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| import pandas as pd | |
| import pickle | |
| import joblib | |
| from datetime import datetime | |
| def add_new_columns(df2): | |
| df = df2.copy() | |
| # easy to implement | |
| df['day_of_week'] = df['Date'].dt.dayofweek | |
| df['day_of_month'] = df['Date'].dt.day | |
| df['last_weekend'] = ((df['day_of_week'] == 4) | (df['day_of_week'] == 5) | (df['day_of_week'] == 6)) & (df['day_of_month'] + 7 > df['Date'].dt.days_in_month) | |
| df['Day_of_week_numeric'] = df['Date'].dt.dayofweek | |
| df['Day_of_year_numeric'] = df['Date'].dt.dayofyear | |
| df['Week_numeric'] = df['Date'].dt.isocalendar().week | |
| # df['Avg_Sale_Weekday'] = df.groupby(['Location', df['Date'].dt.dayofweek])['Number_of_burgers'].transform('mean') | |
| return df | |
| def predict_without_columns(X, yhat, day,month,year,temp,rain,wind,sun,snow, day_of_the_week,location,parken_event, general_event,is_holiday,rf,gb,hgb,xgr,lgbr,reg): | |
| new_row = {name:0 for name in X} | |
| new_row['yhat'] = yhat | |
| new_row['Day'] = day | |
| new_row['Month'] = month | |
| new_row['Year'] = year | |
| new_row['Temperature'] = temp | |
| new_row['Wind'] = wind | |
| new_row['Rain'] = rain | |
| new_row['Sun'] = sun | |
| new_row['Snow'] = snow | |
| new_row['holiday'] = is_holiday | |
| new_row['Day_of_the_week_' + day_of_the_week] = 1 | |
| new_row['Location_' + location] = 1 | |
| new_row['Parken_Event_' + parken_event] = 1 | |
| new_row['General_Event_' + general_event] = 1 | |
| new_pd = pd.DataFrame([new_row]) | |
| new_pd['Date'] = pd.to_datetime(new_pd[['Year', 'Month', 'Day']]) | |
| new_pd = add_new_columns(new_pd) | |
| new_pd.drop('Date', axis=1, inplace=True) | |
| columns = ['yhat', 'Day', 'Month', 'Year', 'Temperature', 'Rain', 'Wind', 'Sun', | |
| 'Snow', 'day_of_week', 'day_of_month', | |
| 'last_weekend', 'Day_of_week_numeric', 'Day_of_year_numeric', | |
| 'Week_numeric', 'holiday', 'Day_of_the_week_Friday', | |
| 'Day_of_the_week_Monday', 'Day_of_the_week_Saturday', | |
| 'Day_of_the_week_Sunday', 'Day_of_the_week_Thursday', | |
| 'Day_of_the_week_Tuesday', 'Day_of_the_week_Wednesday', 'Location_DGH', | |
| 'Location_HDM', 'Location_HPNS', 'Location_LGV', 'Location_NHG', | |
| 'Location_VDV', 'Parken_Event_Concert', 'Parken_Event_Football', | |
| 'Parken_Event_None', 'General_Event_Cultural', 'General_Event_Fashion', | |
| 'General_Event_Festivitie', 'General_Event_None', | |
| 'General_Event_Social', 'General_Event_Sport', | |
| 'General_Event_Worldwide'] | |
| new_pd = new_pd[columns] | |
| prediction_reg = reg.predict(new_pd) | |
| prediction_rf = rf.predict(new_pd) | |
| prediction_xgr = xgr.predict(new_pd) | |
| prediction_gb = gb.predict(new_pd) | |
| prediction_hgb = hgb.predict(new_pd) | |
| prediction_lgbr = lgbr.predict(new_pd) | |
| mean_pred = (prediction_reg + prediction_xgr + prediction_lgbr + prediction_hgb + prediction_gb) / 6 | |
| output = 'On ' + str(day_of_the_week) + ' , ' + str(day) + ' - ' + str(month) + ' - ' + str(year) + ', the location ' + str(location) + ' is predicting to sell ' + str(round(mean_pred[0])) + ' burgers' | |
| return output | |
| def predict(day,month,year,location,temp,rain,wind,sun,parken_event,general_event): | |
| snow=0 | |
| day, month, year = int(day), int(month), int(year) | |
| temp, rain, wind, sun, snow = float(temp), float(rain), float(wind), float(sun), float(snow) | |
