| from streamlit.components.v1 import html | |
| import numpy as np | |
| from PIL import Image, ImageDraw, ImageFont | |
| import matplotlib.pyplot as plt | |
| import io | |
| import pandas as pd | |
| def click_element(element): | |
| open_script = f"<script type = 'text/javascript'>window.parent.document.querySelector('[id^=tabs-bui][id$=-{element}]').click();</script>" | |
| html(open_script, width=0, height=0) | |
| sample = {'Body Type': 2, | |
| 'Sex': 0, | |
| 'How Often Shower': 1, | |
| 'Social Activity': 2, | |
| 'Monthly Grocery Bill': 230, | |
| 'Frequency of Traveling by Air': 2, | |
| 'Vehicle Monthly Distance Km': 210, | |
| 'Waste Bag Size': 2, | |
| 'Waste Bag Weekly Count': 4, | |
| 'How Long TV PC Daily Hour': 7, | |
| 'How Many New Clothes Monthly': 26, | |
| 'How Long Internet Daily Hour': 1, | |
| 'Energy efficiency': 0, | |
| 'Do You Recyle_Paper': 0, | |
| 'Do You Recyle_Plastic': 0, | |
| 'Do You Recyle_Glass': 0, | |
| 'Do You Recyle_Metal': 1, | |
| 'Cooking_with_stove': 1, | |
| 'Cooking_with_oven': 1, | |
| 'Cooking_with_microwave': 0, | |
| 'Cooking_with_grill': 0, | |
| 'Cooking_with_airfryer': 1, | |
| 'Diet_omnivore': 0, | |
| 'Diet_pescatarian': 1, | |
| 'Diet_vegan': 0, | |
| 'Diet_vegetarian': 0, | |
| 'Heating Energy Source_coal': 1, | |
| 'Heating Energy Source_electricity': 0, | |
| 'Heating Energy Source_natural gas': 0, | |
| 'Heating Energy Source_wood': 0, | |
| 'Transport_private': 0, | |
| 'Transport_public': 1, | |
| 'Transport_walk/bicycle': 0, | |
| 'Vehicle Type_None': 1, | |
| 'Vehicle Type_diesel': 0, | |
| 'Vehicle Type_electric': 0, | |
| 'Vehicle Type_hybrid': 0, | |
| 'Vehicle Type_lpg': 0, | |
| 'Vehicle Type_petrol': 0} | |
| def input_preprocessing(data): | |
| data["Body Type"] = data["Body Type"].map({'underweight':0, 'normal':1, 'overweight':2, 'obese':3}) | |
| data["Sex"] = data["Sex"].map({'female':0, 'male':1}) | |
| data = pd.get_dummies(data, columns=["Diet","Heating Energy Source","Transport","Vehicle Type"], dtype=int) | |
| data["How Often Shower"] = data["How Often Shower"].map({'less frequently':0, 'daily':1, "twice a day":2, "more frequently":3}) | |
| data["Social Activity"] = data["Social Activity"].map({'never':0, 'sometimes':1, "often":2}) | |
| data["Frequency of Traveling by Air"] = data["Frequency of Traveling by Air"].map({'never':0, 'rarely':1, "frequently":2, "very frequently":3}) | |
| data["Waste Bag Size"] = data["Waste Bag Size"].map({'small':0, 'medium':1, "large":2, "extra large":3}) | |
| data["Energy efficiency"] = data["Energy efficiency"].map({'No':0, 'Sometimes':1, "Yes":2}) | |
| return data | |
| def hesapla(model,ss, sample_df): | |
| copy_df = sample_df.copy() | |
| travels = copy_df[["Frequency of Traveling by Air", | |
| "Vehicle Monthly Distance Km", | |
| 'Transport_private', | |
| 'Transport_public', | |
| 'Transport_walk/bicycle', | |
| 'Vehicle Type_None', | |
| 'Vehicle Type_diesel', | |
| 'Vehicle Type_electric', | |
| 'Vehicle Type_hybrid', | |
| 'Vehicle Type_lpg', | |
| 'Vehicle Type_petrol']] | |
| copy_df[list(set(copy_df.columns) - set(travels.columns))] = 0 | |
| travel = np.exp(model.predict(ss.transform(copy_df))) | |
| copy_df = sample_df.copy() | |
| energys = copy_df[[ 'Heating Energy Source_coal','How Often Shower', 'How Long TV PC Daily Hour', | |
