearn
 import os
 
-print(f"Prediction environment scikit-learn version: {sklearn.__version__}")
 
-def decode_file(file_path):
-    with open(file_path, 'rb') as file:
-        obj = pickle.load(file)
-    return obj
 
-# Load the model once when starting the app
-try:
-    model = decode_file('model.pkl')
-    print("Model loaded successfully")
-except Exception as e:
-    print(f"Error loading model: {e}")
-    model = None
+model = pickle.load(model.pkl)
 
 def predict(gender, age, hypertension, ever_married, work_type, heart_disease, 
            avg_glucose_level, bmi, smoking_status, Residence_type):
@@ -26,7 +15,6 @@ def predict(gender, age, hypertension, ever_married, work_type, heart_disease,
     if model is None:
         return "Error: Model not loaded"
         
-    # Mapping for categorical variables
     gender_mapping = {'Male': 1, 'Female': 0}
     hypertension_mapping = {'Yes': 1, 'No': 0}
     ever_married_mapping = {'Yes': 1, 'No': 0}
@@ -35,7 +23,6 @@ def predict(gender, age, hypertension, ever_married, work_type, heart_disease,
     smoking_status_mapping = {'formerly smoked': 3, 'smokes': 1, 'never smoked': 2, 'Unknown': 0}
     Residence_type_mapping = {'Urban': 1, 'Rural': 0}
 
-    # Map categorical variables to their corresponding numerical values
     gender = gender_mapping[gender]
     hypertension = hypertension_mapping[hypertension]
     ever_married = ever_married_mapping[ever_married]
@@ -44,23 +31,19 @@ def predict(gender, age, hypertension, ever_married, work_type, heart_disease,
     smoking_status = smoking_status_mapping[smoking_status]
     Residence_type = Residence_type_mapping[Residence_type]
 
-    # Create input data
     inputs = [gender, age, hypertension, ever_married, work_type, heart_disease, 
               avg_glucose_level, bmi, smoking_status, Residence_type]
     input_labels = ['gender', 'age', 'hypertension', 'ever_married', 'work_type', 
                    'heart_disease', 'avg_glucose_level', 'bmi', 'smoking_status', 'Residence_type']
 
-    # Convert to DataFrame
     input_df = pd.DataFrame([inputs], columns=input_labels)
     
     try:
-        # Make prediction
         prediction = model.predict_proba(input_df)[0][1]
         return f"The probability of stroke is {prediction:.2%}"
     except Exception as e:
         return f"Error making prediction: {str(e)}"
 
-# Create the Gradio interface
 iface = gr.Interface(
     fn=predict,
     inputs=[