| location, parken_event, general_event = str(location), str(parken_event), str(general_event) | |
| date_obj = datetime(year, month, day) | |
| day_of_week_num = date_obj.weekday() | |
| days = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"] | |
| day_of_the_week = days[day_of_week_num] | |
| prophet_file = f'prophet_df_{location}.csv' | |
| df_prophet = pd.read_csv(prophet_file) | |
| df_prophet = df_prophet[['ds','yhat']].copy() | |
| df_prophet.set_index('ds',inplace=True) | |
| date_string = f'{year}-{month}-{day}' | |
| datetime_obj = str(datetime.strptime(date_string, '%Y-%m-%d').date()) | |
| yhat = df_prophet.loc[datetime_obj]['yhat'] | |
| holidays_dk = pd.read_csv('holidays_denmark.csv') | |
| holidays_dk = holidays_dk[['ds','holiday']].copy() | |
| holidays_dk.set_index('ds',inplace=True) | |
| is_holiday = 1 if datetime_obj in holidays_dk.index else 0 | |
| print(is_holiday) | |
| rf = joblib.load("filename_rf.joblib") | |
| gb = joblib.load("filename_gb.joblib") | |
| hgb = joblib.load("filename_hgb.joblib") | |
| xgr = joblib.load("filename_xgr.joblib") | |
| lgbr = joblib.load("filename_lgbr.joblib") | |
| reg = joblib.load("filename_reg.joblib") | |
| X = ['yhat', 'Day', 'Month', 'Year', 'Temperature', 'Rain', 'Wind', 'Sun', 'Snow', | |
| 'Day_of_the_week_Friday', 'Day_of_the_week_Monday', | |
| 'Day_of_the_week_Saturday', 'Day_of_the_week_Sunday', | |
| 'Day_of_the_week_Thursday', 'Day_of_the_week_Tuesday', | |
| 'Day_of_the_week_Wednesday', 'Location_DGH', 'Location_HDM', | |
| 'Location_HPNS', 'Location_LGV', 'Location_NHG', 'Location_VDV', | |
| 'Parken_Event_Concert', 'Parken_Event_Football', 'Parken_Event_None', | |
| 'General_Event_Cultural', 'General_Event_Fashion', | |
| 'General_Event_Festivitie', 'General_Event_None', | |
| 'General_Event_Social', 'General_Event_Sport', | |
| 'General_Event_Worldwide'] | |
| actual_prediction = predict_without_columns(X,yhat,day,month,year,temp,rain,wind,sun,snow,day_of_the_week,location,parken_event, general_event,is_holiday,rf,gb,hgb,xgr,lgbr,reg) | |
| return actual_prediction | |
| import gradio as gr | |
| theme = gr.themes.Soft( | |
| primary_hue="green", | |
| secondary_hue="emerald" | |
| ) | |
| # Launch a Gradio interface | |
| title = 'Predicting number of burgers - Gasoline Grill' | |
| description = 'This is an AI model that predicts the number of burgers based on different parameters. These parameters are:\n1. Day: current day of the month (from 1 to 31)\n2. Month: Current month (from 1 to 12)\n3. Year: current year.\n4. Day of the week: current day of the week (from Monday to Sunday)\n5. Location: current location (LGV, NHG, HDM, VDV, DGH, HPNS)\n6. Temperature: expected medium temperature of the day (in ºC)\n7. Rain: expected rain of the day (in mm)\n8. Wind: expected wind of the day (in m/s)\n9. Sun: expected hours of sunlight of the day (in hours)\n10. Parken event: Is there an event in Parken? (Football, Concert, None)\n11. General Event: Is there a general event in Copenhagen? (Cultural, Fashion, Festivity, None, Social, Sport, Worldwide)\n\nFor temperature, wind, rain and sun, check the DMI webpage https://www.dmi.dk/vejrarkiv or check the weather app on your phone :)\n\nOnce you have all the data, you must put it in order, separated by commas and without spaces between the data.\n\nSome examples are shown below. Click on them to see a real example' | |