| 'Heating Energy Source_electricity','How Long Internet Daily Hour', | |
| 'Heating Energy Source_natural gas', | |
| 'Cooking_with_stove', | |
| 'Cooking_with_oven', | |
| 'Cooking_with_microwave', | |
| 'Cooking_with_grill', | |
| 'Cooking_with_airfryer', | |
| 'Heating Energy Source_wood','Energy efficiency']] | |
| copy_df[list(set(copy_df.columns) - set(energys.columns))] = 0 | |
| energy = np.exp(model.predict(ss.transform(copy_df))) | |
| copy_df = sample_df.copy() | |
| wastes = copy_df[[ 'Do You Recyle_Paper','How Many New Clothes Monthly', | |
| 'Waste Bag Size', | |
| 'Waste Bag Weekly Count', | |
| 'Do You Recyle_Plastic', | |
| 'Do You Recyle_Glass', | |
| 'Do You Recyle_Metal', | |
| 'Social Activity',]] | |
| copy_df[list(set(copy_df.columns) - set(wastes.columns))] = 0 | |
| waste = np.exp(model.predict(ss.transform(copy_df))) | |
| copy_df = sample_df.copy() | |
| diets = copy_df[[ 'Diet_omnivore', | |
| 'Diet_pescatarian', | |
| 'Diet_vegan', | |
| 'Diet_vegetarian', 'Monthly Grocery Bill','Transport_private', | |
| 'Transport_public', | |
| 'Transport_walk/bicycle', | |
| 'Heating Energy Source_coal', | |
| 'Heating Energy Source_electricity', | |
| 'Heating Energy Source_natural gas', | |
| 'Heating Energy Source_wood', | |
| ]] | |
| copy_df[list(set(copy_df.columns) - set(diets.columns))] = 0 | |
| diet = np.exp(model.predict(ss.transform(copy_df))) | |
| hesap = {"Travel": travel[0], "Energy": energy[0], "Waste": waste[0], "Diet": diet[0]} | |
| return hesap | |
| def chart(model, scaler,sample_df, prediction): | |
| p = hesapla(model, scaler,sample_df) | |
| bbox_props = dict(boxstyle="round", facecolor="white", edgecolor="white", alpha=0.7) | |
| plt.figure(figsize=(10, 10)) | |
| patches, texts = plt.pie(x=p.values(), | |
| labels=p.keys(), | |
| explode=[0.03] * 4, | |
| labeldistance=0.75, | |
| colors=["#29ad9f", "#1dc8b8", "#99d9d9", "#b4e3dd" ], shadow=True, | |
| textprops={'fontsize': 20, 'weight': 'bold', "color": "#000000ad"}) | |
| for text in texts: | |
| text.set_horizontalalignment('center') | |
| data = io.BytesIO() | |
| plt.savefig(data, transparent=True) | |
| background = Image.open("./media/default.png") | |
| draw = ImageDraw.Draw(background) | |
| font1 = ImageFont.truetype(font="./style/ArchivoBlack-Regular.ttf", size=50) | |
| font = ImageFont.truetype(font="./style/arialuni.ttf", size=50) | |
| draw.text(xy=(320, 50), text=f" How big is your\nCarbon Footprint?", font=font1, fill="#039e8e", stroke_width=1, stroke_fill="#039e8e") | |
| draw.text(xy=(370, 250), text=f"Monthly Emission \n\n {prediction:.0f} kgCO₂e", font=font, fill="#039e8e", stroke_width=1, stroke_fill="#039e8e") | |
| data_back = io.BytesIO() | |
| background.save(data_back, "PNG") | |
| background = Image.open(data_back).convert('RGBA') | |
| piechart = Image.open(data) | |
| ayak = Image.open("./media/ayak.png").resize((370, 370)) | |
| bg_width, bg_height = piechart.size | |
| ov_width, ov_height = ayak.size | |
| x = (bg_width - ov_width) // 2 | |
| y = (bg_height - ov_height) // 2 | |
| piechart.paste(ayak, (x, y), ayak.convert('RGBA')) | |
| background.paste(piechart, (40, 200), piechart.convert('RGBA')) | |
| data2 = io.BytesIO() | |
| background.save(data2, "PNG") | |
| background = Image.open(data2).resize((700, 700)) | |
| data3 = io.BytesIO() | |
| background.save(data3, "PNG") | |
| return data3 | |