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import streamlit as st
import pandas as pd
import joblib
import pandas as pd
from sklearn.preprocessing import LabelEncoder, PolynomialFeatures
def preprocess_input(input_data):
#konvert input ke df
if not isinstance(input_data, pd.DataFrame):
input_data = pd.DataFrame([input_data])
#Pake label encoder
label_encoder = LabelEncoder()
input_data['cbwd'] = label_encoder.fit_transform(input_data['cbwd'])
#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']
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'])
poly_df = pd.DataFrame(poly_features, columns=poly_feature_names, index=input_data.index)
input_data = pd.concat([input_data, poly_df], axis=1)
#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]
#loading model
model = joblib.load('random_forest_predictor_pipeline_model.pkl')
st.title('Beijing PM2.5 Prediction')
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)
hour = st.number_input('Hour', min_value=0, max_value=23, value=0)
DEWP = st.number_input('DEWP', value=0.0)
TEMP = st.number_input('TEMP', value=0.0)
PRES = st.number_input('PRES', value=1000.0)
cbwd = st.selectbox('cbwd', ['NE', 'SE', 'NW', 'cv'])
Iws = st.number_input('Iws', value=0.0)
Is = st.number_input('Is', value=0.0)
Ir = st.number_input('Ir', value=0.0)
if st.button('Predict PM2.5'):
#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
}
#prediksi inputnya
processed_input = preprocess_input(input_data)
prediction = model.predict(processed_input)
st.success(f'The predicted PM2.5 value is: {prediction[0]:.2f}')
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