| examples = ['12,2,2024,Monday,VDV,1.8,5.6,2.4,0,None,None','29,5,2024,Wednesday,LGV,10,2,1.4,5,Football,Social'] | |
| # demo = gr.Interface(fn=predict, title=title, description=description) | |
| with gr.Blocks(theme=theme) as demo: | |
| with gr.Row(): | |
| gr.Markdown( | |
| """ | |
| # Predicting number of burgers - Gasoline Grill 🍔 | |
| This is an AI model that predicts the number of burgers to be sold based on different parameters. These parameters are: | |
| 1. Day, Month , Year | |
| 2. Location (LGV, DGH, NHG, HDM, VDV, HPNS) | |
| 3. Temperature: expected medium temperature of the day (in ºC) | |
| 4. Rain: expected rain of the day (in mm) | |
| 5. Wind: expected wind of the day (in m/s) | |
| 6. Sun: expected hours of sunlight of the day (in hours) | |
| 7. Parken event: Is there an event in Parken? | |
| 8. General Event: Is there a general event in Copenhagen? | |
| For temperature, wind, rain and sun, check the DMI webpage https://www.dmi.dk/vejrarkiv or the weather app on your phone :) | |
| Once you have all the data, click the Predict button. | |
| """ | |
| ) | |
| with gr.Row(): | |
| day = gr.Dropdown([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,21,22,23,24,25,26,27,28,29,30,31],label='Day') | |
| month = gr.Dropdown([1,2,3,4,5,6,7,8,9,10,11,12],label='Month') | |
| year = gr.Dropdown([2024],label='Year', value=2024) | |
| # day_of_the_week = gr.Dropdown(['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'], label='Day of the week') | |
| location = gr.Dropdown(['LGV', 'DGH','NHG' ,'HDM', 'VDV','HPNS'], label='Location') | |
| with gr.Row(): | |
| with gr.Column(): | |
| temp = gr.Slider(minimum=-10, maximum=40, label='Temperature (°C)') | |
| sun = gr.Slider(minimum=0, maximum=17, label='Sunlight (hours)') | |
| with gr.Column(): | |
| rain = gr.Slider(minimum=0, maximum=30, label='Rain (mm)') | |
| wind = gr.Slider(minimum=0, maximum=12, label='Wind (m/s)') | |
| # snow = gr.Slider(minimum=0, maximum=3, label='Snow') | |
| with gr.Row(): | |
| parken_event = gr.Dropdown(['Football', 'Concert', 'None'], label='Parken Event', value='None') | |
| general_event = gr.Dropdown(['Cultural', 'Fashion', 'Festivity', 'None', 'Social', 'Sport', 'Worldwide'], label='General Event', value='None') | |
| with gr.Row(): | |
| predict_2 = gr.Button(value = 'Predict') | |
| with gr.Column(): | |
| prediction = gr.Textbox() | |
| predict_2.click(predict, inputs = [day,month,year,location,temp,rain,wind,sun,parken_event,general_event], outputs = prediction) | |
| # demo.launch(auth=('gasolgrill','gasolgrill')) | |
| demo.launch() | |
| # gr.Interface(predict, | |
| # inputs = [ | |
| # gr.Row([gr.Textbox(label='Day'),gr.Textbox(label='Month'),gr.Textbox(label='Year'),gr.Dropdown(['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'], label='Day of the week'),gr.Dropdown(['LGV', 'NHG', 'DGH', 'HDM', 'VDV'], label='Location')]), | |
| # gr.Row([gr.Slider(minimum=-10, maximum=40, label='Temperature (°C)'), gr.Slider(minimum=0, maximum=17, label='Sunlight (hours)'), gr.Slider(minimum=0, maximum=30, label='Rain (mm)'), gr.Slider(minimum=0, maximum=12, label='Wind (m/s)')]), | |
| # gr.Row([gr.Dropdown(['Football', 'Concert', 'None'], label='Parken Event'), gr.Dropdown(['Cultural', 'Fashion', 'Festivity', 'None', 'Social', 'Sport', 'Worldwide'], label='General Event')]), | |
| # ], | |
| # outputs="textbox", | |
| # title=title, description = description | |
| # ).launch(share=True) |