Update app.py

app.py

@@ -5,22 +5,21 @@ import pandas as pd
 from sklearn.preprocessing import LabelEncoder, PolynomialFeatures
 
 def preprocess_input(input_data):
-    # Convert input_data to DataFrame if it's not already
+    #konvert input ke df
     if not isinstance(input_data, pd.DataFrame):
         input_data = pd.DataFrame([input_data])
     
-    # Label encoding for 'cbwd'
+    #Pake label encoder
     label_encoder = LabelEncoder()
     input_data['cbwd'] = label_encoder.fit_transform(input_data['cbwd'])
     
-    # Feature engineering
+    #fitur engineering
     input_data['season'] = input_data['month'].apply(lambda x: (x % 12 + 3) // 3)
     input_data['day_of_week'] = pd.to_datetime(input_data[['year', 'month', 'day']]).dt.dayofweek
     input_data['is_weekend'] = input_data['day_of_week'].apply(lambda x: 1 if x >= 5 else 0)
     input_data['TEMP_Iws'] = input_data['TEMP'] * input_data['Iws']
     input_data['DEWP_PRES'] = input_data['DEWP'] * input_data['PRES']
     
-    # Polynomial features
     poly = PolynomialFeatures(degree=2, include_bias=False)
     poly_features = poly.fit_transform(input_data[['DEWP', 'TEMP', 'PRES', 'Iws']])
     poly_feature_names = poly.get_feature_names_out(['DEWP', 'TEMP', 'PRES', 'Iws'])
@@ -28,18 +27,17 @@ def preprocess_input(input_data):
     
     input_data = pd.concat([input_data, poly_df], axis=1)
     
-    # Select features for prediction
+    #pilih fitur buat prediksi
     features = ['year', 'month', 'day', 'hour', 'DEWP', 'TEMP', 'PRES', 'cbwd', 'Iws', 'Is', 'Ir',
                 'season', 'day_of_week', 'is_weekend', 'TEMP_Iws', 'DEWP_PRES'] + list(poly_feature_names)
     
     return input_data[features]
 
-# Load the saved model
+#loading model
 model = joblib.load('random_forest_predictor_pipeline_model.pkl')
 
-st.title('PM2.5 Prediction App')
+st.title('Beijing PM2.5 Prediction')
 
-# Create input fields for all required features
 year = st.number_input('Year', min_value=2000, max_value=2050, value=2024)
 month = st.number_input('Month', min_value=1, max_value=12, value=1)
 day = st.number_input('Day', min_value=1, max_value=31, value=1)
@@ -53,17 +51,15 @@ Is = st.number_input('Is', value=0.0)
 Ir = st.number_input('Ir', value=0.0)
 
 if st.button('Predict PM2.5'):
-    # Create a dictionary with the input values
+    #tombol buat inp
     input_data = {
         'year': year, 'month': month, 'day': day, 'hour': hour,
         'DEWP': DEWP, 'TEMP': TEMP, 'PRES': PRES, 'cbwd': cbwd,
         'Iws': Iws, 'Is': Is, 'Ir': Ir
     }
     
-    # Preprocess the input data
+    #prediksi inputnya
     processed_input = preprocess_input(input_data)
-    
-    # Make prediction
     prediction = model.predict(processed_input)
     
     st.success(f'The predicted PM2.5 value is: {prediction[0]:.2f}')