Update data/scrape_real_news.py

Renamed `scrape_articles` method to `scrape_real_articles`

data/scrape_real_news.py

@@ -1,67 +1,67 @@
-from newspaper import Article, build
-from pathlib import Path
-import pandas as pd
-import datetime
-
-# Output path
-BASE_DIR = Path(__file__).resolve().parent
-OUTPUT_PATH = BASE_DIR / "scraped_real.csv"
-
-# News sources
-NEWS_SITES = [
-    ("Reuters", "https://www.reuters.com/"),
-    ("BBC", "https://www.bbc.com/news"),
-    ("NPR", "https://www.npr.org/")
-]
-
-MAX_ARTICLES = 15
-
-def scrape_articles():
-    all_articles = []
-    total_scraped = 0
-
-    for source_name, url in NEWS_SITES:
-        print(f"📡 Scraping from {source_name}...")
-        paper = build(url, memoize_articles=False)
-
-        for article in paper.articles:
-            if total_scraped >= MAX_ARTICLES:
-                break
-
-            try:
-                article.download()
-                article.parse()
-
-                if len(article.text.strip()) < 100:
-                    continue  # Skip very short ones
-
-                text = article.title + ". " + article.text
-                all_articles.append({
-                    "text": text,
-                    "label": 0,
-                    "source": source_name,
-                    "timestamp": datetime.datetime.now().isoformat()
-                })
-
-                total_scraped += 1
-
-            except Exception:
-                continue  # Skip failed downloads
-
-        if total_scraped >= MAX_ARTICLES:
-            break  # Stop scraping once target reached
-
-    if all_articles:
-        df = pd.DataFrame(all_articles)
-
-        if OUTPUT_PATH.exists():
-            df_existing = pd.read_csv(OUTPUT_PATH)
-            df = pd.concat([df_existing, df], ignore_index=True)
-
-        df.to_csv(OUTPUT_PATH, index=False)
-        print(f"✅ Scraped and saved {len(all_articles)} new articles.")
-    else:
-        print("⚠️ No articles scraped.")
-
-if __name__ == "__main__":
-    scrape_articles()
+from newspaper import Article, build
+from pathlib import Path
+import pandas as pd
+import datetime
+
+# Output path
+BASE_DIR = Path(__file__).resolve().parent
+OUTPUT_PATH = BASE_DIR / "scraped_real.csv"
+
+# News sources
+NEWS_SITES = [
+    ("Reuters", "https://www.reuters.com/"),
+    ("BBC", "https://www.bbc.com/news"),
+    ("NPR", "https://www.npr.org/")
+]
+
+MAX_ARTICLES = 15
+
+def scrape_real_articles():
+    all_articles = []
+    total_scraped = 0
+
+    for source_name, url in NEWS_SITES:
+        print(f"📡 Scraping from {source_name}...")
+        paper = build(url, memoize_articles=False)
+
+        for article in paper.articles:
+            if total_scraped >= MAX_ARTICLES:
+                break
+
+            try:
+                article.download()
+                article.parse()
+
+                if len(article.text.strip()) < 100:
+                    continue  # Skip very short ones
+
+                text = article.title + ". " + article.text
+                all_articles.append({
+                    "text": text,
+                    "label": 0,
+                    "source": source_name,
+                    "timestamp": datetime.datetime.now().isoformat()
+                })
+
+                total_scraped += 1
+
+            except Exception:
+                continue  # Skip failed downloads
+
+        if total_scraped >= MAX_ARTICLES:
+            break  # Stop scraping once target reached
+
+    if all_articles:
+        df = pd.DataFrame(all_articles)
+
+        if OUTPUT_PATH.exists():
+            df_existing = pd.read_csv(OUTPUT_PATH)
+            df = pd.concat([df_existing, df], ignore_index=True)
+
+        df.to_csv(OUTPUT_PATH, index=False)
+        print(f"✅ Scraped and saved {len(all_articles)} new articles.")
+    else:
+        print("⚠️ No articles scraped.")
+
+if __name__ == "__main__":
+    scrape_articles()