Custom Alfaromeo Statistics Display

Week06/alfaromeo_Furkan_ERYILMAZ.py

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+import os
+import time
+import random
+import csv
+from selenium import webdriver
+from selenium.webdriver.chrome.options import Options
+from selenium.webdriver.chrome.service import Service
+from webdriver_manager.chrome import ChromeDriverManager  # pip install webdriver-manager
+from selenium.webdriver.common.by import By
+
+
+class AlfaRomeo:
+    def __init__(self, link, csv_file="./alfaromeo.csv", update_interval=1200, headless=True):
+        self.chrome_options = Options()
+        if headless:
+            self.chrome_options.add_argument("--headless")
+        self.chrome_options.add_argument("user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36")
+        self.browser = webdriver.Chrome(service=Service(ChromeDriverManager().install()), options=self.chrome_options)
+        self.link = link
+        self.csv_file = csv_file
+        self.update_interval = update_interval
+        self.__car_list = []
+
+    def update_cars(self):
+        car_list = []
+        next_page = True
+        link = self.link
+        while next_page:
+            self.browser.get(link)
+            time.sleep(1 + random.random())
+            cars = self.browser.find_elements(by=By.CSS_SELECTOR, value=".searchResultsItem")
+            for c in cars:
+                if c.get_attribute("data-id") is None:
+                    continue
+                model = c.find_elements(by=By.CSS_SELECTOR, value='.searchResultsTagAttributeValue')
+                infos = c.find_elements(by=By.CSS_SELECTOR, value='.searchResultsAttributeValue')
+                price = c.find_elements(by=By.CSS_SELECTOR, value='.searchResultsPriceValue')
+                location = c.find_elements(by=By.CSS_SELECTOR, value='.searchResultsLocationValue')
+                try:
+                    car_list.append(
+                        {
+                            'model': model[0].text,
+                            'version': model[1].text,
+                            'year': int(infos[0].text),
+                            'km': int(infos[1].text.replace('.', '')),
+                            'color': infos[2].text,
+                            'price': int(price[0].text.replace('.', '').replace('TL', '')),
+                            'location': location[0].text.replace('\n', ' ')
+                        }
+                    )
+                except:
+                    continue
+            time.sleep(1 + random.random())
+            next_link = self.browser.find_elements(by=By.CSS_SELECTOR, value=".prevNextBut")
+            next_page = False if len(next_link) == 0 else True
+            for n in next_link:
+                if n.get_attribute("title") == "Sonraki":
+                    link = n.get_attribute("href")
+                    next_page = True
+                else:
+                    next_page = False
+        self.browser.close()
+        return car_list
+
+    def save_cars(self):
+        with open(self.csv_file, "w", newline="") as f:
+            writer = csv.writer(f)
+            for c in self.__car_list:
+                writer.writerow([
+                    c['model'], c['version'], c['year'], c['km'], c['color'], c['price'], c['location']
+                ])
+
+    def load_cars(self):
+        with open(self.csv_file, "r") as f:
+            reader = csv.reader(f)
+            for row in reader:
+                try:
+                    self.__car_list.append({
+                        'model': row[0],
+                        'version': row[1],
+                        'year': int(row[2]),
+                        'km': int(row[3]),
+                        'color': row[4],
+                        'price': int(row[5]),
+                        'location': row[6]
+                    })
+                except:
+                    continue
+
+    def delete_cars(self):
+        try:
+            os.remove(self.csv_file)
+        except:
+            pass
+
+    def cars(self):
+        if not os.path.exists(self.csv_file) or (time.time() - os.path.getmtime(self.csv_file)) > self.update_interval:
+            self.__car_list = self.update_cars()
+            self.save_cars()
+        else:
+            self.load_cars()
+        return self.__car_list
+
+
+if __name__ == "__main__":
+    alfa_romeo = AlfaRomeo(
+        link="https://canbula.com/alfaromeo/page01.html",
+        csv_file=f"{os.getcwd()}/alfaromeo.csv",
+        update_interval=3600,
+        headless=False
+    )
+    cars = alfa_romeo.cars()
+    print(cars)

