This Python code defines a Streamlit web app for scraping news articles from a specific website based on user-selected date parameters. The app includes input fields for choosing the year, month, and day of the news, along with an option to toggle between English and Hindi languages. Users can click a "Scrap" button to initiate the scraping process.
Upon clicking the "Scrap" button, the code sends a request to the specified website, extracts news articles using BeautifulSoup, and displays them in a Streamlit interface. Each news article is presented in a stylized box with a clickable link to the original article. The app provides a visual representation of the scraped news for the selected date.
Note: The website URL used for scraping is specific to News Website, and the structure of the HTML elements is assumed to remain consistent for successful scraping.
pip install streamlit
pip install beautifulsoup4
pip install requests
Firstly import the streamlit, beautifulsoup4 and requests libraries through the terminal that will help in the program.
Download the zip file and extract the files. Then open your terminal or command prompt, navigate to the directory where the main.py python file is saved, and run the following command:
streamlit run main.py
- News Scraping: Extract news articles from a specific website based on user-selected date parameters.
- User Interaction: Provide a user-friendly interface using Streamlit for users to input the desired date and language preferences.
- Visualization: Display the scraped news articles in a visually appealing format with clickable links.
- Customized News Retrieval: Users can select a specific date to retrieve news articles, allowing for historical news exploration.
- Language Flexibility: The option to toggle between English and Hindi caters to a diverse user base.
- Interactive Interface: Streamlit's interactive elements enhance user experience, making the news retrieval process engaging.
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Website Dependency: The scraping process is sensitive to changes in the target website's HTML structure, requiring frequent updates to maintain functionality.
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Legal and Ethical Concerns: Web scraping may violate terms of service and raise ethical questions, necessitating permission from website owners and adherence to legal standards.
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Data Accuracy Challenges: The accuracy of scraped data is contingent on consistent formatting on the website; unexpected changes can result in inaccurate or incomplete information extraction.
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Limited Language Support: While English and Hindi are supported, the project may not accommodate all languages, potentially limiting user accessibility.
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Dynamic Content Handling: Implement a solution to handle websites with dynamic content loaded via JavaScript, ensuring comprehensive scraping for a wider range of websites.
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Error Handling and User Feedback: Enhance the user interface with error handling mechanisms to provide informative feedback in case of scraping failures or issues, improving user experience.
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Multi-Website Support: Extend the application to support scraping from multiple news websites with different structures, allowing users to choose their preferred news sources.
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Advanced Language Options: Incorporate language detection mechanisms to dynamically identify and support a broader range of languages, providing a more inclusive language selection for users.
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Caching Mechanism: Implement a caching mechanism to store previously scraped data, reducing the need for redundant requests and improving overall application performance.
- Streamlit: Used for building interactive web applications with minimal code.
- BeautifulSoup: Employed for web scraping, extracting structured data from HTML and XML files.
- Requests: Utilized for sending HTTP requests to the specified website to fetch web pages.
- HTML/CSS Styling: Applied to enhance the visual presentation of scraped news articles in the Streamlit app.
- Python: The primary programming language for building the entire application.
This project provides hands-on experience in web scraping, web development, and data visualization, making it a valuable learning opportunity for Python developers interested in these domains.
