updated app and removed unused functions

app.py

@@ -4,15 +4,16 @@ import joblib
 import pandas as pd
 from datetime import timedelta, datetime
 from functions import get_weather_data_weekly, data_encoder, get_aplevel
+from PIL import Image
 
 
 def fancy_header(text, font_size=24):
     res = f'<p style="color:#ff5f27; font-size: {font_size}px;text-align:center">{text}</p>'
     st.markdown(res, unsafe_allow_html=True)
 
-# TODO: set the screen to widehardo
+vienna_image = Image.open('vienna.jpg')
 st.title('Air Quality Prediction Project 🌩')
-st.image("bienna.jpg", use_column_width='auto')
+st.image(vienna_image, use_column_width='auto')
 st.write(36 * "-")
 
 st.markdown("# This is a final project in the course ID2223 Scalable Machine Learning and Deep Learning :computer:")

functions.py

@@ -163,14 +163,6 @@ def data_encoder(X):
     X['conditions'] = OrdinalEncoder().fit_transform(X[['conditions']])
     return X
 
-def transform(df):
-    df.loc[df["windgust"].isna(),'windgust'] = df['windspeed']
-    df['snow'].fillna(0,inplace=True)
-    df['snowdepth'].fillna(0, inplace=True)
-    df['pressure'].fillna(df['pressure'].mean(), inplace=True)
-    return df
-
-
 def get_aplevel(temps:np.ndarray) -> list:
     boundary_list = np.array([0, 50, 100, 150, 200, 300]) # assert temps.shape == [x, 1]
     redf = np.logical_not(temps<=boundary_list) # temps.shape[0] x boundary_list.shape[0] ndarray

requirements.txt

@@ -4,4 +4,5 @@ numpy
 joblib
 python-dotenv
 xgboost
-optuna
+optuna
+Pillow