Week06/app_Furkan_ERYILMAZ.py

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+from flask import Flask, render_template, url_for
+from alfaromeo_Furkan_ERYILMAZ import AlfaRomeo
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+import io
+import base64
+
+app = Flask(__name__)
+
+@app.route('/')
+def index():
+    alfa_romeo = AlfaRomeo(
+        link="https://canbula.com/alfaromeo/page01.html",
+        csv_file=f"{os.getcwd()}/alfaromeo.csv",
+        update_interval=3600,
+        headless=False
+    )
+    cars = alfa_romeo.cars()
+    average_price = f"{np.round(np.mean([car['price'] for car in cars])):.0f} TL"
+    average_km = f"{np.round(np.mean([car['km'] for car in cars])):.0f}"
+    average_year = f"{np.round(np.mean([car['year'] for car in cars])):.0f}"
+    return render_template(
+        'index.html',
+        title='Data Visualization Web Application',
+        cars=cars,
+        average_price=average_price,
+        average_km=average_km,
+        average_year=average_year,
+        km_vs_price=km_vs_price(cars),
+        model_histogram=model_histogram(cars),
+        city_pie=city_pie(cars)
+    )
+
+def km_vs_price(cars):
+    km = []
+    price = []
+    for car in cars:
+        km.append(car['km'])
+        price.append(car['price'])
+    fig = plt.figure()
+    plt.scatter(km, price)
+    plt.xlabel("KM")
+    plt.ylabel("Price [TL]")
+    plt.title("KM vs Price")
+    plt.xticks(rotation=0,
+               ticks=range(0, 600000, 100000),
+               labels=[f"{x//1000}K" for x in range(0, 600000, 100000)])
+    plt.semilogy()
+    plt.yticks(rotation=0,
+               ticks=[100000, 200000, 500000, 1e6, 2e6, 5e6],
+               labels=["100K", "200K", "500K", "1M", "2M", "5M"])
+    img = io.BytesIO()
+    fig.savefig(img, format='png')
+    img.seek(0)
+    return base64.b64encode(img.getvalue()).decode('utf-8')
+
+def model_histogram(cars):
+    model = []
+    for car in cars:
+        model.append(car['model'])
+    fig = plt.figure(figsize=(12, 5))
+    plt.subplots_adjust(bottom=0.35)
+    plt.hist(model, bins=range(0, len(set(model))+1), align='left', rwidth=0.8)
+    plt.xlabel('Model')
+    plt.ylabel('Count')
+    plt.title('Model Histogram')
+    plt.xticks(rotation=90)
+    img = io.BytesIO()
+    fig.savefig(img, format='png')
+    img.seek(0)
+    return base64.b64encode(img.getvalue()).decode('utf-8')
+
+def city_pie(cars):
+    city = []
+    for car in cars:
+        city.append(car['location'].split(' ')[0])
+    city_counts = {}
+    for c in city:
+        if c in city_counts:
+            city_counts[c] += 1
+        else:
+            city_counts[c] = 1
+    top_5 = sorted(city_counts.items(), key=lambda x: x[1], reverse=True)[:5]
+    top_5_cities = [x[0] for x in top_5]
+    other = 0
+    for c in city_counts:
+        if c not in top_5_cities:
+            other += city_counts[c]
+    fig = plt.figure(figsize=(12, 5))
+    plt.pie([x[1] for x in top_5] + [other],
+            labels=[x[0] for x in top_5] + ['Other'],
+            autopct='%1.1f%%')
+    plt.title('City Pie Chart')
+    img = io.BytesIO()
+    fig.savefig(img, format='png')
+    img.seek(0)
+    return base64.b64encode(img.getvalue()).decode('utf-8')
+
+
+if __name__ == '__main__':
+    app.run()

Week06/static/style_Furkan_ERYILMAZ.css

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+body {
+    margin: 0;
+    padding: 0;
+    height: 100%;
+}
+
+h1 {
+    margin: 5rem 0;
+}
+
+.fixed_header {
+    display: block;
+    overflow: auto;
+    height: 650px;
+    width: 100%;
+    border: 1px dashed black;
+}
+
+thead {
+    background-color: white;
+    position: sticky;
+    top: 0;
+}
+
+.fixed_header thead tr th {
+    text-align: left;
+}
+
+.fixed_header tbody tr td {
+    text-align: left;
+}
+
+.image-clone {
+    display: none;
+    position: fixed;
+    top: 50%;
+    left: 50%;
+    transform: translate(-50%, -50%);
+    z-index: 902;
+}
+
+img:not(.image-clone) {
+    width: 100%;
+    height: 100%;
+    object-fit: cover;
+}
+
+.card:has(img) {
+    cursor: pointer;
+}
+
+.card {
+    position: relative;
+    z-index: 901;
+}
+
+.card:has(img)::after {
+    content: 'Hover over graph to view zoomed';
+    margin: 1rem 0;
+}
+
+.col-md-4:has(.card:hover) .background-overlay {
+    position: fixed;
+    left: 0;
+    top: 0;
+    width: 100%;
+    height: 100%;
+    background: rgba(0, 0, 0, 0.75);
+    z-index: 900;
+}
+
+.col-md-4:has(.image-clone:hover) .background-overlay {
+    position: fixed;
+    left: 0;
+    top: 0;
+    width: 100%;
+    height: 100%;
+    background: rgba(0, 0, 0, 0.75);
+    z-index: 900;
+}