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Digikala is a leading online store in Iran, recognized for its wide variety of products and services. Initially focused on selling electronic and digital items, over the past 15 years, it has transformed into a comprehensive e-commerce platform offering a diverse range of product categories. This expansion has made Digikala one of the leading e-commerce platforms in the Middle East.

Digikala has evolved into a complete ecosystem of online businesses that extends beyond just e-commerce. The group's focus has been on completing the value chain of Iran's digital economy, providing the necessary infrastructure for various types of businesses. Their activities include content production, logistics, financial technology, marketing technology, and cloud technology. With its wide access and comprehensive services, Digikala meets the diverse needs and populations of its customers.

• Over 41.5 million active monthly users.

• More than 9.7 million SKUs (Stock Keeping Units).

• Collaborations with over 308,000 sellers.

• A significant presence in logistics and distribution with over 530 infrastructure centers.

• Strong fulfillment capacity, capable of handling more than 904,000 orders daily.

• An expanding product range that saw a 29% growth last year.

• A variety of subsidiaries and services, including Fidibo (an e-book store), Digistyle (an online fashion and clothing store), Digipay (focused on mobile and web payments), and Pindo (a consumer-to-consumer advertising platform).

Digikala's success is largely due to its commitment to ensuring a positive online buying and selling experience. The company regularly monitors product quality and pricing and actively responds to feedback and reports from users to maintain high standards.

The Digikala Web Crawler project is a comprehensive and flexible system for collecting and analyzing data from the Digikala online store. Developed in Python, this system can extract information about sellers and products from Digikala web pages. The main goal of this project is to provide a powerful tool for competitive analysis and accurate business decision-making. With this project, you will be able to extract information about sellers and products and create a history for them. Should any changes occur in product or seller information, the previous data will be stored in history tables for review.

• db_handler.py: Manages the SQLite database for storing and manipulating collected data. Check the details

• driver_manager.py: Creates and manages driver operations. Check the details

• product_details_extractor.py: Extracts product page information from Digikala and sends the data to db_handler. Check the details

• seller_product_data_extractor.py: Extracts category page information and seller information, and sends the data to db_handler. Check the details

• webScriper.py: A file to create a crawler object and use the extraction functions. Check the details

• console_panel.py: A Command Line Interface (CLI) that allows the user to control the crawler process and export data in CSV format. Check the details

• app.py: A web-based user interface using Flask (under development). Check the details

• logger.py: A logging system to track crawler operations and debug. Check the details

• TODOS.py: A file for organizing to-do lists and bugs. Check the details

This project is a powerful tool for merchants and market analysts looking to gain a better understanding of the market and competitor behavior. With this crawler, users can analyze prices, check product inventory, and gain a better understanding of seller performance. The system can also help identify new market opportunities and improve sales strategies.

• Extracting seller and product information and storing data in inventory and history tables.
• Outputting data in CSV format.

Ensure Python version 3.x is installed on your system. Use the following command to check your Python version:

python --version

Step 2: Clone the Project Repository

Clone the project from its repository :

git clone https://github.com/rabbittx/Digikala-Crawler.git
cd 'Digikala-Crawler'

Step 3: Create a Virtual Environment

To avoid library conflicts, create a virtual environment:

python -m venv env

Step 4: Activate the Virtual Environment

Activate the virtual environment:

On Windows:

venv\Scripts\activate

On macOS and Linux:

source env/bin/activate

Step 5: Install Dependencies

Install the project dependencies using the requirements.txt file:

pip install -r requirements.txt

Step 6: Set up the Environment File

Install Geckodriver: First, download geckodriver for your browser: fireFox chrome and place it in your desired location or alongside the project. Suggested path alongside the project:

• archive\gekcodrive\firefox\geckodriver.exe
• archive\gekcodrive\chrome\chromedriver.exe

Step 7: Run the Project to use this project you can use console panel or web panel .

Run the script panel to start the project:

python console_panel.py

Run the script web panel to start the project:

python app.py

Step 8: Use the Project

After the first run, you need to add the required settings. Specify the path of your web driver and the database location (the database is created automatically, you just need to enter an address for storage):

• Suggested path for the database: archive\dataBase\digikala_database.db Also, specify which browser you are using, Firefox or Chrome, and then the project is ready to use. From the displayed menu, select your desired option. You can also choose whether you want the browser to run in the background or normally. After extracting the needed information, you can use the output menu to receive your data in a CSV file.

Note

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* The version of the web driver must match your browser version; otherwise, you will encounter errors.
* To avoid placing an excessive load on Digikala's servers, timing considerations have been made. Please do not remove or reduce them.
* Please refrain from running multiple scripts simultaneously to speed up extraction.

This project uses sqlite3 for storing the data extracted. The database created in this project includes 3 tables which store information in different extraction states.

• sellers: Contains fields such as id, seller_id, crawl_date, seller_name, membership_period, satisfaction_with_goods, seller_performance, people_have_given_points, timely_supply, obligation_to_send, no_return, and introduction_of_the_seller.

• products: Includes id, product_id, seller_id, crawl_date, seller_name, product_link, product_image, product_rate, product_name, product_price, product_price_discount_percent, product_price_discount, product_special_sale, stock.

• products_extraction: Fields like id, crawl_date, product_id, seller_id, seller_name, categories, product_link, product_title, product_main_title, user_review, insurer, Insurance_discount_percent, Insurance_price_before_discount, Insurance_final_price, Other_sellers_for_this_product, satisfaction_with_the_product, warranty, digiclub_points, discount_percent, price_before_discount, final_price, product_stock, product_image, other_seller{other seller info}, similar_products{products info}, related_videos, introduction_box, expert_check, specifications_box, reviews, question_box, also_bought_items{product info}, seller_offer.

Additionally, this project accounts for changes in the information of sellers, their products, or product details. When a field in the database exists and the newly extracted information differs, the existing information in the database is moved to historical tables, and the new extracted information replaces them.

• sellers_history: Previous fields + history_id.
• products_history: Previous fields + history_id.
• products_extraction_history: Previous fields + history_id.

• Adding integration with other browsers.
• Adding the feature to display output in different formats.
• Creating unit tests for different parts.
• Adding an API to use main components in other applications.
• Web user interface using Flask.

• Optimizing memory usage.
• Improving project performance on different systems.
• Enhancing the speed and efficiency of algorithms.

• Expanding setting options and features in the panel.
• Creating a graphical user interface for easier interaction.
• Adding navigation and guidance to the user manual.

• Examining and improving security regarding user inputs.
• Adding better error management and reporting.
• Testing and fixing existing bugs.