Ikizamini

Welcome to the Ikizamini application, a comprehensive tool to help you practice and prepare for the theory exam for your driving test.

Table of Contents

• Features
• Technologies Used
• Generating Questions

Features

• Interactive quiz to test your knowledge of driving rules, regulations, and safety.
• Real-time feedback on your answers.
• Timer to simulate the actual exam conditions.
• Detailed results page showing correct and incorrect answers.

Technologies Used

• React: A JavaScript library for building user interfaces.
• Vite: A build tool that provides a fast development environment.
• TypeScript: A typed superset of JavaScript that compiles to plain JavaScript.
• Firebase Hosting: Fast and secure hosting for web applications.
• Tailwind CSS: A utility-first CSS framework for rapid UI development.
• React Router: Declarative routing for React applications.
• Redux Toolkit: The official, recommended way to write Redux logic.

Generating Questions

This application uses questions generated by crawling the Rwanda Traffic Guide website. Here is how the questions were generated:

Web Crawler for Rwanda Traffic Guide Questions

This script crawls pages from the Rwanda Traffic Guide website, extracts questions, options, correct answers, and associated images, then saves this data in a JSON file.

Requirements

Ensure you have the following libraries installed:

• requests
• beautifulsoup4
• lxml

Script Overview

Variables and Setup

1. Base URL and Starting URL:

   • The base URL and the starting URL for the crawling process are defined.

   base_url = "https://rwandatrafficguide.com/"
   start_url = "https://rwandatrafficguide.com/rtg001-ikinyabiziga-cyose-cyangwa-ibinyabiziga-bigomba-kugira/"

2. Directory to Save Images:

   • Creates a directory to save downloaded images.

   os.makedirs("downloaded_images", exist_ok=True)

3. Custom Headers:

   • Custom headers are defined to mimic a browser request and avoid being blocked by the website.

   headers = {
       "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
   }

4. Data Storage:

   • An empty list is created to store the extracted data.

   data = []

Functions

1. Extract ID from URL:

   • Extracts the numerical ID from the URL using a regular expression.

   def extract_id_from_url(url):
       match = re.search(r'rtg(\d+)', url)
       return int(match.group(1)) if match else None

2. Fetch Page:

   • Fetches the HTML content of the page.

   def fetch_page(url):
       response = requests.get(url, headers=headers)
       if response.status_code == 200:
           return response.text
       else:
           print(f"Failed to retrieve the page. Status code: {response.status_code}")
           return None

3. Parse Page:

   • Parses the HTML content to extract the question, options, correct answer, and image.

   def parse_page(html, url):
       soup = BeautifulSoup(html, 'html.parser')
       entry_content = soup.find('div', class_='entry-content clr')
       if not entry_content:
           return None
   
       question = entry_content.find('p', class_='question').text.strip() if entry_content.find('p', class_='question') else ""
       options_list = entry_content.find('ul', class_='list')
       options = {}
       answer = ""
   
       if options_list:
           for idx, li in enumerate(options_list.find_all('li'), start=1):
               option_text = li.text.strip()
               option_key = chr(96 + idx)  # 'a', 'b', 'c', 'd'
               options[option_key] = option_text
               if li.find('strong', class_='colored'):
                   answer = option_key
   
       image_url = ""
       image_tag = entry_content.find('figure', class_='wp-block-image')
       if image_tag and image_tag.find('img'):
           img_src = image_tag.find('img')['src']
           img_name = os.path.basename(img_src)
           img_response = requests.get(img_src, headers=headers)
           with open(os.path.join("downloaded_images", img_name), 'wb') as f:
               f.write(img_response.content)
           image_url = img_name
   
       question_id = extract_id_from_url(url)
   
       data.append({
           "id": question_id,
           "question": question,
           "image": image_url,
           "options": options,
           "answer": answer
       })
   
       next_page_tag = soup.find('div', class_='nav-next')
       next_page_url = next_page_tag.a['href'] if next_page_tag and next_page_tag.a else None
   
       return next_page_url

Main Logic

The script starts crawling from the initial URL and continues to the next page until no further pages are found.

url = start_url
while url:
    html = fetch_page(url)
    if html:
        url = parse_page(html, url)
    else:
        break

Save Data to JSON

After crawling, the script saves the extracted data in a JSON file.

with open('questions.json', 'w', encoding='utf-8') as json_file:
    json.dump(data, json_file, ensure_ascii=False, indent=4)

print("Crawling completed and data saved to questions.json")

Running the Script

1. Ensure you have the required libraries installed.
2. Save the script to a Python file, e.g., crawl_questions.py.
3. Run the script:

   python crawl_questions.py

The script will create a directory named downloaded_images to save any images it downloads. It will also create a JSON file named questions.json containing the crawled data.

Example JSON Output

[
    {
        "id": 22,
        "question": "Itara ryo guhagarara ry’ibara ritukura rigomba kugaragara igihe ijuru rikeye nibura mu ntera ikurikira",
        "image": "RTGQ398-Ibibazo-Nibisubizo-Byamategeko-Yumuhanda-Rwanda-Traffic-Guide-Com-ni-ikihe-icyapa-gisobanura-umuhanda-w-icyerekezo-kimwe-icyapa-e-a.jpg",
        "options": {
            "a": "Metero 100 ku manywa na metero 20 mu ijoro",
            "b": "Metero 150 ku manywa na metero50 mu ijoro",
            "c": "Metero 200 ku manywa na metero100 mu ijoro",
            "d": "Nta gisubizo cy’ukuri kirimo"
        },
        "answer": "d"