Merge pull request avinashkranjan#2934 from jaivsh/second

Added CNN web scraper

Author: avinashkranjan

CNN Scraper/README.md

@@ -0,0 +1,6 @@
+## CNN scraper
+
+This scraper script scrapes the CNN content from it's website using the relevant functions. The details of various of it's functions are as follows: 
+
+- news_by_location(): Provides news by location/country/continent
+- news_by_category(): Proides news articles by category. 

CNN Scraper/cnn.py

@@ -0,0 +1,111 @@
+from bs4 import BeautifulSoup
+import requests
+
+
+class NewsCNN:
+    """
+    Create an instance of `NewsCNN` class.\n
+    ```python
+    news = NewsCNN()
+    ```
+    | Methods               | Details                                                                           |
+    | ---------------------------- | -------------------------------------------------------------------------- |
+    | `.news_by_location(country="india)` | Returns the list of articles by a specific country.               |
+    | `.news_by_category(type)`           | Returns the list of articles by a specific category.              |
+    """
+
+    def __init__(self):
+        self.headers = {
+            "User-Agent": "Mozilla/5.0 (Windows NT 6.3; Win 64 ; x64) Apple WeKit /537.36(KHTML , like Gecko) Chrome/80.0.3987.162 Safari/537.36"
+        }
+
+    def news_by_location(self, country: str):
+        """
+        Returns the relevant news articles corresponding to that particular geo-continent or country\n
+        Class - `NewsCNN`
+        Parameters: \n
+        - country: Name of the country\n
+        ```python
+        news = newsCNN()
+        news.news_by_location()
+        ```
+        """
+
+        try:
+            sol = []
+            obj_keys = ["news", "link"]
+            location = country.lower()
+            URL = f"https://edition.cnn.com/world/{location}"
+            page = requests.get(URL)
+            parse = BeautifulSoup(page.content, "html.parser")
+            heads = parse.find_all("span", attrs={"data-editable": "headline"})
+            links1 = parse.find_all(
+                "a",
+                attrs={
+                    "class": "container__link container_lead-plus-headlines-with-images__link"
+                },
+            )
+            links2 = parse.find_all(
+                "a", attrs={"class": "container__link container_vertical-strip__link"}
+            )
+            links3 = parse.find_all(
+                "a",
+                attrs={"class": "container__link container_lead-plus-headlines__link"},
+            )
+
+            base = "https://edition.cnn.com/"
+            allurls = []
+            allheads = []
+
+            for i in heads:
+                tmp = i.text
+                allheads.append(tmp)
+
+            for i in links1 + links2 + links3:
+                t = base + i["href"]
+                allurls.append(t)
+            allurls = list(set(allurls))
+
+            for i in range(len(allurls)):
+                obj_values = [allheads[i], allurls[i]]
+                new_obj = dict(zip(obj_keys, obj_values))
+                sol.append(new_obj)
+
+            return sol
+        except:
+            return None
+
+    def news_by_category(self, type: str):
+        """
+        Returns a list of news articles from a specific category.
+
+        Parameters:
+        - type (str): The category of news articles to retrieve. Allowable types are: "politics", "business", "opinions", "health", "style".
+
+        Returns:
+        A list of dictionaries, each containing news article information including title and link, or an exception if an error occurs.
+
+        Example:
+        ```python
+        news = NewsCNN()
+        politics_articles = news.news_by_category("politics")
+        ```
+        """
+        try:
+            sol = []
+            type = type.lower()
+            url = f"https://edition.cnn.com/{type}"
+            page = requests.get(url, headers=self.headers)
+            parse = BeautifulSoup(page.content, "html.parser")
+            articles = parse.find_all(
+                "a", {"class": "container__link container_lead-plus-headlines__link"}
+            )
+            for article in articles:
+                text = article.find("span", {"data-editable": "headline"})
+                if text:
+                    link = "https://edition.cnn.com" + article["href"]
+                    data = {"Title": text.text, "Link": link}
+                    sol.append(data)
+            return sol
+        except Exception as e:
+            return e

CNN Scraper/requirements.txt

@@ -0,0 +1,3 @@
+beautifulsoup4==4.9.1
+bs4==0.0.1
+requests==2.31.0