Temesvári Csanád commited on
Commit
174811d
1 Parent(s): af5a939

revert back

Browse files
Files changed (2) hide show
  1. app.py +5 -4
  2. functions.py +6 -6
app.py CHANGED
@@ -3,7 +3,7 @@ import hopsworks
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  import joblib
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  import pandas as pd
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  import datetime
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- from functions import get_weather_data_weekly, data_encoder, get_info
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  from PIL import Image
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@@ -14,7 +14,7 @@ def fancy_header(text, font_size=24):
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  st.set_page_config(layout="wide")
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  st.title('Air Quality Prediction Project for Vienna! 🌩')
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  vienna_image = Image.open('vienna.jpg')
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- st.image(vienna_image, use_column_width='auto', caption='Vienna')
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  st.write(36 * "-")
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  st.markdown("# This is a final project in the course ID2223 Scalable Machine Learning and Deep Learning :computer:")
@@ -49,7 +49,8 @@ fancy_header("Making AQI predictions for the next 7 days")
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  preds = model.predict(data_encoder(weekly_data)).astype(int)
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- color_level, poll_level = get_info(preds.reshape(1, -1))
 
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  next_week_datetime = [today + datetime.timedelta(days=d) for d in range(7)]
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  next_week_str = [f"{days.strftime('%A')}, {days.strftime('%Y-%m-%d')}" for days in next_week_datetime]
@@ -57,6 +58,6 @@ next_week_str = [f"{days.strftime('%A')}, {days.strftime('%Y-%m-%d')}" for days
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  df = pd.DataFrame(data=[preds, poll_level], index=["AQI", "Air pollution level"], columns=next_week_str)
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  st.write("Here they are!")
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- st.dataframe(df)
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  st.button("Re-run")
 
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  import joblib
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  import pandas as pd
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  import datetime
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+ from functions import get_weather_data_weekly, data_encoder, get_aplevel, get_color
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  from PIL import Image
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  st.set_page_config(layout="wide")
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  st.title('Air Quality Prediction Project for Vienna! 🌩')
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  vienna_image = Image.open('vienna.jpg')
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+ st.image(vienna_image, use_column_width='auto')
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  st.write(36 * "-")
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  st.markdown("# This is a final project in the course ID2223 Scalable Machine Learning and Deep Learning :computer:")
 
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  preds = model.predict(data_encoder(weekly_data)).astype(int)
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+ air_pollution_level = ['Good', 'Moderate', 'Unhealthy for sensitive Groups','Unhealthy' ,'Very Unhealthy', 'Hazardous']
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+ poll_level = get_aplevel(preds.T.reshape(-1, 1), air_pollution_level)
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  next_week_datetime = [today + datetime.timedelta(days=d) for d in range(7)]
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  next_week_str = [f"{days.strftime('%A')}, {days.strftime('%Y-%m-%d')}" for days in next_week_datetime]
 
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  df = pd.DataFrame(data=[preds, poll_level], index=["AQI", "Air pollution level"], columns=next_week_str)
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  st.write("Here they are!")
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+ st.dataframe(df.style.apply(get_color, subset=(["Air pollution level"], slice(None))))
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  st.button("Re-run")
functions.py CHANGED
@@ -172,9 +172,9 @@ def get_aplevel(temps:np.ndarray, table:list):
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  level = [table[el] for el in cat[1]]
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  return level
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- def get_info(level:list):
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- air_pollution_level = {1:[50, 'Good', 'Green'], 2:[100, 'Moderate','Yellow'], 3:[150, 'Unhealthy for sensitive Groups','DarkOrange'],4:[200, 'Unhealthy','Red'] ,5:[300, 'Very Unhealthy','Purple'], 6:[1000, 'Hazardous','DarkRed']}
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- ind = [np.max(np.nonzero( el >= [air_pollution_level[key][0] for key in air_pollution_level.keys()])) for el in level]
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- color_text = [f"color:{air_pollution_level[idex][2]};" for idex in ind]
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- pollution_level = [air_pollution_level[idex][1] for idex in ind]
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- return color_text, pollution_level
 
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  level = [table[el] for el in cat[1]]
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  return level
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+ def get_color(level:list):
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+ air_pollution_level = ['Good', 'Moderate', 'Unhealthy for sensitive Groups','Unhealthy' ,'Very Unhealthy', 'Hazardous']
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+ color_list = ["Green", "Yellow", "DarkOrange", "Red", "Purple", "DarkRed"]
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+ ind = [air_pollution_level.index(lel) for lel in level]
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+ text = [f"color:{color_list[idex]};" for idex in ind]
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+ return text